CN106094839A - Robot anti-collision human system - Google Patents

Abstract

The invention provides robot anti-collision human system, perform module including sensing module, communication module, central control module and walking；Whether described sensing module has people in zone of action for the appointment orientation sensing robot, and by communication module, sensing result is sent to central control module, and described central control module controls described walking according to sensing result and performs module action.The invention have the benefit that by sensing module sensing robot appointment orientation whether have people in zone of action, then by central control module control walking perform module action, it is achieved that the control to robot ambulation, it is to avoid robot thrustes into.

Description

Robot anti-collision human system

Technical field

The present invention relates to Robot Design field, be specifically related to robot anti-collision human system.

Background technology

Robot in correlation technique can only complete simply to work on production line in the locality of factory.Robot is not Can be exactly, if machine person to person is too close, to be just likely to bump against people, one with people's intimate contact important reason Individual hundreds of jin that weighs, the most several very heavy ferrum fellows bump against the flesh and blood of people, and consequence is well imagined.So, in order to anti- Only people may be knocked by robot, and current robot can only work on specific place, specific track.

Summary of the invention

For solving the problems referred to above, it is desirable to provide robot anti-collision human system.

The purpose of the present invention realizes by the following technical solutions:

Robot anti-collision human system, performs module including sensing module, communication module, central control module and walking；Institute State whether sensing module has people in zone of action for the appointment orientation sensing robot, and by communication module, sensing is tied Fruit is sent to central control module, and described central control module controls described walking according to sensing result and performs module action.

The invention have the benefit that by sensing module sensing robot appointment orientation whether have people in zone of action In, then control walking execution module action by central control module, it is achieved that the control to robot ambulation, it is to avoid robot Thrust into, thus solve above-mentioned technical problem.

Accompanying drawing explanation

The invention will be further described to utilize accompanying drawing, but the embodiment in accompanying drawing does not constitute any limit to the present invention System, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to obtain according to the following drawings Other accompanying drawing.

Fig. 1 is present configuration schematic diagram；

Fig. 2 is the schematic diagram of inventive sensor fault diagnosis module.

Reference:

Sensing module 1, communication module 2, central control module 3, walking perform module 4, sensor fault diagnosis module 5, Signals collecting filter unit 51, fault signature extraction unit 52, online feature extraction unit 53, characteristic vector preferred cell 54, Failure modes recognition unit 55, failure mode updating block 56, health records unit 57.

Detailed description of the invention

The invention will be further described with the following Examples.

Application scenarios 1

Seeing Fig. 1, Fig. 2, the robot anti-collision human system of an embodiment of this application scene includes sensing module 1, leads to Letter module 2, central control module 3 and walking perform module 4；Described sensing module 1 for sensing the appointment orientation of robot is No have people in zone of action, and by communication module 2, sensing result is sent to central control module 3, and described central authorities control Module 3 controls described walking according to sensing result and performs module 4 action.

Preferably, described appointment orientation includes front, left side, right side.

Whether the appointment orientation that the above embodiment of the present invention senses robot by sensing module 1 has people in zone of action In, then control walking execution module 4 action by central control module 3, it is achieved that the control to robot ambulation, it is to avoid machine People thrustes into, thus solves above-mentioned technical problem.

Preferably, described sensing module 1 includes left pyroelectric sensor, right pyroelectric sensor, and front pyroelectricity senses Device.

Whether the left pyroelectric sensor sensing robot left side of this preferred embodiment has people in zone of action, and right heat is released Whether the electric transducer sensing robot right side has people in zone of action, and whether front pyroelectric sensor senses has before robot People is in zone of action；The infrared signal that pyroelectric sensor is launched by sensing human body, judges that sensor is covered Cover region territory whether presence of people；After determining current kinetic scope nobody, the machine talent goes to this region.

Preferably, described robot anti-collision human system also includes the sensor fault diagnosis mould diagnosing each sensor Block 5, described sensor fault diagnosis module 5 includes signals collecting filter unit 51, fault signature extraction unit 52, online feature Extraction unit 53, characteristic vector preferred cell 54, failure modes recognition unit 55, failure mode updating block 56 and health records Unit 57.

The above embodiment of the present invention arranges sensor fault diagnosis module 5 and achieves sensor fault diagnosis module 5 Fast construction, is conducive to monitoring each sensor, it is ensured that monitoring effectively performs.

Preferably, described collection filtration module 1 is used for gathering historical sensor signal and on-line sensor test signal, and Combination form wave filter is used to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction unit 52 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online feature extraction unit 53 is for carrying out integrated Empirical Mode to filtered on-line sensor test signal State is decomposed (EEMD) and is processed, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred cell 54 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes recognition unit 55 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode updating block 56, for being updated training set, continues to optimize sensor fault Diagnostic cast, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode updating block 56, to improve adaptation ability and the range of application of model.

Preferably, described health records unit 57 includes sub module stored and secure access submodule, described storage submodule Block uses storage model based on cloud storage, specifically, is encrypted after being compressed by fault message, is uploaded to cloud storage Device, described secure access submodule, for conducting interviews information, specifically, corresponding to sub module stored, downloads data to This locality, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records unit 57, on the one hand ensure that information security, on the other hand can be at any time Fault is conducted interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.96, the monitoring velocity phase of sensor fault diagnosis module 5 To improve 10%, the monitoring accuracy of sensor fault diagnosis module 5 improves 12% relatively.

Application scenarios 2

Seeing Fig. 1, Fig. 2, the robot anti-collision human system of an embodiment of this application scene includes sensing module 1, leads to Letter module 2, central control module 3 and walking perform module 4；Described sensing module 1 for sensing the appointment orientation of robot is No have people in zone of action, and by communication module 2, sensing result is sent to central control module 3, and described central authorities control Module 3 controls described walking according to sensing result and performs module 4 action.

Preferably, described appointment orientation includes front, left side, right side.

Whether the appointment orientation that the above embodiment of the present invention senses robot by sensing module 1 has people in zone of action In, then control walking execution module 4 action by central control module 3, it is achieved that the control to robot ambulation, it is to avoid machine People thrustes into, thus solves above-mentioned technical problem.

Preferably, described sensing module 1 includes left pyroelectric sensor, right pyroelectric sensor, and front pyroelectricity senses Device.

Whether the left pyroelectric sensor sensing robot left side of this preferred embodiment has people in zone of action, and right heat is released Whether the electric transducer sensing robot right side has people in zone of action, and whether front pyroelectric sensor senses has before robot People is in zone of action；The infrared signal that pyroelectric sensor is launched by sensing human body, judges that sensor is covered Cover region territory whether presence of people；After determining current kinetic scope nobody, the machine talent goes to this region.

Preferably, described robot anti-collision human system also includes the sensor fault diagnosis mould diagnosing each sensor Block 5, described sensor fault diagnosis module 5, signals collecting filter unit 51, fault signature extraction unit 52, online feature carry Take unit 53, characteristic vector preferred cell 54, failure modes recognition unit 55, failure mode updating block 56 and health records list Unit 57.

The above embodiment of the present invention arranges sensor fault diagnosis module 5 and achieves sensor fault diagnosis module 5 Fast construction, is conducive to monitoring each sensor, it is ensured that monitoring effectively performs.

Preferably, described collection filtration module 1 is used for gathering historical sensor signal and on-line sensor test signal, and Combination form wave filter is used to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction unit 52 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online feature extraction unit 53 is for carrying out integrated Empirical Mode to filtered on-line sensor test signal State is decomposed (EEMD) and is processed, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred cell 54 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes recognition unit 55 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode updating block 56, for being updated training set, continues to optimize sensor fault Diagnostic cast, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode updating block 56, to improve adaptation ability and the range of application of model.

Preferably, described health records unit 57 includes sub module stored and secure access submodule, described storage submodule Block uses storage model based on cloud storage, specifically, is encrypted after being compressed by fault message, is uploaded to cloud storage Device, described secure access submodule, for conducting interviews information, specifically, corresponding to sub module stored, downloads data to This locality, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records unit 57, on the one hand ensure that information security, on the other hand can be at any time Fault is conducted interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.95, the monitoring velocity phase of sensor fault diagnosis module 5 To improve 11%, the monitoring accuracy of sensor fault diagnosis module 5 improves 11% relatively.

Application scenarios 3

Seeing Fig. 1, Fig. 2, the robot anti-collision human system of an embodiment of this application scene includes sensing module 1, leads to Letter module 2, central control module 3 and walking perform module 4；Described sensing module 1 for sensing the appointment orientation of robot is No have people in zone of action, and by communication module 2, sensing result is sent to central control module 3, and described central authorities control Module 3 controls described walking according to sensing result and performs module 4 action.

Preferably, described appointment orientation includes front, left side, right side.

Whether the appointment orientation that the above embodiment of the present invention senses robot by sensing module 1 has people in zone of action In, then control walking execution module 4 action by central control module 3, it is achieved that the control to robot ambulation, it is to avoid machine People thrustes into, thus solves above-mentioned technical problem.

Preferably, described sensing module 1 includes left pyroelectric sensor, right pyroelectric sensor, and front pyroelectricity senses Device.

Whether the left pyroelectric sensor sensing robot left side of this preferred embodiment has people in zone of action, and right heat is released Whether the electric transducer sensing robot right side has people in zone of action, and whether front pyroelectric sensor senses has before robot People is in zone of action；The infrared signal that pyroelectric sensor is launched by sensing human body, judges that sensor is covered Cover region territory whether presence of people；After determining current kinetic scope nobody, the machine talent goes to this region.

Preferably, described robot anti-collision human system also includes the sensor fault diagnosis mould diagnosing each sensor Block 5, described sensor fault diagnosis module 5, signals collecting filter unit 51, fault signature extraction unit 52, online feature carry Take unit 53, characteristic vector preferred cell 54, failure modes recognition unit 55, failure mode updating block 56 and health records list Unit 57.

The above embodiment of the present invention arranges sensor fault diagnosis module 5 and achieves sensor fault diagnosis module 5 Fast construction, is conducive to monitoring each sensor, it is ensured that monitoring effectively performs.

Preferably, described collection filtration module 1 is used for gathering historical sensor signal and on-line sensor test signal, and Combination form wave filter is used to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction unit 52 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online feature extraction unit 53 is for carrying out integrated Empirical Mode to filtered on-line sensor test signal State is decomposed (EEMD) and is processed, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred cell 54 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes recognition unit 55 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode updating block 56, for being updated training set, continues to optimize sensor fault Diagnostic cast, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode updating block 56, to improve adaptation ability and the range of application of model.

Preferably, described health records unit 57 includes sub module stored and secure access submodule, described storage submodule Block uses storage model based on cloud storage, specifically, is encrypted after being compressed by fault message, is uploaded to cloud storage Device, described secure access submodule, for conducting interviews information, specifically, corresponding to sub module stored, downloads data to This locality, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records unit 57, on the one hand ensure that information security, on the other hand can be at any time Fault is conducted interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.94, the monitoring velocity phase of sensor fault diagnosis module 5 To improve 12%, the monitoring accuracy of sensor fault diagnosis module 5 improves 10% relatively.

Application scenarios 4

Seeing Fig. 1, Fig. 2, the robot anti-collision human system of an embodiment of this application scene includes sensing module 1, leads to Letter module 2, central control module 3 and walking perform module 4；Described sensing module 1 for sensing the appointment orientation of robot is No have people in zone of action, and by communication module 2, sensing result is sent to central control module 3, and described central authorities control Module 3 controls described walking according to sensing result and performs module 4 action.

Preferably, described appointment orientation includes front, left side, right side.

Whether the appointment orientation that the above embodiment of the present invention senses robot by sensing module 1 has people in zone of action In, then control walking execution module 4 action by central control module 3, it is achieved that the control to robot ambulation, it is to avoid machine People thrustes into, thus solves above-mentioned technical problem.

Preferably, described sensing module 1 includes left pyroelectric sensor, right pyroelectric sensor, and front pyroelectricity senses Device.

Whether the left pyroelectric sensor sensing robot left side of this preferred embodiment has people in zone of action, and right heat is released Whether the electric transducer sensing robot right side has people in zone of action, and whether front pyroelectric sensor senses has before robot People is in zone of action；The infrared signal that pyroelectric sensor is launched by sensing human body, judges that sensor is covered Cover region territory whether presence of people；After determining current kinetic scope nobody, the machine talent goes to this region.

Preferably, described robot anti-collision human system also includes the sensor fault diagnosis mould diagnosing each sensor Block 5, described sensor fault diagnosis module 5, signals collecting filter unit 51, fault signature extraction unit 52, online feature carry Take unit 53, characteristic vector preferred cell 54, failure modes recognition unit 55, failure mode updating block 56 and health records list Unit 57.

The above embodiment of the present invention arranges sensor fault diagnosis module 5 and achieves sensor fault diagnosis module 5 Fast construction, is conducive to monitoring each sensor, it is ensured that monitoring effectively performs.

Preferably, described collection filtration module 1 is used for gathering historical sensor signal and on-line sensor test signal, and Combination form wave filter is used to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction unit 52 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online feature extraction unit 53 is for carrying out integrated Empirical Mode to filtered on-line sensor test signal State is decomposed (EEMD) and is processed, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred cell 54 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes recognition unit 55 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode updating block 56, for being updated training set, continues to optimize sensor fault Diagnostic cast, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode updating block 56, to improve adaptation ability and the range of application of model.

Preferably, described health records unit 57 includes sub module stored and secure access submodule, described storage submodule Block uses storage model based on cloud storage, specifically, is encrypted after being compressed by fault message, is uploaded to cloud storage Device, described secure access submodule, for conducting interviews information, specifically, corresponding to sub module stored, downloads data to This locality, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records unit 57, on the one hand ensure that information security, on the other hand can be at any time Fault is conducted interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.93, the monitoring velocity phase of sensor fault diagnosis module 5 To improve 13%, the monitoring accuracy of sensor fault diagnosis module 5 improves 9% relatively.

Application scenarios 5

Seeing Fig. 1, Fig. 2, the robot anti-collision human system of an embodiment of this application scene includes sensing module 1, leads to Letter module 2, central control module 3 and walking perform module 4；Described sensing module 1 for sensing the appointment orientation of robot is No have people in zone of action, and by communication module 2, sensing result is sent to central control module 3, and described central authorities control Module 3 controls described walking according to sensing result and performs module 4 action.

Preferably, described appointment orientation includes front, left side, right side.

Whether the appointment orientation that the above embodiment of the present invention senses robot by sensing module 1 has people in zone of action In, then control walking execution module 4 action by central control module 3, it is achieved that the control to robot ambulation, it is to avoid machine People thrustes into, thus solves above-mentioned technical problem.

Preferably, described sensing module 1 includes left pyroelectric sensor, right pyroelectric sensor, and front pyroelectricity senses Device.

Whether the left pyroelectric sensor sensing robot left side of this preferred embodiment has people in zone of action, and right heat is released Whether the electric transducer sensing robot right side has people in zone of action, and whether front pyroelectric sensor senses has before robot People is in zone of action；The infrared signal that pyroelectric sensor is launched by sensing human body, judges that sensor is covered Cover region territory whether presence of people；After determining current kinetic scope nobody, the machine talent goes to this region.

Preferably, described robot anti-collision human system also includes the sensor fault diagnosis mould diagnosing each sensor Block 5, described sensor fault diagnosis module 5, signals collecting filter unit 51, fault signature extraction unit 52, online feature carry Take unit 53, characteristic vector preferred cell 54, failure modes recognition unit 55, failure mode updating block 56 and health records list Unit 57.

The above embodiment of the present invention arranges sensor fault diagnosis module 5 and achieves sensor fault diagnosis module 5 Fast construction, is conducive to monitoring each sensor, it is ensured that monitoring effectively performs.

Preferably, described collection filtration module 1 is used for gathering historical sensor signal and on-line sensor test signal, and Combination form wave filter is used to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction unit 52 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online feature extraction unit 53 is for carrying out integrated Empirical Mode to filtered on-line sensor test signal State is decomposed (EEMD) and is processed, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred cell 54 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes recognition unit 55 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode updating block 56, for being updated training set, continues to optimize sensor fault Diagnostic cast, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode updating block 56, to improve adaptation ability and the range of application of model.

Preferably, described health records unit 57 includes sub module stored and secure access submodule, described storage submodule Block uses storage model based on cloud storage, specifically, is encrypted after being compressed by fault message, is uploaded to cloud storage Device, described secure access submodule, for conducting interviews information, specifically, corresponding to sub module stored, downloads data to This locality, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records unit 57, on the one hand ensure that information security, on the other hand can be at any time Fault is conducted interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.92, the monitoring velocity phase of sensor fault diagnosis module 5 To improve 14%, the monitoring accuracy of sensor fault diagnosis module 5 improves 8% relatively.

Last it should be noted that, above example is only in order to illustrate technical scheme, rather than the present invention is protected Protecting the restriction of scope, although having made to explain to the present invention with reference to preferred embodiment, those of ordinary skill in the art should Work as understanding, technical scheme can be modified or equivalent, without deviating from the reality of technical solution of the present invention Matter and scope.

Claims (3)

1. robot anti-collision human system, is characterized in that, performs including sensing module, communication module, central control module and walking Module；Whether described sensing module has people in zone of action for the appointment orientation sensing robot, and passes through communication module Sensing result is sent to central control module, and described central control module controls described walking according to sensing result and performs module Action.

Robot the most according to claim 1 anti-collision human system, is characterized in that, described appointment orientation include front, left side, Right side.

Robot the most according to claim 2 anti-collision human system, is characterized in that, described sensing module includes left pyroelectricity Sensor, right pyroelectric sensor, front pyroelectric sensor.