CN110110785A - A kind of express mail logistics progress state-detection classification method - Google Patents
A kind of express mail logistics progress state-detection classification method Download PDFInfo
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Abstract
The present invention provides a kind of express mail logistics progress state-detection classification method, this method carries out the feature that dimension-reduction treatment determines classification to the vibration data of express mail first；Then by carrying out two classification to data, physical state is determined；Normal Refresh Data is covered again, abnormal data are stored to memory module, more classification finally are executed to abnormal data and degree differentiates, obtain the classification and degree of abnormal conditions.The available accurate detailed Exception Type of method of the invention, carries out real time intelligent control to express mail logistics progress vibrational state to realize.The data such as temperature and humidity, intensity of illumination are included in detection range simultaneously, keep entire detection architecture more scientific perfect.
Description
Technical field
The present invention relates to monitoring logistics transportation field more particularly to a kind of express mail state-detection classification methods.
Background technique
It is even fairly simple for the algorithm of express mail logistics progress condition discrimination at present, it is main still with simple threshold value side
Method is judged, for example the instantaneous acceleration in some direction is more than some threshold value, then it is assumed that express mail occurs in logistics progress
Abnormal conditions, such classifying quality is poor, can not identify certain fortuitous events, and excessively relies on the reliability of sensor,
Algorithm itself corrects ability without data.In addition, under normal conditions due to the data source of these algorithms, i.e. sensor collects
Initial data it is less, thus make related algorithm obtain express mail physical state information very not comprehensively.For this purpose, the present invention is being based on
On the basis of multi sensor combination acquires all kinds of express mail logistics progress status informations, a set of more perfect express mail logistics is proposed
Journey state-detection sorting algorithm carries out preferentially dimensionality reduction by genetic algorithm and pre-processes, using support vector machines to express mail logistics
Journey state feature carries out two classifying screen early period and selects express mail logistics progress vibrational state abnormal data, then by data from collection terminal
Wireless transmissions carry out the processing of more classification data based on neuroid to central processing unit, obtain accurate detailed exception class
Type carries out real time intelligent control to express mail logistics progress vibrational state to realize.Simultaneously by data such as temperature and humidity, intensities of illumination
It is included in detection range, keeps entire detection architecture more scientific perfect.
Summary of the invention
To solve the above-mentioned problems, the present invention proposes a kind of express mail logistics progress state-detection classification method, present invention tool
Body adopts the following technical scheme that this method comprises the following steps: (1) carrying out dimension-reduction treatment to the vibrational state data of express mail and determine
The feature of classification；(2) two classification are carried out according to vibrational state data of the determining feature to express mail, be divided into normal
The normal data is refreshed and is covered, the abnormal data is stored by data and abnormal data；(3) to abnormal data into
The more classification of row and degree differentiate, obtain the classification and degree of abnormal conditions.
Preferably, in step (1) dimension-reduction treatment process are as follows: input data, data prediction, chromosome coding, generation
Initial population calculates fitness, setting termination condition, selection, intersection, carries out 100 generation genetic iterations, and constantly repeating, and takes most
Convergent optimal solution eventually, determines whether to retain the dimensional characteristics according to the digit of chromosome coding in optimal solution；
The classification accuracy rate when fitness is by the feature retained after dimension-reduction treatment as classification foundation, classification is just
True rate calculation formula are as follows:
γ is that function output is that sorted label successively constitutes the one-dimension array being arranged to make up, γ*Array γ is each
Position subtracts 1 obtained array, and λ is that test set data point label is arranged successively one n dimension group of composition.
Preferably, in step (2) the vibrational state data of express mail are carried out with the process of two classification are as follows:
Dimension-reduction treatment is carried out by training dataset and obtains a hyperplane, and the hyperplane is apart from normal condition point set
With the plane of abnormal conditions point set minimum range, by one group of tape label and the test set data point after dimension-reduction treatment is utilized and is somebody's turn to do
Hypersurface is classified, and differentiates that side of point in the two sides hyperplane 0-1.
Preferably, the process of more classification and degree differentiation is carried out in step (3) to abnormal data are as follows: use multilayer perceptron
Network structure, first stage forward direction process input information successively calculate the output valve of each unit, second-order from input layer through hidden layer
Section back-propagation process is output error successively to be calculated forward to the error of hidden layer each unit, and weighed with this error correction front layer
Value；
Each node layer output are as follows:
Y3=1,2,3... (class labels)
Wherein, W indicates that weight vectors, θ indicate biasing；
Output function are as follows:
Error function:
Wherein tpiAnd OpiThe respectively desired output of network and practical calculate exports；
The training dataset of input is the sample matrix of tape label, and row matrix indicates different training samples, list sample sheet
Complete characteristic, training dataset is trained test using ten folding interior extrapolation methods, and entire data set is consistent according to quantity,
Be randomly divided into 10 parts, successively take wherein 7 parts be used as training set, 3 are allocated as being trained for validation test collection, train one it is non-thread
Property system, using abnormal data as input, using classification results as export；
After having executed multi-classification algorithm, the sample point of no label is put into set computational discrimination value, discriminant value is fallen in not
With representing different severity, the degree coefficient of computational discrimination value in threshold value
Wherein, K indicates degree coefficient, amaxIt indicates 6 dimensional vectors, is made of 6 dimension acceleration peak values of sample, β serves as reasons
Each element gets the new vector of number composition, W respectively in 6 dimensional vectors that 6 dimension acceleration peak values of most serious abnormal conditions are constitutedT
For a weight coefficient matrix,Symbol indicates that two are waited the element of dimensional vectors corresponding position to be multiplied.
Detailed description of the invention
Fig. 1 is Preprocessing Algorithm flow chart.
Fig. 2 is the calculation flow chart of fitness.
Fig. 3 is chiasma result figure.
Fig. 4 is genetic algorithm flow chart.
Fig. 5 is variation principle flow chart.
Fig. 6 is multi-Layer Perceptron Neural Network structure chart.
Fig. 7 is the intelligent classification algorithm flow chart based on neuroid.
Specific embodiment
Method for detecting vibration step:
(1) dimensionality reduction is pre-processed
Input data: 500 three axis linear accelerations of continuous sampling point, 500 × 6 squares of three shaft angle acceleration informations composition
Battle array.
Data prediction: the 6 of 500 6 dimension datas of sampled point (three axis linear accelerations, three shaft angle acceleration) are calculated separately
Kind statistics feature.
6 kinds of features see the table below 1.
1 statistics feature of table
When measuring express mail Vibration Condition, one 6 × 6 30 sextuple space models, the vibrational state of express mail have been obtained
A corresponding point can be found in space.
Preprocessing Algorithm: algorithm flow chart is as shown in Figure 1.
Chromosome coding: mode is binary coding, and the chromosome after coding is one 1 × 36 array, and array is by 0 He
1 is constituted, and each chromosome has and only 51, indicates that 36 dimensional features will be compressed to 5 dimensions.0 indicates that it corresponds to the system of columns
Counting feature will indicate to retain the statistical nature by dimension-reduction treatment, 1.
Generate initial population: initial population number of individuals is 100, and each individual is a chromosome, initial population generation side
Formula is random generation, 5 numbers in random 1-36, indicates that this coding 1, remaining digit code are 0.
Calculate fitness: classification when fitness is by 5 dimensional feature retained after dimension-reduction treatment as classification foundation is just
True rate.I.e. classification accuracy rate is higher after dimensionality reduction, and fitness is higher.
When calculating a chromosome fitness, using the binary coding of chromosome as vector α.
It is arranged successively by pre-processing 36 obtained dimension datas as vector β.
Carrying out following calculate can be obtained the vector after dimensionality reduction:
In definitionSymbol indicates that two are waited the element of dimensional vectors corresponding position to be multiplied, and obtain a same dimensional vector.
It before calculating classification accuracy rate, needs to be trained using the training set of one group of tape label, training set is by drop
Dimension processing, to obtain a hyperplane, which has a following feature, i.e. two class point set of distance (normal condition point and different
Reason condition point) in minimum range point distance it is maximum.
Realization process are as follows:
Training set { the x that given one group of size is Ni,yi}N, input as xi, export as yi, for nonlinear regression, data by
Nonlinear equation y (x)=f (xi)+eiProvide, by obtain it is following in the form of estimation model:
Wherein w is weight vector,It is the nonlinear function that the input space is mapped to high-order feature space, b indicates inclined
Residual quantity, eiIt is then the departure of the reality output of i-th group of training data and estimation output, referred to as error of fitting.
So w, b can be described as optimization problem:
Lagrange's multiplier is used to above formula:
Wherein αiFor Lagrange's multiplier, γ is penalty coefficient, respectively to w, b, ei,αiDifferential construction:
Eliminate w and ei, optimal problem is converted into the form of system of linear equations:
Wherein, y=[y1；...；yN], α=[α1；...；αN], 1v=[1；...；1], Ω is square matrix, and m row n-th arranges member
Element is Ωmn=K (xm,xn), m, n=1 ..., N.
In view of the actual conditions of linearly inseparable, Gaussian function, the General Expression of Gaussian function is can be used in kernel function
Formula is as follows.
xiFor independent variable, xjFor Gaussian kernel center, σ is width control parameter, controls radial extension.
It finally obtainsIn solution a and b.
After obtaining hyperplane, by one group of tape label and the test test set data point after dimension-reduction treatment utilizes this super bent
Face is classified, and differentiates point in hyperplane two sides.The coordinate value of point to be sorted is actually substituted into hyperplane equation, is differentiated
A positive and negative process.
Function output is that sorted label successively constitutes the one-dimension array being arranged to make up, and is denoted as γ.
By array γ, each subtracts 1, obtains array γ*。
Test test set data point label, which is arranged successively, constitutes a n dimension group λ.
Accuracy calculation formula are as follows:
The calculation process of fitness is as shown in Figure 2.
The setting of termination condition: termination condition of the invention is set as fixed filial generation algebra, carries out cross and variation selection
The 100th generation filial generation generated is the optimal solution of Preprocessing Algorithm.
Selection principle: construction wheel disc is based on principle of probability, parent is after cross and variation, the higher new dyeing of fitness
Body is more by selection quantity present in filial generation.Fitness it is low will the high substitution of degree of being accommodated.
Cross-over principle: by number 1,3 in population ..., 99 chromosome number consecutively 2,4 ..., 100 chromosome two
Two 1 groups of pairings.The random number that each pair of chromosome generates a 0-1 is corresponding to it, the random number and preset crossover probability
pcIt is compared, if being less than pcExecute crossover operation.
pc0.5-0.8 is taken to be advisable.
The implementation method of crossover operation is that each pair of chromosome is divided into two sections, is intersected by a random separation.
After intersecting, newly-generated child chromosome is likely to occur the case where quantity of coding 1 in chromosome is not 5.
As shown in Figure 3.
The algorithm flow of the part is as shown in Figure 4.
Make a variation principle: some algorithm process such as Fig. 5.
Wherein, pmFor mutation probability, it is a preset value, takes 0.003-0.01.N indicates chromosome in population
Number.
In conclusion carrying out 100 generation genetic iterations, and constantly repeat.Scattered property is held back in observation, takes final convergent optimal solution,
The digit of chromosome coding 1 indicates to retain the dimensional characteristics in optimal solution, weeds out the dimension for 0 digit expression.
(2) two sorting algorithms after dimensionality reduction
Using the 5 dimension acceleration information statistics features retained after pre-processing preferentially as practical heat transfer agent collection terminal
The classification foundation for the sorting algorithm being written in microprocessor.
Applied classification method to construct a quintuple space, when using calculating fitness.Pass through drop
Tape label training set training* after dimension (is different from training, to obtain more accurate smooth hypersurface, new training set
Data volume it is more, and constantly update), solved using least square method, obtain one apart from training set in 2 class points most
A farthest hyperplane Ω of the distance of close point is as classification foundation.The reality to express mail logistics progress Vibration Condition is realized with this
When detect.
(3) multi-classification algorithm
6 kinds of statistics of the 6 dimension acceleration of abnormal period (continuous 500 sampled points) are determined as by two sorting algorithms
Learn feature and 6 kinds of acceleration peak values of this time, it will central processing unit is passed to by communication module.These data will
It is performed further discriminant classification and degree differentiates.
The more classification methods of express mail physical state proposed by the present invention are a kind of intelligent classification algorithms based on neuroid.
Input data is to be determined as the 6 of 500 abnormal continuous sampling points by two sorting algorithms to tie up statistics feature in the 6 of acceleration
Value, i.e. 6 dimensional vectors.Output is the class label shaped like 0 (or 1,2,3,4...).The present invention, which uses, passes through a large amount of tape labels
Sample training neuroid structure nonlinear system.
1) multi-Layer Perceptron Neural Network structure (as shown in Figure 6)
Each node layer output are as follows:
Y3=1,2,3... (class labels)
W indicates that weight vectors, θ indicate biasing in formula.
Algorithm is divided into two first stage in stage forward direction process input informations and successively calculates each unit through hidden layer from input layer
Output valve second stage back-propagation process be that output error is successively calculated forward to the error of hidden layer each unit, and missed with this
Difference amendment front layer weight.Weight is corrected usually using gradient method, therefore it is required that output can be led.
Output function are as follows:
Error function:
Wherein tpiAnd OpiThe respectively desired output of network and practical calculate exports.
netj=∑ wijOj Oj=f (netj)
Obtain sample error
Using steepest gradient method, partial gradient is defined
If it is considered that the influence of weight
To make error during modified weight reduce fastest, correction amount are as follows:
Δwij=-η δjOi wij(t+1)=wij(t)+Δwij(t)
If node j is output unit, have:
Oj=yj
If node j is not output unit,
It is rightFunction uses iterative method:
F (x)=y (1-y)
To accelerate convergence, the moment of inertia is added
Δwij(t)=- η δjOj+Δwij(t-1)
Its algorithm flow is as shown in Figure 7.
2) multi-classification algorithm is realized
The training dataset of input is the sample matrix of tape label, and row matrix indicates different training samples, list sample sheet
Complete characteristic, therefore the training set with n sample is the matrix of n × 36.Desired output be n × 1 to
Amount is the artificial label (being indicated with 0,1,2,3,4...) of each sample.Data set be trained-is surveyed using ten folding interior extrapolation methods
It tries and is, i.e., it is entire data set is consistent according to quantity, 10 parts are randomly divided into, successively takes and is wherein used as training set for 7 parts, 3 are allocated as
It is trained for validation test collection.A nonlinear system is trained with this, it can be more accurately using abnormal data as defeated
Enter, using classification results as output.
Can be used as classification function by the obtained linear system of above method training, to the following needs classify without label
Data carry out Accurate classification.
3) intensity of anomaly distinguished number
After having executed multi-classification algorithm, the sample point of no label has been placed into some category set Γn(n=0,1,2,
3...).The case where most serious that the point is corresponding with the set of predefined, corresponding characteristic compared, such as
Most serious vertically falls situation and may be defined as falling from storied 3-D shelf top layer in logistics progress.Calculate 500 companies of sample
The peak value that continuous sampled point 6 ties up acceleration ties up the peak value of acceleration divided by 500 continuous sampling points 6 of most serious abnormal conditions respectively,
6 data are obtained, are added respectively multiplied by weight, final discriminant value is obtained.Discriminant value fall in different threshold values represent it is different
Severity.
Formula are as follows:
In definitionSymbol indicates that two are waited the element of dimensional vectors corresponding position to be multiplied, and obtain a same dimensional vector.
Wherein K indicates degree coefficient, amaxIt indicates 6 dimensional vectors, is made of 6 dimension acceleration peak values of sample, β serves as reasons
Each element gets the new vector of number composition, W respectively in 6 dimensional vectors that 6 dimension acceleration peak values of most serious abnormal conditions are constitutedT
For a weight coefficient matrix, obtained by experience.
4) verification algorithm is assisted
Also with neuroid, using three axis geomagnetic datas as feature, by 6 kinds of three axis geomagnetic datas statistics of sample
Feature is learned as sample characteristics, constructs the input matrix of n × 18 of n sample, the manual tag of n × 1 is as desired output.Training
Nonlinear system carries out compliance evaluation by nonlinear system, verifies the validity of classifier.When invalid feelings are verified in appearance
Condition notifies administrator, and draws each Acceleration pulse figure, for administrator's inquiry judging.
5) differentiation about temperature and humidity, intensity of illumination exception
In view of practical application scene, it is only necessary to know whether practical logistics transportation environment exceeds special express mail for temperature and humidity
Claimed range see whether actual samples data are below or above so the actual algorithm of this part is exactly given threshold
The range, and result is provided, since the differentiation of the part is relatively simple, therefore do not repeat them here.
Differentiation for intensity of illumination exception removes given threshold, differentiates whether real-time lighting intensity exceeds outside requirement, may be used also
Whether it is greater than some value according to neighbouring sample point intensity of illumination derivative, to discriminate whether the express mail packages in damaged condition feelings for occurring happening suddenly
Condition, the part is also relatively simple, therefore does not repeat them here.
Claims (4)
1. a kind of express mail logistics progress state-detection classification method, which is characterized in that this method comprises the following steps:
(1) feature that dimension-reduction treatment determines classification is carried out to the vibrational state data of express mail；
(2) two classification are carried out to the vibrational state data of express mail according to the determining feature, be divided into normal data and
The normal data is refreshed and is covered, the abnormal data is stored by abnormal data；
(3) more classification are carried out to abnormal data and degree differentiates, obtain the classification and degree of abnormal conditions.
2. a kind of express mail logistics progress state-detection classification method as described in claim 1, which is characterized in that in step (1)
The process of dimension-reduction treatment are as follows: input data, chromosome coding, generates initial population, calculates fitness, setting data prediction
Termination condition selection, intersection, carries out 100 generation genetic iterations, and constantly repeats, and final convergent optimal solution is taken, according to optimal solution
The digit of middle chromosome coding determines whether to retain the dimensional characteristics；
The classification accuracy rate when fitness is by the feature retained after dimension-reduction treatment as classification foundation, classification accuracy rate
Calculation formula are as follows:
γ is that function output is that sorted label successively constitutes the one-dimension array being arranged to make up, γ*By array γ, each is subtracted
1 obtained array, λ are that test set data point label is arranged successively one n dimension group of composition.
3. a kind of express mail logistics progress state-detection classification method as described in claim 1, which is characterized in that in step (2)
The vibrational state data of express mail are carried out with the process of two classification are as follows:
Dimension-reduction treatment is carried out by training dataset and obtains a hyperplane, and the hyperplane is apart from normal condition point set and different
The plane of reason condition point set minimum range, by one group of tape label and the test set data point after dimension-reduction treatment utilizes this super bent
Face is classified, and differentiates point in hyperplane two sides.
4. a kind of express mail logistics progress state-detection classification method as described in claim 1, which is characterized in that in step (3)
The process of more classification and degree differentiation is carried out to abnormal data are as follows: use multi-Layer Perceptron Neural Network structure, first stage forward direction mistake
Journey input information successively calculates the output valve of each unit from input layer through hidden layer, and second stage back-propagation process is will to export mistake
The poor error for successively calculating hidden layer each unit forward, and with this error correction front layer weight；
Each node layer output are as follows:
Y3=1,2,3... (class labels)
Wherein, W indicates that weight vectors, θ indicate biasing；
Output function are as follows:
Error function:
Wherein tpiAnd OpiThe respectively desired output of network and practical calculate exports；
The training dataset of input is the sample matrix of tape label, and row matrix indicates different training samples, and list sample sheet is complete
Characteristic, training dataset is trained test using ten folding interior extrapolation methods, entire data set is consistent according to quantity, at random
It is divided into 10 parts, successively takes and be wherein used as training set for 7 parts, 3 are allocated as being trained for validation test collection, train a nonlinear system
System, using abnormal data as input, using classification results as output；
After having executed multi-classification algorithm, the sample point of no label is put into set computational discrimination value, discriminant value falls in different thresholds
Different severity, the degree coefficient of computational discrimination value are represented in value
Wherein, K indicates degree coefficient, amaxIt indicates 6 dimensional vectors, is made of 6 dimension acceleration peak values of sample, β is by most tight
Each element gets the new vector of number composition, W respectively in 6 dimensional vectors that 6 dimension acceleration peak values of weight abnormal conditions are constitutedTIt is one
A weight coefficient matrix,Symbol indicates that two are waited the element of dimensional vectors corresponding position to be multiplied.
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| CN111815250A (en) * | 2020-09-11 | 2020-10-23 | 北京福佑多多信息技术有限公司 | Goods state identification method and device for logistics and two-classification modeling method |
| CN112325936A (en) * | 2020-10-30 | 2021-02-05 | 北京印刷学院 | Logistics environment detection and identification method and system |
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