CN109191049A - A kind of logistics transportation supervisory systems and monitoring and managing method based on cloud computing - Google Patents
A kind of logistics transportation supervisory systems and monitoring and managing method based on cloud computing Download PDFInfo
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- CN109191049A CN109191049A CN201811037096.3A CN201811037096A CN109191049A CN 109191049 A CN109191049 A CN 109191049A CN 201811037096 A CN201811037096 A CN 201811037096A CN 109191049 A CN109191049 A CN 109191049A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/061—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using biological neurons, e.g. biological neurons connected to an integrated circuit
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
Abstract
The logistics transportation supervisory systems based on cloud computing that the invention discloses a kind of, comprising: top control module；Monitoring logistics transportation module；Material handling module；Personnel's administration module；First data module, input terminal are connect with the output end of the monitoring logistics transportation module；Second data module, input terminal are connect with the output end of the material handling module；Third data module, input terminal are connect with the output end of personnel's administration module；Alarm module is connect with the top control module；Wherein, first data module, second data module, the third data module are connect with the input terminal of the top control module simultaneously.The control method for the logistics transportation supervisory systems based on cloud computing that the invention discloses a kind of.
Description
Technical field
The present invention relates to logistics managements, and in particular to a kind of logistics transportation supervisory systems and monitoring party based on cloud computing
Method.
Background technique
Logistics refers to meet the needs of client, is realized with minimum cost by modes such as transport, keeping, dispatchings
Raw material, semi-finished product, finished product or relevant information carry out the place of production by commodity to commodity area of consumption plan, implementation and management
Overall process.Logistics is the system of a control raw material, manufactured goods, finished product and information, through various intermediate rings since supply
The transfer of section and possess and reach the movement in kind in ultimate consumer's hand, the hard objectives of tissue are realized with this.Modern logistics
It is the product of economic globalization, and pushes the critical services industry of economic globalization.World Modern logistics is in steady-state growth state
Gesture, Europe, the U.S., Japan become the important Logistic Base within the scope of Present Global.
China's Logistics Industry is started late, with the rapid development of the national economy, China's Logistics Industry continues to increase rapidly
Speed, logistics system constantly improve, and industry operation is increasingly mature and standardizes.
Modern logistics are typically considered by transport, storage, packaging, handling, circulation and process, dispatching and all link structures of information
At.Each link has respective function, interests and idea originally.Systems approach is exactly to utilize modern management method and modern skill
Art makes links share overall information, and all links are organized and managed as an integrated system, so that
System can provide the customer service of competitive superiority under the conditions of alap totle drilling cost.Systems approach thinks, system
Benefit is not the simple addition of their each local link benefits.Systems approach it is meant that for appearance some aspect
The problem of, whole influence factors is analyzed and evaluated.From this thought, logistics system is simultaneously not simply pursued
The respective least cost on links because exist between the benefit of each link of logistics influence each other, inclining of mutually restricting
To there is alternately delicate relationships.For example overemphasize the saving of packaging material, it is possible to transport caused by being easy to breakage because of it
With the rising of terminal charges.Therefore, systems approach emphasizes totle drilling cost analysis to be carried out, and avoids sub-optimal effect and cost trade-offs
The analysis of application to reach the lowest cost, while meeting the purpose of set level of customer service.
In order to guarantee the orderly progress of logistics, need using to logistic management system, existing logistics management module is often
Can only cabinet cargo be managed, chain of command is smaller, can not be managed to personnel and haulage vehicle, influences the use of people.
Summary of the invention
The present invention has designed and developed a kind of logistics transportation supervisory systems based on cloud computing, and the purpose of the present invention is can be more than enough
Angle acquisition logistics transportation system simultaneously makes monitoring management.
The present invention has designed and developed a kind of logistics transportation monitoring and managing method based on cloud computing, and goal of the invention of the invention is base
After cargo and environmental information in many-sided acquisition logistics transportation, reasonable prison is made to logistics transportation based on BP neural network
Pipe.
Technical solution provided by the invention are as follows:
A kind of logistics transportation supervisory systems based on cloud computing characterized by comprising
Top control module；
Monitoring logistics transportation module；
Material handling module；
Personnel's administration module；
First data module, input terminal are connect with the output end of the monitoring logistics transportation module；
Second data module, input terminal are connect with the output end of the material handling module；
Third data module, input terminal are connect with the output end of personnel's administration module；
Alarm module is connect with the top control module；
Wherein, first data module, second data module, the third data module simultaneously with the master control
The input terminal of module connects.
Preferably, the monitoring logistics transportation module includes temperature sensor, humidity sensor, measured oxygen concentration biography
Sensor and carbon dioxide concentration measurement sensor；
The material handling module includes stock up RF Reader, shipment RF Reader and weight sensor；And
Personnel's administration module includes staff attendance reading card device.
Preferably, further includes:
Temperature-controlling air-conditioning is connect with the temperature sensor；
Humidifier is connect with the humidity sensor；
Oxygenerator is connect with the measured oxygen concentration sensor；
Blower is connect with the carbon dioxide concentration measurement sensor；
Wherein, the temperature-controlling air-conditioning, the humidifier, the oxygenerator and the blower connect with the top control module simultaneously
It connects.
A kind of logistics transportation monitoring and managing method based on cloud computing, using the logistics transportation supervisory systems, using BP mind
The temperature-controlling air-conditioning, the humidifier, the oxygenerator and the blower are controlled through network, included the following steps:
Step 1: measuring the weight M of cargo in supervision region, supervision region by weight sensor according to the sampling period
Outer environment temperature TO, cargo oxygen demand to be regulatedCargo demand temperature T to be regulatedT；
Step 2: successively parameter is standardized, the input layer vector x={ x of three layers of BP neural network is determined1,x2,
x3,x4}；Wherein, x1For weight coefficient, x2For environment temperature coefficient, x3For oxygen demand coefficient of discharge, x4For demand temperature coefficient；
Step 3: the input layer DUAL PROBLEMS OF VECTOR MAPPING is to middle layer, the middle layer vector y={ y1,y2,…,ym}；M is
Middle layer node number；
Step 4: obtaining output layer vector z={ z1,z2,z3,z4}；Wherein, z1It is adjusted for temperature-controlling air-conditioning control valve opening and is
Number, z2For humidifier regulating valve aperture regulation coefficient, z3For oxygenerator oxygen feeding amount control valve opening adjustment factor, z4Turn for blower
Fast adjustment factor；
Step 5: control temperature-controlling air-conditioning regulating valve, humidifier regulating valve, oxygenerator oxygen feeding amount regulating valve and rotation speed of fan,
Make
θa(i+1)=z1 iθa_max,
θb(i+1)=z2 iθb_max,
θc(i+1)=z3 iθc_max,
ωi+1=z4 iωmax,
Wherein, wherein z1 i、z2 i、z3 i、z4 iRespectively ith sample period output layer vector parameter, θa_max、θb_max、
θc_max、ωmaxThe maximum opening of the temperature-controlling air-conditioning regulating valve respectively set, the maximum opening of humidifier regulating valve, oxygenerator into
The maximum (top) speed of the maximum opening of oxygen flow regulation valve, blower, θa(i+1)、θb(i+1)、θc(i+1)、ωi+1Respectively i+1 sampling week
The aperture of temperature-controlling air-conditioning regulating valve when the phase, the aperture of humidifier regulating valve, the aperture of oxygenerator oxygen feeding amount regulating valve, blower turn
Speed；
Step 6: being adjusted to temperature-controlling air-conditioning regulating valve, humidifier regulating valve, oxygenerator oxygen feeding amount regulating valve and rotation speed of fan
Afterwards, top control module supervises the temperature T in region by temperature sensor measurementI, humidity sensor measurement supervision region in it is wet
RH is spent, measured oxygen concentration sensor measurement supervises the oxygen concentration in regionCarbon dioxide concentration measurement sensor measurement
Supervise the gas concentration lwevel in regionReal time processing is monitored to supervisory systems, to supervisory systems into
Row monitoring.
Preferably, in the step 2, weight M, the environment temperature T of cargoO, oxygen demandAnd temperature TT
Carry out specification formula are as follows:
Wherein, xjFor the parameter in input layer vector, XjRespectively measurement parameter M, TO、TT, j=1,2,3,4；
XjmaxAnd XjminMaximum value and minimum value in respectively corresponding measurement parameter.
Preferably, in the step 6, supervisory systems is monitored using BP neural network, including walks as follows
It is rapid:
Step 1, according to the sampling period, pass through temperature sensor measurement and supervise temperature T in regionI, humidity sensor survey
Humidity RH in amount supervision region, measured oxygen concentration sensor measurement supervise the oxygen concentration in regionCarbon dioxide is dense
Spend the gas concentration lwevel in measurement sensor measurement supervision regionAcquire the rf frequency F that stocks upa, acquire shipment radio frequency
Frequency Fb；
Step 2 successively standardizes parameter, determines the input layer vector x={ x of three layers of BP neural network1,x2,
x3,x4,x5,x6}；Wherein, x1For temperature coefficient, x2For humidity coefficient, x3For oxygen concentration coefficient, x4For gas concentration lwevel system
Number, x5For rf frequency coefficient of stocking up, x6For shipment rf frequency coefficient；
Step 3, the input layer DUAL PROBLEMS OF VECTOR MAPPING to middle layer, the middle layer vector y={ y1,y2,…,ym}；During m is
Interbed node number；
Step 4 obtains output layer neuron vector o={ o1,o2,o3,o4}；Wherein, o1It is good for supervisory systems operating status,
o2General, the o for supervisory systems operation3Poor, o is run for supervisory systems4For supervisory systems early warning, the output layer neuron value isK be output layer neuron sequence number, k={ 1,2,3,4 }, i be state of the art value, i=1,
2,3,4 }, work as okWhen being 1, supervisory systems is in o at this timekCorresponding state of the art；Top control module to supervisory systems monitoring data into
The real-time analysis processing of row, is monitored supervisory systems.
Preferably, in the step 2, temperature TI, humidity RH, oxygen concentrationGas concentration lwevelIt stocks up
Rf frequency FaAnd shipment rf frequency FbCarry out specification formula are as follows:
Wherein, xjFor the parameter in input layer vector, XjRespectively measurement parameter TI、RH、 Fa、Fb, j=
1,2,3,4,5,6；XjmaxAnd XjminMaximum value and minimum value in respectively corresponding measurement parameter.
Preferably, the starting of the temperature-controlling air-conditioning adjusts aperture are as follows:
Wherein, f (M)=0.042ln (M)+0.222；
In formula, θa_maxFor the maximal regulated aperture of temperature-controlling air-conditioning, λaFor the first adjustment factor, TTFor cargo demand to be regulated
Temperature, TOFor the overseas environment temperature of controlled area, TIFor the temperature in supervision region, Aa1It is related to temperature-controlling air-conditioning aperture regulation
First constant, Aa2For second constant relevant to temperature-controlling air-conditioning aperture regulation, M is the weight for supervising cargo in region, χMFor
Weight correction coefficient.
Preferably, the starting of the oxygen machine adjusts aperture are as follows:
Wherein, f (M)=0.042ln (M)+0.222, f (RH)=2.096 (RH)0.055；
In formula, θc_maxFor the maximal regulated aperture of oxygenerator oxygen feeding amount, λbFor the second adjustment factor, CO2For in supervision region
Oxygen concentration,To supervise the gas concentration lwevel in region,For cargo oxygen demand to be regulated, M is supervision
The weight of cargo, χ in regionMFor weight correction coefficient, Ab1For first constant relevant to oxygenerator aperture regulation, Ab2For with system
The relevant second constant of oxygen machine aperture regulation, δRHFor humidity correction factor.
Preferably, λaValue range is 1.33~1.41, λbValue range is 1.35~1.43, Aa1It is 0.134, Aa2
It is 0.347, χMIt is 1.08, Ab1It is 0.248, Ab2It is 0.565, δRHIt is 0.98.
The present invention compared with prior art possessed by the utility model has the advantages that
1, the present invention is according to the relevant information in the information and transport process of supervision of cargo, based on BP neural network to object
Progress temperature control, control oxygen, control are wet in streaming system, to guarantee to reach preservation demand during transportation；
2, the present invention is steady based on operation of the BP neural network to monitoring system according to supervision of the cargo environment and external environment
It is qualitative to make reasonability supervision；
3, the present invention can to supervision environment in initial temperature, starting air in oxygen concentration be adjusted with reach protect
Deposit regulatory requirements.
Specific embodiment
The present invention is described in further detail below, to enable those skilled in the art's refer to the instruction text being capable of evidence
To implement.
The logistics transportation supervisory systems based on cloud computing that the present invention provides a kind of, comprising: top control module, logistics transportation prison
Control module, material handling module, personnel's administration module, the first data module, the second data module, third data module, alarm
Module；Wherein, the input terminal of the first data module is connect with the output end of the monitoring logistics transportation module, the second data module
Input terminal connect with the output end of the material handling module, the input terminal of third data module and personnel's administration module
Output end connection, alarm module connect with top control module；Wherein, the first data module, the second data module, third number
It is connect simultaneously with the input terminal of top control module according to module.
In another embodiment, top control module includes temperature sensor, humidity sensor, measured oxygen concentration sensor
With carbon dioxide concentration measurement sensor, the material handling module include stock up RF Reader, shipment RF Reader and
Weight sensor, personnel's administration module include staff attendance reading card device.
In another embodiment, further includes: temperature-controlling air-conditioning, humidifier, oxygenerator and blower；Wherein, temperature-controlling air-conditioning with
Temperature sensor, humidifier are connect with humidity sensor, and oxygenerator is connect with measured oxygen concentration sensor, blower and titanium dioxide
The connection of concentration of carbon measurement sensor；Wherein, temperature-controlling air-conditioning, humidifier, oxygenerator and blower are connect with top control module simultaneously.
The present invention provides a kind of logistics transportation monitoring and managing method based on cloud computing, top control module carry out monitoring data real
When analysis handle, the temperature-controlling air-conditioning, the humidifier, the oxygenerator and the blower are controlled based on BP neural network
System, includes the following steps:
Step 1: establishing BP mind for network model；
For the BP network architecture that the present invention uses by up of three-layer, first layer is input layer, total n node, corresponding
Indicate that n detection signal of equipment working state, these signal parameters are provided by data preprocessing module.The second layer is hidden layer,
Total m node is determined in an adaptive way by the training process of network.Third layer is output layer, total p node, by system
Actual needs output in response to determining that.
The mathematical model of the network are as follows:
Input layer vector: x=(x1,x2,…,xn)T
Middle layer vector: y=(y1,y2,…,ym)T
Output layer vector: z=(z1,z2,…,zp)T
In the present invention, input layer number is n=4, and output layer number of nodes is p=4.Hidden layer number of nodes m is estimated by following formula
It obtains:
By the weight M of cargo in weight sensor Monitoring and supervision region, by temperature sensor Monitoring and supervision region outside
Environment temperature TO, determine cargo oxygen demand to be regulatedDetermine cargo demand temperature T to be regulatedT；
According to sampling period, 4 parameters of input are as follows: x1For weight coefficient, x2For environment temperature coefficient, x3It is needed for oxygen
Ask coefficient of discharge, x4For demand temperature coefficient；
Since the data that sensor obtains belong to different physical quantitys, dimension is different.Therefore, mind is inputted in data
Before network, need to turn to data requirement into the number between 0-1.
Specifically, measuring the weight M of cargo in supervision region using weight sensor, after being standardized, ring is obtained
Border temperature coefficient x1:
Wherein, MminAnd MmaxRespectively supervise the minimum weight and maximum weight of cargo in region.
Likewise, the environment temperature T overseas using temperature sensor measurement controlled areaO, after being standardized, obtain environment
Humidity coefficient x2:
Wherein, TO_minAnd TO_maxThe respectively overseas minimum temperature of controlled area and maximum temperature.
Determine cargo oxygen demand to be regulatedAfter being standardized, oxygen demand coefficient of discharge x is obtained3:
Wherein,WithRespectively minimum oxygen demand and maximum oxygen demand.
Determine cargo demand temperature T to be regulatedT, after being standardized, obtain engine temperature coefficient x4:
Wherein, TT_minAnd TT_maxRespectively minimum essential requirement temperature and greatest requirements temperature.
4 parameters of output signal respectively indicate are as follows: z1For temperature-controlling air-conditioning control valve opening adjustment factor, z2For humidifier
Control valve opening adjustment factor, z3For oxygenerator oxygen feeding amount control valve opening adjustment factor, z4For rotation speed of fan adjustment factor；
Temperature-controlling air-conditioning control valve opening adjustment factor z1The temperature-controlling air-conditioning regulating valve being expressed as in next sampling period is opened
The ratio between the maximum opening set in degree and current sample period, i.e., in the ith sample period, collected control valve opening is
θai, the control valve opening adjustment factor z in ith sample period is exported by BP neural network1 iAfterwards, control i+1 sampling week
Interim temperature-controlling air-conditioning control valve opening is θa(i+1), it is made to meet θa(i+1)=z1 iθa_max；
Humidifier regulating valve aperture regulation coefficient z2Be expressed as humidifier regulating valve aperture in next sampling period with
The ratio between maximum opening set in current sample period, i.e., in the ith sample period, collected control valve opening is θbi,
The control valve opening adjustment factor z in ith sample period is exported by BP neural network2 iAfterwards, it controls in the i+1 sampling period
Humidifier regulating valve aperture is θb(i+1), it is made to meet θb(i+1)=z2 iθb_max；
Oxygenerator oxygen feeding amount control valve opening adjustment factor z3The oxygenerator oxygen feeding amount being expressed as in next sampling period
The ratio between the maximum opening set in control valve opening and current sample period, i.e., it is collected into oxygen in the ith sample period
Adjustable valve aperture is θci, the oxygen feeding amount control valve opening adjustment factor z in ith sample period is exported by BP neural network3 i
Afterwards, controlling oxygenerator oxygen feeding amount control valve opening in the i+1 sampling period is θc(i+1), it is made to meet θc(i+1)=z3 iθc_max；
Rotation speed of fan adjustment factor z4It is expressed as setting in the rotation speed of fan and current sample period in next sampling period
The ratio between fixed maximum (top) speed, i.e., in the ith sample period, collected rotation speed of fan is ωi, exported by BP neural network
The rotation speed of fan adjustment factor z in ith sample period4 iAfterwards, controlling rotation speed of fan in the i+1 sampling period is ωi+1, make it
Meet ωi+1=z4 iωmax；
Step 2: carrying out the training of BP neural network.
After establishing BP neural network nodal analysis method, the training of BP neural network can be carried out.According to the experience number of product
According to the sample for obtaining training, and give the connection weight w between input node i and hidden layer node jij, hidden node j and output
Connection weight w between node layer kjk, the threshold θ of hidden node jj, export the threshold value w of node layer kij、wjk、θj、θkIt is -1
Random number between to 1.
In the training process, w is constantly correctedijAnd wjkValue, until systematic error be less than or equal to anticipation error when, complete
The training process of neural network.
As shown in table 1, given the value of each node in one group of training sample and training process.
Each nodal value of 1 training process of table
Step 3: acquisition data run parameter input neural network is regulated coefficient；
Trained artificial neural network is solidificated among chip, and hardware circuit is made to have prediction and intelligent decision function,
To form Intelligent hardware.After Intelligent hardware power-up starting, temperature-controlling air-conditioning, humidifier, oxygenerator and blower bring into operation, humidification
Device regulating valve initial opening is θb0=0.88 θb_max, blower initial speed is ω0=0.93 ωmax；
Wherein, the starting of temperature-controlling air-conditioning adjusts aperture are as follows:
Wherein, f (M)=0.042ln (M)+0.222；
In formula, θa_maxFor the maximal regulated aperture of temperature-controlling air-conditioning, λaFor the first adjustment factor, TTFor cargo demand to be regulated
Temperature, TOFor the overseas environment temperature of controlled area, unit is DEG C TIFor supervision region in temperature, unit be DEG C, Aa1For with control
The relevant first constant of warm air-conditioning aperture regulation, Aa2For second constant relevant to temperature-controlling air-conditioning aperture regulation, M is supervision region
The weight of interior cargo, unit kg, χMFor weight correction coefficient, unit kg；
The starting of oxygen machine adjusts aperture are as follows:
Wherein, f (M)=0.042ln (M)+0.222, f (RH)=2.096 (RH)0.055；
In formula, θc_maxFor the maximal regulated aperture of oxygenerator oxygen feeding amount, λbFor the second adjustment factor,To supervise region
Interior oxygen concentration, unit mL/cm3,For the gas concentration lwevel in supervision region, unit mL/cm3,For to
Supervise cargo oxygen demand, unit mL/cm3, M is the weight for supervising cargo in region, unit kg, χMFor weight correction
Coefficient, unit kg, Ab1For first constant relevant to oxygenerator aperture regulation, Ab2It is relevant to oxygenerator aperture regulation
Second constant, δRHFor humidity correction factor, unit %；
Meanwhile initial weight M is measured by using weight sensor0, the overseas original ambient temperature T of controlled areaO_0, to
Supervise cargo oxygen initial demand amountCargo initial demand temperature T to be regulatedT_0, by the way that above-mentioned parameter is standardized, obtain
To the initial input vector of BP neural networkBy the operation of BP neural network initially exported to
Amount
Step 4: control temperature-controlling air-conditioning control valve opening, humidifier regulating valve aperture, oxygenerator oxygen feeding amount regulating valve are opened
Degree, rotation speed of fan；Obtain initial output vectorAfterwards, the tune of valve opening and revolving speed can be adjusted
Control adjusts temperature-controlling air-conditioning control valve opening, humidifier regulating valve aperture, oxygenerator oxygen feeding amount control valve opening, rotation speed of fan, makes
Next sampling period temperature-controlling air-conditioning control valve opening, humidifier regulating valve aperture, oxygenerator oxygen feeding amount control valve opening, blower
Revolving speed is respectively as follows:
θa1=z1 0θa_max,
θb1=z2 0θb_max,
θc1=z3 0θC_max,
ω=z4 0ωmax,
The overseas environment of weight M, the controlled area for supervising cargo in region in the ith sample period is obtained by sensor
Temperature TO, cargo oxygen demand to be regulatedAnd cargo demand temperature T to be regulatedT, i-th is obtained by being standardized
The input vector x in a sampling periodi=(x1 i,x2 i,x3 i,x4 i), the ith sample period is obtained by the operation of BP neural network
Output vector zi=(z1 i,z2 i,z3 i,z4 i), then control temperature-controlling air-conditioning control valve opening, humidifier regulating valve aperture, oxygen processed
Machine oxygen feeding amount control valve opening, rotation speed of fan, temperature-controlling air-conditioning control valve opening, humidifier are adjusted when making the i+1 sampling period
Valve opening, oxygenerator oxygen feeding amount control valve opening, rotation speed of fan are respectively as follows:
θa(i+1)=z1 iθa_max,
θb(i+1)=z2 iθb_max,
θc(i+1)=z3 iθc_max,
ωi+1=z4 iωmax,
Step 5: being adjusted to temperature-controlling air-conditioning regulating valve, humidifier regulating valve, oxygenerator oxygen feeding amount regulating valve and rotation speed of fan
Afterwards, top control module supervises the temperature T in region by temperature sensor measurementI, humidity sensor measurement supervision region in it is wet
RH is spent, measured oxygen concentration sensor measurement supervises the oxygen concentration in regionCarbon dioxide concentration measurement sensor measurement
Supervise the gas concentration lwevel in regionReal time processing is monitored to supervisory systems, to supervisory systems into
Row monitoring.
In the present invention, real-time analysis processing is carried out to engine monitoring data for the main controller in step 5, determined
Engine technology state comprising following steps:
Step 1: establishing BP neural network model.
Totally interconnected connection is formed on BP model between the neuron of each level, is not connected between the neuron in each level
It connects, the output of input layer is identical as input, i.e. oi=xi.The operating characteristic of the neuron of intermediate hidden layer and output layer
For
opj=fj(netpj)
Wherein p indicates current input sample, ωjiFor from neuron i to the connection weight of neuron j, opiFor neuron
The current input of j, opjIt is exported for it；fjFor it is non-linear can micro- non-decreasing function, be generally taken as S type function, i.e. fj(x)=1/ (1
+e-x)。
For the BP network architecture that the present invention uses by up of three-layer, first layer is input layer, total n node, corresponding
Indicate that n detection signal of equipment working state, these signal parameters are provided by data preprocessing module；The second layer is hidden layer,
Total m node is determined in an adaptive way by the training process of network；Third layer is output layer, total p node, by system
Actual needs output in response to determining that.
The mathematical model of the network are as follows:
Input vector: x=(x1,x2,...,xn)T
Middle layer vector: y=(y1,y2,...,ym)T
Output vector: o=(o1,o2,...,op)T
In the present invention, input layer number is n=6, and output layer number of nodes is p=4, hidden layer number of nodes m=5.
6 parameters of input layer respectively indicate are as follows: x1For temperature coefficient, x2For humidity coefficient, x3For oxygen concentration coefficient, x4
For gas concentration lwevel coefficient, x5For rf frequency coefficient of stocking up, x6For shipment rf frequency coefficient；
4 parameters of output layer respectively indicate are as follows: o1Good, the o for supervisory systems operating status2General, the o for supervisory systems operation3
Poor, o is run for supervisory systems4For supervisory systems early warning, the output layer neuron value isK is
Output layer neuron sequence number, k={ 1,2,3,4 }, i are state of the art value, and i={ 1,2,3,4 } works as okWhen being 1, supervise at this time
Guard system is in okCorresponding state of the art；Top control module carries out real-time analysis processing to supervisory systems monitoring data, to supervision
System is monitored.
Step 2: carrying out the training of BP neural network.
After establishing BP neural network nodal analysis method, the training of BP neural network can be carried out.It is passed through according to the history of product
Test the sample of data acquisition training, and the connection weight between given input node i and hidden layer node j, hidden node j and defeated
Connection weight between node layer k out.
(1) training method
Each subnet is using individually trained method；When training, first have to provide one group of training sample, each of these sample
This, to forming, when all reality outputs of network and its consistent ideal output, is shown to train by input sample and ideal output
Terminate；Otherwise, by correcting weight, keep the ideal output of network consistent with reality output；Output sample when the training of each subnet
As shown in table 5.
The output sample of 5 network training of table
(2) training algorithm
BP network is trained using error back propagation (Backward Propagation) algorithm, and step can be concluded
It is as follows:
Step 1: a selected structurally reasonable network, is arranged the initial value of all Node B thresholds and connection weight.
Step 2: making following calculate to each input sample:
(a) forward calculation: to l layers of j unit
In formula,L layers of j unit information weighted sum when being calculated for n-th,For l layers of j units with it is previous
Connection weight between the unit i of layer (i.e. l-1 layers),For preceding layer (i.e. l-1 layers, number of nodes nl-1) unit i give
The working signal come；When i=0, enableFor the threshold value of l layers of j unit.
If the activation primitive of unit j is sigmoid function,
And
If neuron j belongs to the first hidden layer (l=1), have
If neuron j belongs to output layer (l=L), have
And ej(n)=xj(n)-oj(n)；
(b) retrospectively calculate error:
For output unit
To hidden unit
(c) weight is corrected:
η is learning rate.
Step 3: new sample or a new periodic samples are inputted, and until network convergence, the sample in each period in training
Input sequence is again randomly ordered.
BP algorithm seeks nonlinear function extreme value using gradient descent method, exists and falls into local minimum and convergence rate is slow etc.
Problem.A kind of more efficiently algorithm is Levenberg-Marquardt optimization algorithm, it makes the e-learning time shorter,
Network can be effectively inhibited and sink into local minimum.Its weighed value adjusting rate is selected as
Δ ω=(JTJ+μI)-1JTe
Wherein J is error to Jacobi (Jacobian) matrix of weight differential, and I is input vector, and e is error vector,
Variable μ is the scalar adaptively adjusted, for determining that study is completed according to Newton method or gradient method.
In another embodiment, λaValue range is 1.33~1.41, λbValue range is 1.35~1.43, Aa1For
0.134, Aa2It is 0.347, χMIt is 1.08, Ab1It is 0.248, Ab2It is 0.565, δRHIt is 0.98；As a preference, λaValue is
1.35 λbValue is 1.41.
In another embodiment, personnel's administration module includes staff attendance reading card device, and top control module can examine personnel
Diligent situation exercises supervision.
Although the embodiments of the present invention have been disclosed as above, but its is not only in the description and the implementation listed
With it can be fully applied to various fields suitable for the present invention, for those skilled in the art, can be easily
Realize other modification, therefore without departing from the general concept defined in the claims and the equivalent scope, the present invention is simultaneously unlimited
In specific details and shown here as with description.
Claims (10)
1. a kind of logistics transportation supervisory systems based on cloud computing characterized by comprising
Top control module；
Monitoring logistics transportation module；
Material handling module；
Personnel's administration module；
First data module, input terminal are connect with the output end of the monitoring logistics transportation module；
Second data module, input terminal are connect with the output end of the material handling module；
Third data module, input terminal are connect with the output end of personnel's administration module；
Alarm module is connect with the top control module；
Wherein, first data module, second data module, the third data module simultaneously with the top control module
Input terminal connection.
2. a kind of logistics transportation supervisory systems based on cloud computing as described in claim 1, which is characterized in that the logistics fortune
Defeated monitoring module includes temperature sensor, humidity sensor, measured oxygen concentration sensor and carbon dioxide concentration measurement sensing
Device；
The material handling module includes stock up RF Reader, shipment RF Reader and weight sensor；And
Personnel's administration module includes staff attendance reading card device.
3. a kind of logistics transportation supervisory systems based on cloud computing as claimed in claim 2, which is characterized in that further include:
Temperature-controlling air-conditioning is connect with the temperature sensor；
Humidifier is connect with the humidity sensor；
Oxygenerator is connect with the measured oxygen concentration sensor；
Blower is connect with the carbon dioxide concentration measurement sensor；
Wherein, the temperature-controlling air-conditioning, the humidifier, the oxygenerator and the blower are connect with the top control module simultaneously.
4. a kind of logistics transportation monitoring and managing method based on cloud computing, which is characterized in that use logistics transportation as claimed in claim 3
Supervisory systems controls the temperature-controlling air-conditioning, the humidifier, the oxygenerator and the blower using BP neural network
System, includes the following steps:
Step 1: weight M, the controlled area for measuring cargo in supervision region by weight sensor are overseas according to the sampling period
Environment temperature TO, cargo oxygen demand to be regulatedCargo demand temperature T to be regulatedT；
Step 2: successively parameter is standardized, the input layer vector x={ x of three layers of BP neural network is determined1,x2,x3,x4}；
Wherein, x1For weight coefficient, x2For environment temperature coefficient, x3For oxygen demand coefficient of discharge, x4For demand temperature coefficient；
Step 3: the input layer DUAL PROBLEMS OF VECTOR MAPPING is to middle layer, the middle layer vector y={ y1,y2,…,ym}；M is middle layer
Node number；
Step 4: obtaining output layer vector z={ z1,z2,z3,z4}；Wherein, z1For temperature-controlling air-conditioning control valve opening adjustment factor,
z2For humidifier regulating valve aperture regulation coefficient, z3For oxygenerator oxygen feeding amount control valve opening adjustment factor, z4For rotation speed of fan tune
Save coefficient；
Step 5: control temperature-controlling air-conditioning regulating valve, humidifier regulating valve, oxygenerator oxygen feeding amount regulating valve and rotation speed of fan, make
θa(i+1)=z1 iθa_max,
θb(i+1)=z2 iθb_max,
θc(i+1)=z3 iθc_max,
ωi+1=z4 iωmax,
Wherein, wherein z1 i、z2 i、z3 i、z4 iRespectively ith sample period output layer vector parameter, θa_max、θb_max、θc_max、
ωmaxThe maximum opening of the temperature-controlling air-conditioning regulating valve respectively set, the maximum opening of humidifier regulating valve, oxygenerator oxygen feeding amount
The maximum (top) speed of the maximum opening of regulating valve, blower, θa(i+1)、θb(i+1)、θc(i+1)、ωi+1Respectively the i+1 sampling period when
Temperature-controlling air-conditioning regulating valve aperture, the aperture of humidifier regulating valve, the aperture of oxygenerator oxygen feeding amount regulating valve, rotation speed of fan；
Step 6: after being adjusted to temperature-controlling air-conditioning regulating valve, humidifier regulating valve, oxygenerator oxygen feeding amount regulating valve and rotation speed of fan,
Top control module supervises the temperature T in region by temperature sensor measurementI, humidity sensor measurement supervision region in humidity
RH, measured oxygen concentration sensor measurement supervise the oxygen concentration in regionCarbon dioxide concentration measurement sensor measurement prison
Gas concentration lwevel in the domain of area under controlReal time processing is monitored to supervisory systems, supervisory systems is carried out
Monitoring.
5. the logistics transportation monitoring and managing method based on cloud computing as claimed in claim 4, which is characterized in that in the step 2,
Weight M, the environment temperature T of cargoO, oxygen demandAnd temperature TTCarry out specification formula are as follows:
Wherein, xjFor the parameter in input layer vector, XjRespectively measurement parameter M, TO、TT, j=1,2,3,4；XjmaxWith
XjminMaximum value and minimum value in respectively corresponding measurement parameter.
6. the logistics transportation monitoring and managing method based on cloud computing as claimed in claim 4, which is characterized in that in the step 6
In, supervisory systems is monitored using BP neural network, is included the following steps:
Step 1, according to the sampling period, pass through temperature sensor measurement and supervise temperature T in regionI, humidity sensor measurement supervision
Humidity RH in region, measured oxygen concentration sensor measurement supervise the oxygen concentration in regionCarbon dioxide concentration measurement
Sensor measurement supervises the gas concentration lwevel in regionAcquire the rf frequency F that stocks upa, acquire shipment rf frequency Fb；
Step 2 successively standardizes parameter, determines the input layer vector x={ x of three layers of BP neural network1,x2,x3,x4,
x5,x6}；Wherein, x1For temperature coefficient, x2For humidity coefficient, x3For oxygen concentration coefficient, x4For gas concentration lwevel coefficient, x5
For rf frequency coefficient of stocking up, x6For shipment rf frequency coefficient；
Step 3, the input layer DUAL PROBLEMS OF VECTOR MAPPING to middle layer, the middle layer vector y={ y1,y2,…,ym}；M is middle layer
Node number；
Step 4 obtains output layer neuron vector o={ o1,o2,o3,o4}；Wherein, o1Good, the o for supervisory systems operating status2For
Supervisory systems operation is general, o3Poor, o is run for supervisory systems4For supervisory systems early warning, the output layer neuron value isK be output layer neuron sequence number, k={ 1,2,3,4 }, i be state of the art value, i=1,2,
3,4 }, work as okWhen being 1, supervisory systems is in o at this timekCorresponding state of the art；Top control module carries out supervisory systems monitoring data
Real-time analysis processing, is monitored supervisory systems.
7. the logistics transportation monitoring and managing method based on cloud computing as claimed in claim 6, which is characterized in that in the step 2, temperature
Spend TI, humidity RH, oxygen concentrationGas concentration lwevelStock up rf frequency FaAnd shipment rf frequency FbIt carries out
Specification formula are as follows:
Wherein, xjFor the parameter in input layer vector, XjRespectively measurement parameter TI、RH、Fa、Fb, j=1,2,3,
4,5,6；XjmaxAnd XjminMaximum value and minimum value in respectively corresponding measurement parameter.
8. the logistics transportation monitoring and managing method based on cloud computing as claimed in claim 4, which is characterized in that the temperature-controlling air-conditioning
Starting adjusts aperture are as follows:
Wherein, f (M)=0.042ln (M)+0.222；
In formula, θa_maxFor the maximal regulated aperture of temperature-controlling air-conditioning, λaFor the first adjustment factor, TTFor cargo demand temperature to be regulated,
TOFor the overseas environment temperature of controlled area, TIFor the temperature in supervision region, Aa1It is relevant to temperature-controlling air-conditioning aperture regulation
One constant, Aa2For second constant relevant to temperature-controlling air-conditioning aperture regulation, M is the weight for supervising cargo in region, χMFor weight
Correction coefficient.
9. the logistics transportation monitoring and managing method based on cloud computing as claimed in claim 8, which is characterized in that the oxygen machine rises
Begin to adjust aperture are as follows:
Wherein, f (M)=0.042ln (M)+0.222, f (RH)=2.096 (RH)0.055；
In formula, θc_maxFor the maximal regulated aperture of oxygenerator oxygen feeding amount, λbFor the second adjustment factor,For the oxygen in supervision region
Gas concentration,To supervise the gas concentration lwevel in region,For cargo oxygen demand to be regulated, M is supervision region
The weight of interior cargo, χMFor weight correction coefficient, Ab1For first constant relevant to oxygenerator aperture regulation, Ab2For with oxygenerator
The relevant second constant of aperture regulation, δRHFor humidity correction factor.
10. the logistics transportation monitoring and managing method based on cloud computing as claimed in claim 9, which is characterized in that λaValue range is
1.33~1.41, λbValue range is 1.35~1.43, Aa1It is 0.134, Aa2It is 0.347, χMIt is 1.08, Ab1It is 0.248, Ab2
It is 0.565, δRHIt is 0.98.
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