CN109035477A - A kind of fork truck equipment state comprehensive appraisal procedure, apparatus and system - Google Patents
A kind of fork truck equipment state comprehensive appraisal procedure, apparatus and system Download PDFInfo
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- G—PHYSICS
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- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
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- G07C5/006—Indicating maintenance
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
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Abstract
The invention discloses a kind of fork truck equipment state comprehensive appraisal procedures, apparatus and system, obtain the history data of fork truck, pretreatment and characteristics extraction are carried out by the history data, obtain fork truck equipment state description information, using each dimension index in fork truck equipment state description information as a feature vector, based on history data multiple feature vectors are carried out with part respectively and quantifies marking, generate high-dimensional feature vector, vector is assessed as fork truck holistic health degree, percentage weighted sum is carried out to each component of high-dimensional feature vector, obtain a percentage, using percentage as fork truck holistic health degree assessed value, comprehensive assessment is carried out to fork truck equipment state.Since the present invention is when carrying out comprehensive assessment to fork truck equipment state, multiple dimension indexs have been comprehensively considered, therefore the comprehensive assessment to fork truck operating status may be implemented.
Description
Technical field
The present invention relates to technical field of industrial equipment, more specifically, being related to a kind of fork truck equipment state comprehensive assessment side
Method, apparatus and system.
Background technique
Fork truck is a kind of industrial transportation vehicle, typically refers to load and unload pallet cargo, stacking and short distance fortune
The wheeled transport vehicle of defeated operation.Fork truck is even more important as equipment common in industrial production, the reliability of work, therefore
The equipment state of fork truck is monitored and is assessed the normal use for helping to ensure equipment, extend service life of equipment, guaranteed
The working efficiency of equipment.
Comprehensive assessment is carried out for fork truck equipment state however, there is presently no complete set schemes.
Summary of the invention
In view of this, the present invention discloses a kind of fork truck equipment state comprehensive appraisal procedure, apparatus and system, to realize to fork
The comprehensive assessment of vehicle device state.
A kind of fork truck equipment state comprehensive appraisal procedure, comprising:
Obtain the history data of fork truck；
Pretreatment and characteristics extraction are carried out to the history data, obtain fork truck equipment state description information,
In, the fork truck equipment state description information includes multiple dimension indexs, and the multiple dimension index includes: fork truck load, fork
Vehicle working environment, fork truck power consumption, fork truck utilization rate, fork truck abnormal conditions, the health status of battery, driver operating condition and
It is any several or whole in motor operation situation；
Using each dimension index in the fork truck equipment state description information as a feature vector, gone through based on described
History operation data carries out part respectively to multiple described eigenvectors and quantifies marking, high-dimensional feature vector is generated, as fork truck
Holistic health degree assesses vector；
Percentage weighted sum is carried out to each component of the high-dimensional feature vector, a percentage is obtained, by institute
Percentage is stated as fork truck holistic health degree assessed value, comprehensive assessment is carried out to fork truck equipment state.
Preferably, the fork truck load is to be obtained to the history data using linear regression method.
Preferably, the fork truck load is to be derived by the history data using mechanism model.
Preferably, the process packet to be derived by the fork truck load using mechanism model to the history data
It includes:
The row that lifting power model based on fork truck movement and electric current, torque and revolving speed based on motor operation are established
It walks about mechanical model, obtains fork truck mechanism model；
The fork truck mechanism model is modified to obtain Correction Mechanism model；
Fork truck load computation model is obtained based on the Correction Mechanism model and the history data；
Computation model, which is loaded, according to the fork truck determines that fork truck loads.
Preferably, the fork truck working environment include: fork truck running environment jolt degree, operation road gradient, fork truck fortune
The distance whether row stationarity, fork truck have reversing situation and carrying fork truck one to plow cargo.
Preferably, the fork truck abnormal conditions include: fault alarm classifiction statistics, collision frequency, collision front and back fortune
Row data, overload recording and abnormal road conditions record, the overload recording include: surcharge preloading duration and overload number；The exception road
Condition record includes: that super security standpoint traveling and road surface are jolted extremely.
A kind of fork truck equipment state comprehensive assessment device, comprising:
Acquiring unit, for obtaining the history data of fork truck；
Processing unit obtains fork truck equipment shape for carrying out pretreatment and characteristics extraction to the history data
State description information, wherein the fork truck equipment state description information includes multiple dimension indexs, the multiple dimension index packet
Include: fork truck load, is driven at fork truck working environment, fork truck power consumption, fork truck utilization rate, fork truck abnormal conditions, the health status of battery
It is any several or whole in dynamic device operating condition and motor operation situation；
High-dimensional feature vector generation unit, for by each dimension index in the fork truck equipment state description information
As a feature vector, part is carried out to multiple described eigenvectors based on the history data respectively and quantifies marking,
High-dimensional feature vector is generated, assesses vector as fork truck holistic health degree；
State evaluation unit carries out percentage weighted sum for each component to the high-dimensional feature vector, obtains
To a percentage, using the percentage as fork truck holistic health degree assessed value, comprehensive assessment is carried out to fork truck equipment state.
Preferably, the fork truck load is to be obtained to the history data using linear regression method.
Preferably, the fork truck load is to be derived by the history data using mechanism model.
Preferably, the processing unit is derived by the fork truck using mechanism model to the history data and loads
Process specifically include:
The row that lifting power model based on fork truck movement and electric current, torque and revolving speed based on motor operation are established
It walks about mechanical model, obtains fork truck mechanism model；
The fork truck mechanism model is modified to obtain Correction Mechanism model；
Fork truck load computation model is obtained based on the Correction Mechanism model and the history data；
Computation model, which is loaded, according to the fork truck determines that fork truck loads.
Preferably, the fork truck working environment include: fork truck running environment jolt degree, operation road gradient, fork truck fortune
The distance whether row stationarity, fork truck have reversing situation and carrying fork truck one to plow cargo.
Preferably, the fork truck abnormal conditions include: fault alarm classifiction statistics, collision frequency, collision front and back fortune
Row data, overload recording and abnormal road conditions record, the overload recording include: surcharge preloading duration and overload number；The exception road
Condition record includes: that super security standpoint traveling and road surface are jolted extremely.
A kind of fork truck equipment state assessment system, comprising: fork truck operation data acquires equipment, Cloud Server and local service
Device, the local server include fork truck equipment state comprehensive assessment device described above；
The fork truck operation data acquisition equipment acquires the operation data of fork truck, and will be described for being arranged in fork truck
Operation data is uploaded to the Cloud Server；
The Cloud Server is used to store the history data of fork truck, and can receive what the local server was sent
Data acquisition instruction, is sent to the local server for the history data of fork truck.
Preferably, the history data for the fork truck that the local server is used to will acquire is stored in the distributed text of HDFS
In part system, and it is for statistical analysis to the history data of fork truck using MapReduce Computational frame, obtain fork truck equipment
Context information.
Preferably, the fork truck operation data acquisition equipment includes: oil-lifting jar pressure sensor and acceleration transducer.
From above-mentioned technical solution it is found that the invention discloses a kind of fork truck equipment state comprehensive appraisal procedure, device and
System obtains the history data of fork truck, carries out pretreatment and characteristics extraction by the history data, obtains fork truck
Equipment state description information is based on using each dimension index in fork truck equipment state description information as a feature vector
History data carries out part respectively to multiple feature vectors and quantifies marking, generates high-dimensional feature vector, whole as fork truck
Body health degree assesses vector, carries out percentage weighted sum to each component of high-dimensional feature vector, obtains a percentage,
Using percentage as fork truck holistic health degree assessed value, comprehensive assessment is carried out to fork truck equipment state.Since the present invention is to fork
When vehicle device state carries out comprehensive assessment, multiple dimension indexs have been comprehensively considered, comprising: fork truck load, fork truck working environment, fork
Vehicle power consumption, fork truck utilization rate, fork truck abnormal conditions, the health status of battery, driver operating condition and motor operation situation
In it is any several or whole, it is thereby achieved that the comprehensive assessment to fork truck operating status, to not only solve fork truck dimension
Further device data analyzes required equipment state description information to shield etc., also contributes to fork truck manufacturer and realizes to fork truck
The analysis of longtime running situation, and then and the maintenance of ancillary equipment and design, it is ensured that the normal use of fork truck, extend fork truck use
Service life guarantees the working efficiency of fork truck.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of invention for those of ordinary skill in the art without creative efforts, can also basis
Disclosed attached drawing obtains other attached drawings.
Fig. 1 is a kind of fork truck equipment state comprehensive appraisal procedure flow chart disclosed by the embodiments of the present invention；
Fig. 2 is the structural schematic diagram that a kind of fork truck equipment state comprehensive disclosed by the embodiments of the present invention assesses device；
Fig. 3 is a kind of structural schematic diagram of fork truck equipment state comprehensive assessment system disclosed by the embodiments of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a kind of fork truck equipment state comprehensive appraisal procedures, apparatus and system, obtain fork truck
History data carries out pretreatment and characteristics extraction by the history data, obtains fork truck equipment state description letter
Breath is based on history data pair using each dimension index in fork truck equipment state description information as a feature vector
Multiple feature vectors carry out respectively part quantify marking, generate high-dimensional feature vector, as fork truck holistic health degree assess to
Amount carries out percentage weighted sum to each component of high-dimensional feature vector, a percentage is obtained, using percentage as fork
Vehicle holistic health degree assessed value carries out comprehensive assessment to fork truck equipment state.Since the present invention is carried out to fork truck equipment state
When comprehensive assessment, multiple dimension indexs have been comprehensively considered, comprising: fork truck load, fork truck working environment, fork truck power consumption, fork truck benefit
With any several in rate, fork truck abnormal conditions, the health status of battery, driver operating condition and motor operation situation or
It is whole, it is thereby achieved that the comprehensive assessment to fork truck operating status, to not only solve the further equipment such as fork truck maintenance
Equipment state description information needed for data analysis also contributes to fork truck manufacturer and realizes to fork truck longtime running situation
Analysis, and then and the maintenance of ancillary equipment and design, it is ensured that the normal use of fork truck, extend fork truck service life, guarantee fork truck
Working efficiency.
Referring to Fig. 1, a kind of fork truck equipment state comprehensive appraisal procedure flow chart, this method disclosed in one embodiment of the invention
Comprising steps of
Step S101, the history data of fork truck is obtained；
Wherein, the history data of fork truck includes: to obtain lifting required for fork truck load using linear regression method
Motor feedback torque current, lifting motor speed, cell voltage, movable motor electric current and movable motor speed；
Elevating ram pressure sensor data, acceleration required for fork truck loads are calculated using mechanism model derivation
Sensing data, gyroscope sensor data；Motor speed etc..
Step S102, pretreatment and characteristics extraction are carried out to history data, obtains fork truck equipment state description letter
Breath；
Pretreatment is carried out to history data to refer mainly to: data cleansing being carried out to history data, specifically includes: deleting
Except extraneous data and repeated data that initial data is concentrated, smooth noise data screen out the data unrelated with theme is excavated, processing
Missing values and exceptional value etc..
It should be noted that in fork truck equipment state description information each state description mode or algorithm, not only can be with
Applied to the characteristics extraction to fork truck history data, can be used for proposing the characteristic value of fork truck current operating situation
It takes.
Wherein, the fork truck equipment state description information includes multiple dimension indexs, and the multiple dimension index includes: fork
Vehicle load, fork truck working environment, fork truck power consumption, fork truck utilization rate, fork truck abnormal conditions, the health status of battery, driver
It is any several or whole in operating condition and motor operation situation.
Characteristics extraction is that a kind of group measured value to a certain mode converts, with the prominent representative spy of the mode
A kind of method of sign.
Fork truck equipment state can be described in terms of eight in the present embodiment, in practical applications, can by this eight
A aspect is refined as multiple small characteristic values, by the way that these characteristic values are integrated the synthesis that can be achieved to fork truck equipment state
Analysis, so that the evaluation also more comprehensive and preparation to fork truck state.
In the following, be discussed in detail in terms of eight of fork truck equipment state description, it is specific as follows:
1) fork truck loads
Using linear regression method or mechanism can be used to fork truck history data about fork truck load estimation
Model inference is calculated.
Linear regression method is a kind of regression analysis using in mathematical statistics, to determine between two or more variable
A kind of statistical analysis technique of complementary quantitative relationship.
1. the process for obtaining fork truck load using linear regression method to fork truck history data is as follows:
Fork truck load quality M is obtained using linear regression method to history data.
M=(h1,h2,h3,h4,h5)(x1,x2,x3,x4,x5)T
Wherein, h1To be lifted motor feedback torque current, h2To be lifted motor speed, h3For cell voltage, h4For walking electricity
Electromechanics stream, h5For movable motor speed, x1,x2,x3,x4,x5For Linear Regression Model Parameters.
Wherein, parameter x1,x2,x3,x4,x5Linear regression can be carried out by the history data to fork truck to obtain.
Specifically, the history data using least square method to the fork truck being input in linear regression model (LRM)
Identification is practised, parameter x is obtained1,x2,x3,x4,x5, it can load M is calculated according to linear regression model (LRM).
It should be noted that parameter identification is a kind of skill for combining theoretical model with experimental data and being used for predicting
Art determines one group of parameter value according to experimental data and the model of foundation, enable the numerical result being calculated by model most
Good fitting test data.
The present invention in Learning Identification used parameter identification apply-official formula it is as follows:
K (m+1)=P (m) x (m+1) [1+xT(m+1)P(m)x(m+1)]-1；
P (m)=(xT(m)x(m))-1；
P (m+1)=P (m)-K (m+1) xT(m+1)P(m)；
In formula, x is the input of fork truck system, the h in corresponding fork truck load quality M calculation formula1~h5Value, y is fork truck
The output of system, corresponding fork truck load quality M, m are recursion number,For estimates of parameters, K is gain matrix, and K (m+1) is
Gain matrix under the m+1 times recursion, P (m) are pilot process amount, and x (m+1) is the system input under the m+1 times recursion, xT
It (m+1) is the transposition of the system input under the m+1 times recursion, xTIt (m) is the transposition of the system input under the m times recursion, x
It (m) is the system input under the m times recursion, P (m+1) is pilot process amount,For the parameter under the m+1 times recursion
Estimated value,For the estimates of parameters under the m times recursion, y (m+1) is the system output under the m times recursion.
Wherein, after determining that the corresponding fork truck of fork truck operational data loads, self study can be carried out again and is realized to parameter
Correction, fork truck operational data includes: h1To be lifted motor feedback torque current, h2To be lifted motor speed, h3For cell voltage,
h4For movable motor electric current, h5For movable motor speed, fork truck load is fork truck load quality M.
2. the process that fork truck load is calculated using mechanism model derivation to fork truck history data is as follows:
Fork truck load computation model is obtained in conjunction with the history data of fork truck and the operation mechanism of fork truck, is based on the load
Computation model determine fork truck load, specifically: based on fork truck movement lifting power model (F=ma) and based on motor fortune
The walking kinetic model that capable electric current, torque and revolving speed is established, obtains fork truck mechanism model；Fork truck mechanism model is repaired
Correction Mechanism model is just being obtained, fork truck load computation model is being obtained based on Correction Mechanism model and history data, according to institute
It states fork truck load computation model and determines that fork truck loads.
Wherein, the electric current, torque and revolving speed of motor operation can directly be measured by corresponding sensor obtains.Fork truck
On elevating ram pressure sensor and acceleration transducer are installed, the acceleration transducer for measure forklift-walking acceleration
Degree.
Walking kinetic model are as follows:
Fq-fvV-fsign (v) M=Ma
Fq=τ R-fτ=kIq-fτ
In formula, M is vehicle body overall load, and a is forklift-walking acceleration, FqFor elevating ram pressure, fvIt is viscous for fork truck traveling
Property coefficient of friction, f is the coefficient of sliding friction, fτFor motor spin friction coefficient, τ is fork truck Motor torque, and R is fork truck wheel half
Diameter, IqFor electric current, k is model parameter, and v is the fork truck speed of service, and sign (v) is to be to one mathematical function calculating of v progress, tool
Body is to work as x > 0, sign (x)=1；Work as x=0, sign (x)=0；As x < 0, sign (x)=- 1.
Lifting power model are as follows:
F-mg-f=ma, wherein cylinder thrust is F=kIqOr F=kP
Fork truck load quality calculation formula, model parameter are obtained by identified parameters SimMan universal patient simulator f and model parameter k
K is known quantity.
History data based on fork truck mechanism model and fork truck obtains fork truck load computation model, is loaded using fork truck
Computation model estimates fork truck load maximum value within a preset period of time and load mean value, and will load maximum value and load it is flat
Loading condition of the mean value as fork truck in the preset time period.
2) fork truck working environment
The degree 1. fork truck running environment is jolted；
It jolts degree using acceleration up and down that the acceleration transducer being arranged on fork truck measures as fork truck running environment
Measurement.
2. running road gradient；
The tilt angle that the gyroscope measurement being arranged on fork truck is obtained is as the measurement of operation road gradient.
3. fork truck running stability；
Whether the fore-aft acceleration that the acceleration transducer being arranged on fork truck is measured smoothly spends as fork truck operation
Amount.
4. whether fork truck has reversing situation；
Motor keeps rotating forward during fork truck advances, and shows that truck driver is being carried if negative value occurs in motor speed
There is reversing behavior in the process, it means that fork truck working environment is more narrow, there is the place that cannot turn round.
5. carrying fork truck one plows the distance of cargo；
Determine that carrying fork truck one plows the distance of cargo based on lifting motor electric current and motor speed integrated value.Specifically:
Movable motor revolving speed two neighboring lifting motor current peak is integrated, to plow the distance of cargo to carrying fork truck one
Estimated.
It should be noted that fork truck working environment includes but is not limited to above-mentioned five listed kind situation, in practical applications,
Fork truck working environment can be any one or a few combination in above-mentioned five kinds of situations, can also increase according to the actual situation
The decision condition of fork truck working environment, depending on concrete foundation is actually needed, the present invention is it is not limited here.
3) fork truck power consumption
1. the electricity that fork truck uses；
2. fork truck power consumption: fork truck supply voltage being multiplied with feedback current vector sum, obtains fork truck power consumption.
4) fork truck utilization rate
1. daily/fork truck power-on time for monthly counting and motor working time.The online rate of fork truck and the utilization of capacity are made
For fork truck utilization review index.
2. the monthly statistical data of each fork truck utilization rate (day/moon).
5) fork truck abnormal conditions
1. various fault alarm classifiction statistics；
2. collision frequency, collision front and back operation data (include: driver, motor, temperature, acceleration, driver's operation note
Record etc.)；
3. overload recording, comprising: surcharge preloading duration and overload number；
4. abnormal road conditions record, comprising: super security standpoint traveling and road surface are jolted extremely.
6) health status of battery
1. the electric current of battery, voltage and charge value；
2. the analysis and assessment of battery health status.
7) driver operating condition
1. driver routine data counts, comprising: current maxima, voltage max, maximum temperature etc..
2. driver control performance evaluation, comprising: overshoot and response time etc..
8) motor operation situation
1. motor routine data, comprising: current maxima, voltage max and maximum temperature etc..
2. motor performance is evaluated, comprising: shaft vibration situation.
It should be strongly noted that the characteristic value extracted from history data in this step are as follows: fork truck load, fork truck
Working environment, fork truck power consumption, fork truck utilization rate, fork truck abnormal conditions, the health status of battery, driver operating condition and electricity
It is any several or whole in machine operating condition, wherein characteristic value includes but is not limited to listed eight kinds, can also be according to reality
Border needs to increase other characteristic values, and depending on concrete foundation is actually needed, the present invention is it is not limited here.
Step S103, it using each dimension index in fork truck equipment state description information as a feature vector, is based on
History data carries out part respectively to multiple feature vectors and quantifies marking, generates high-dimensional feature vector, whole as fork truck
Body health degree assesses vector；
Wherein, based on history data multiple feature vectors are carried out with the process that part quantifies marking respectively, is one
The rule of thumb process that data give a mark to feature vector, for example, a certain fork truck is constantly in the working condition of excess load, then
Feature vector fork truck working environment one can be chosen as 50 points；When fork truck is run under good operating condition, feature vector fork truck
Working environment one can be chosen as 90 points.And the health status of battery then can reversely give a mark, etc. according to its service life.
Step S104, percentage weighted sum is carried out to each component of high-dimensional feature vector, obtains a percentage,
Using percentage as fork truck holistic health degree assessed value, comprehensive assessment is carried out to fork truck equipment state.
Wherein, the weight of each component of high-dimensional feature vector can be with depend on the actual needs.
For example, selecting fork truck load, fork truck power consumption, fork truck utilization rate, the health status of fork truck abnormal conditions and battery
Five feature vectors carry out comprehensive assessment to fork truck equipment state, this five feature vectors are obtained by hundred-mark system in step s 103
Point, be respectively as follows: fork truck load for 80 points, fork truck power consumption be 90 points, fork truck utilization rate is 85 points, fork truck abnormal conditions be 95 points and
The health status of battery is 70 points；Then this five weight ratio is rule of thumb set as 3:3:2:1:1, then to five spies
Levy the carry out percentage weighted sum of vector, obtained percentage are as follows: 0.8*0.3+0.9*0.3+0.85*0.3+0.95*0.1+
0.7*0.1=0.93, therefore, fork truck holistic health degree assessed value are 0.93.
In summary, the invention discloses a kind of fork truck equipment state comprehensive appraisal procedure, the history run of fork truck is obtained
Data carry out pretreatment and characteristics extraction by the history data, fork truck equipment state description information are obtained, by fork truck
Each dimension index in equipment state description information as a feature vector, based on history data to multiple features to
Amount carries out part quantization marking respectively, generates high-dimensional feature vector, vector is assessed as fork truck holistic health degree, to high-dimensional
Each component of feature vector carries out percentage weighted sum, a percentage is obtained, using percentage as fork truck holistic health
Assessed value is spent, comprehensive assessment is carried out to fork truck equipment state.Since the present invention is when carrying out comprehensive assessment to fork truck equipment state,
Multiple dimension indexs are comprehensively considered, comprising: fork truck load, fork truck working environment, fork truck power consumption, fork truck utilization rate, fork truck are different
It is any several or whole in reason condition, the health status of battery, driver operating condition and motor operation situation, therefore,
The comprehensive assessment to fork truck operating status may be implemented, to not only solve the further device data analysis institute such as fork truck maintenance
The equipment state description information needed also contributes to analysis of the fork truck manufacturer realization to fork truck longtime running situation, in turn
And the maintenance and design of ancillary equipment, it is ensured that the normal use of fork truck extends fork truck service life, guarantees the work effect of fork truck
Rate.
Corresponding with above method embodiment, the invention also discloses a kind of fork truck equipment state comprehensives to assess device.
Referring to fig. 2, a kind of structural schematic diagram of fork truck equipment state comprehensive assessment device disclosed in one embodiment of the invention,
The device includes:
Acquiring unit 201, for obtaining the history data of fork truck；
Wherein, the history data of fork truck includes: to obtain lifting required for fork truck load using linear regression method
Motor feedback torque current, lifting motor speed, cell voltage, movable motor electric current and movable motor speed；
Elevating ram pressure sensor data, acceleration required for fork truck loads are calculated using mechanism model derivation
Sensing data, gyroscope sensor data；Motor speed etc..
Processing unit 202 obtains fork truck equipment for carrying out pretreatment and characteristics extraction to the history data
Context information；
Pretreatment is carried out to history data to refer mainly to: data cleansing being carried out to history data, specifically includes: deleting
Except extraneous data and repeated data that initial data is concentrated, smooth noise data screen out the data unrelated with theme is excavated, processing
Missing values and exceptional value etc..
It should be noted that in fork truck equipment state description information each state description mode or algorithm, not only can be with
Applied to the characteristics extraction to fork truck history data, can be used for proposing the characteristic value of fork truck current operating situation
It takes.
Wherein, the fork truck equipment state description information includes multiple dimension indexs, and the multiple dimension index includes: fork
Vehicle load, fork truck working environment, fork truck power consumption, fork truck utilization rate, fork truck abnormal conditions, the health status of battery, driver
It is any several or whole in operating condition and motor operation situation.
Characteristics extraction is that a kind of group measured value to a certain mode converts, with the prominent representative spy of the mode
A kind of method of sign.
Fork truck equipment state can be described in terms of eight in the present embodiment, in practical applications, can by this eight
A aspect is refined as multiple small characteristic values, by the way that these characteristic values are integrated the synthesis that can be achieved to fork truck equipment state
Analysis, so that the evaluation also more comprehensive and preparation to fork truck state.
In the following, be discussed in detail in terms of eight of fork truck equipment state description, it is specific as follows:
1) fork truck loads
Using linear regression method or mechanism can be used to fork truck history data about fork truck load estimation
Model inference is calculated.
Linear regression method is a kind of regression analysis using in mathematical statistics, to determine between two or more variable
A kind of statistical analysis technique of complementary quantitative relationship.
1. processing unit 202 obtains the process of fork truck load such as using linear regression method to fork truck history data
Under:
Fork truck load quality M is obtained using linear regression method to history data.
M=(h1,h2,h3,h4,h5)(x1,x2,x3,x4,x5)T
Wherein, h1To be lifted motor feedback torque current, h2To be lifted motor speed, h3For cell voltage, h4For walking electricity
Electromechanics stream, h5For movable motor speed, x1,x2,x3,x4,x5For Linear Regression Model Parameters.
Wherein, parameter x1,x2,x3,x4,x5Linear regression can be carried out by the history data to fork truck to obtain.
Specifically, the history data using least square method to the fork truck being input in linear regression model (LRM)
Identification is practised, parameter x is obtained1,x2,x3,x4,x5, it can load M is calculated according to linear regression model (LRM).
It should be noted that parameter identification is a kind of skill for combining theoretical model with experimental data and being used for predicting
Art determines one group of parameter value according to experimental data and the model of foundation, enable the numerical result being calculated by model most
Good fitting test data.
The present invention in Learning Identification used parameter identification apply-official formula it is as follows:
K (m+1)=P (m) x (m+1) [1+xT(m+1)P(m)x(m+1)]-1；
P (m)=(xT(m)x(m))-1；
P (m+1)=P (m)-K (m+1) xT(m+1)P(m)；
In formula, x is the input of fork truck system, the h in corresponding fork truck load quality M calculation formula1~h5Value, y is fork truck
The output of system, corresponding fork truck load quality M, m are recursion number,For estimates of parameters, K is gain matrix, and K (m+1) is
Gain matrix under the m+1 times recursion, P (m) are pilot process amount, and x (m+1) is the system input under the m+1 times recursion, xT
It (m+1) is the transposition of the system input under the m+1 times recursion, xTIt (m) is the transposition of the system input under the m times recursion, x
It (m) is the system input under the m times recursion, P (m+1) is pilot process amount,For the parameter under the m+1 times recursion
Estimated value,For the estimates of parameters under the m times recursion, y (m+1) is the system output under the m times recursion.
Wherein, after determining that the corresponding fork truck of fork truck operational data loads, self study can be carried out again and is realized to parameter
Correction, fork truck operational data includes: h1To be lifted motor feedback torque current, h2To be lifted motor speed, h3For cell voltage,
h4For movable motor electric current, h5For movable motor speed, fork truck load is fork truck load quality M.
2. processing unit 202 derives the process that fork truck load is calculated to fork truck history data using mechanism model
It is as follows:
Fork truck load computation model is obtained in conjunction with the history data of fork truck and the operation mechanism of fork truck, is based on the load
Computation model determine fork truck load, specifically: based on fork truck movement lifting power model (F=ma) and based on motor fortune
The walking kinetic model that capable electric current, torque and revolving speed is established, obtains fork truck mechanism model；Fork truck mechanism model is repaired
Correction Mechanism model is just being obtained, fork truck load computation model is being obtained based on Correction Mechanism model and history data, according to institute
It states fork truck load computation model and determines that fork truck loads.
Wherein, the electric current, torque and revolving speed of motor operation can directly be measured by corresponding sensor obtains.Fork truck
On elevating ram pressure sensor and acceleration transducer are installed, the acceleration transducer for measure forklift-walking acceleration
Degree.
Walking kinetic model are as follows:
Fq-fvV-fsign (v) M=Ma
Fq=τ R-fτ=kIq-fτ
In formula, M is vehicle body overall load, and a is forklift-walking acceleration, FqFor elevating ram pressure, fvIt is viscous for fork truck traveling
Property coefficient of friction, f is the coefficient of sliding friction, fτFor motor spin friction coefficient, τ is fork truck Motor torque, and R is fork truck wheel half
Diameter, IqFor electric current, k is model parameter, and v is the fork truck speed of service, and sign (v) is to be to one mathematical function calculating of v progress, tool
Body is to work as x > 0, sign (x)=1；Work as x=0, sign (x)=0；As x < 0, sign (x)=- 1.
Lifting power model are as follows:
F-mg-f=ma, wherein cylinder thrust is F=kIqOr F=kP
Fork truck load quality calculation formula, model parameter are obtained by identified parameters SimMan universal patient simulator f and model parameter k
K is known quantity.
History data based on fork truck mechanism model and fork truck obtains fork truck load computation model, is loaded using fork truck
Computation model estimates fork truck load maximum value within a preset period of time and load mean value, and will load maximum value and load it is flat
Loading condition of the mean value as fork truck in the preset time period.
2) fork truck working environment
The degree 1. fork truck running environment is jolted；
It jolts degree using acceleration up and down that the acceleration transducer being arranged on fork truck measures as fork truck running environment
Measurement.
2. running road gradient；
The tilt angle that the gyroscope measurement being arranged on fork truck is obtained is as the measurement of operation road gradient.
3. fork truck running stability；
Whether the fore-aft acceleration that the acceleration transducer being arranged on fork truck is measured smoothly spends as fork truck operation
Amount.
4. whether fork truck has reversing situation；
Motor keeps rotating forward during fork truck advances, and shows that truck driver is being carried if negative value occurs in motor speed
There is reversing behavior in the process, it means that fork truck working environment is more narrow, there is the place that cannot turn round.
5. carrying fork truck one plows the distance of cargo；
Determine that carrying fork truck one plows the distance of cargo based on lifting motor electric current and motor speed integrated value.Specifically:
Movable motor revolving speed two neighboring lifting motor current peak is integrated, to plow the distance of cargo to carrying fork truck one
Estimated.
It should be noted that fork truck working environment includes but is not limited to above-mentioned five listed kind situation, in practical applications,
Fork truck working environment can be any one or a few combination in above-mentioned five kinds of situations, can also increase according to the actual situation
The decision condition of fork truck working environment, depending on concrete foundation is actually needed, the present invention is it is not limited here.
3) fork truck power consumption
1. the electricity that fork truck uses；
2. fork truck power consumption: fork truck supply voltage being multiplied with feedback current vector sum, obtains fork truck power consumption.
4) fork truck utilization rate
1. daily/fork truck power-on time for monthly counting and motor working time.The online rate of fork truck and the utilization of capacity are made
For fork truck utilization review index.
2. the monthly statistical data of each fork truck utilization rate (day/moon).
5) fork truck abnormal conditions
1. various fault alarm classifiction statistics；
2. collision frequency, collision front and back operation data (include: driver, motor, temperature, acceleration, driver's operation note
Record etc.)；
3. overload recording, comprising: surcharge preloading duration and overload number；
4. abnormal road conditions record, comprising: super security standpoint traveling and road surface are jolted extremely.
6) health status of battery
1. the electric current of battery, voltage and charge value；
2. the analysis and assessment of battery health status.
7) driver operating condition
1. driver routine data counts, comprising: current maxima, voltage max, maximum temperature etc..
2. driver control performance evaluation, comprising: overshoot and response time etc..
8) motor operation situation
1. motor routine data, comprising: current maxima, voltage max and maximum temperature etc..
2. motor performance is evaluated, comprising: shaft vibration situation.
It should be strongly noted that the characteristic value extracted from history data in the present embodiment are as follows: fork truck load, fork
Vehicle working environment, fork truck power consumption, fork truck utilization rate, fork truck abnormal conditions, the health status of battery, driver operating condition and
It is any several or whole in motor operation situation, wherein characteristic value includes but is not limited to listed eight kinds, can also basis
Actual needs increases other characteristic values, and depending on concrete foundation is actually needed, the present invention is it is not limited here.
High-dimensional feature vector generation unit 203, for by each dimension in the fork truck equipment state description information
Index carries out part to multiple described eigenvectors based on the history data respectively and quantifies to beat as a feature vector
Point, high-dimensional feature vector is generated, assesses vector as fork truck holistic health degree；
Wherein, based on history data multiple feature vectors are carried out with the process that part quantifies marking respectively, is one
The rule of thumb process that data give a mark to feature vector, for example, a certain fork truck is constantly in the working condition of excess load, then
Feature vector fork truck working environment one can be chosen as 50 points；When fork truck is run under good operating condition, feature vector fork truck
Working environment one can be chosen as 90 points.And the health status of battery then can reversely give a mark, etc. according to its service life.
State evaluation unit 204 carries out percentage weighted sum for each component to the high-dimensional feature vector,
A percentage is obtained, using the percentage as fork truck holistic health degree assessed value, synthesis is carried out to fork truck equipment state and is commented
Estimate.
Wherein, the weight of each component of high-dimensional feature vector can be with depend on the actual needs.
For example, selecting fork truck load, fork truck power consumption, fork truck utilization rate, the health status of fork truck abnormal conditions and battery
Five feature vectors carry out comprehensive assessment to fork truck equipment state, this five feature vectors are obtained by hundred-mark system in step s 103
Point, be respectively as follows: fork truck load for 80 points, fork truck power consumption be 90 points, fork truck utilization rate is 85 points, fork truck abnormal conditions be 95 points and
The health status of battery is 70 points；Then this five weight ratio is rule of thumb set as 3:3:2:1:1, then to five spies
Levy the carry out percentage weighted sum of vector, obtained percentage are as follows: 0.8*0.3+0.9*0.3+0.85*0.3+0.95*0.1+
0.7*0.1=0.93, therefore, fork truck holistic health degree assessed value are 0.93.
In summary, the invention discloses a kind of fork truck equipment state comprehensives to assess device, obtains the history run of fork truck
Data carry out pretreatment and characteristics extraction by the history data, fork truck equipment state description information are obtained, by fork truck
Each dimension index in equipment state description information as a feature vector, based on history data to multiple features to
Amount carries out part quantization marking respectively, generates high-dimensional feature vector, vector is assessed as fork truck holistic health degree, to high-dimensional
Each component of feature vector carries out percentage weighted sum, a percentage is obtained, using percentage as fork truck holistic health
Assessed value is spent, comprehensive assessment is carried out to fork truck equipment state.Since the present invention is when carrying out comprehensive assessment to fork truck equipment state,
Multiple dimension indexs are comprehensively considered, comprising: fork truck load, fork truck working environment, fork truck power consumption, fork truck utilization rate, fork truck are different
It is any several or whole in reason condition, the health status of battery, driver operating condition and motor operation situation, therefore,
The comprehensive assessment to fork truck operating status may be implemented, to not only solve the further device data analysis institute such as fork truck maintenance
The equipment state description information needed also contributes to analysis of the fork truck manufacturer realization to fork truck longtime running situation, in turn
And the maintenance and design of ancillary equipment, it is ensured that the normal use of fork truck extends fork truck service life, guarantees the work effect of fork truck
Rate.
Corresponding with the above system embodiment, the invention also discloses a kind of fork truck equipment state assessment systems.
Referring to Fig. 3, a kind of structural schematic diagram of fork truck equipment state assessment system disclosed in one embodiment of the invention, this is
System includes: fork truck operation data acquisition equipment 301, Cloud Server 302 and local server 303, wherein local server 303
Fork truck equipment state assessment device including embodiment illustrated in fig. 2, Cloud Server 302 acquire equipment with fork truck operation data respectively
301 and local server 303 connect；
Wherein,
Fork truck operation data acquires equipment 301 for being arranged in fork truck, acquires the operation data of fork truck, and by fork truck
Operation data is uploaded to Cloud Server 302, and in practical applications, fork truck operation data acquires equipment 301 can be by the fork truck of acquisition
Operation data Cloud Server 302 is passed to by wireless network WiFi/4G etc..
It includes but is not limited to oil-lifting jar pressure sensor and acceleration transducer that fork truck operation data, which acquires equipment 301,.
Cloud Server 302 is used to store the history data of fork truck, and can receive the number of the transmission of local server 303
According to acquisition instruction, the history data of fork truck is sent to local server 303.
The history data for the fork truck that local server 303 can will acquire is stored in HDFS distributed file system
In, and it is for statistical analysis to the history data of fork truck using MapReduce Computational frame, it obtains fork truck equipment state and retouches
State information.
It should be noted that history data of the local server 303 using the fork truck obtained, to fork truck equipment state
The process for carrying out comprehensive assessment can be found in above-mentioned corresponding embodiment, and details are not described herein again.
In summary, fork truck equipment state comprehensive assessment system disclosed by the invention, comprising: the acquisition of fork truck operation data is set
Standby 301, Cloud Server 302 and local server 303, fork truck operation data acquire the operation data that equipment 301 acquires fork truck, and
It is uploaded to Cloud Server 302, local server 303 obtains the history data of fork truck from Cloud Server 302, by this
History data carries out pretreatment and characteristics extraction, obtains fork truck equipment state description information, fork truck equipment state is retouched
Each dimension index in information is stated as a feature vector, multiple feature vectors are carried out respectively based on history data
Part quantization marking, generates high-dimensional feature vector, vector is assessed as fork truck holistic health degree, to high-dimensional feature vector
Each component carries out percentage weighted sum, obtains a percentage, right using percentage as fork truck holistic health degree assessed value
Fork truck equipment state carries out comprehensive assessment.Since the present invention is when carrying out comprehensive assessment to fork truck equipment state, comprehensively consider
Multiple dimension indexs, comprising: fork truck load, fork truck working environment, fork truck power consumption, fork truck utilization rate, fork truck abnormal conditions, electric power storage
It is any several or whole in the health status in pond, driver operating condition and motor operation situation, it is thereby achieved that fork
The comprehensive assessment of vehicle operating status, to not only solve the further required equipment state of device data analysis such as fork truck maintenance
Description information also contributes to fork truck manufacturer and realizes analysis to fork truck longtime running situation, and then and ancillary equipment
Maintenance and design, it is ensured that the normal use of fork truck extends fork truck service life, guarantees the working efficiency of fork truck.
Finally, it is to be noted that, herein, relational terms such as first and second and the like be used merely to by
One entity or operation are distinguished with another entity or operation, without necessarily requiring or implying these entities or operation
Between there are any actual relationship or orders.Moreover, the terms "include", "comprise" or its any other variant meaning
Covering non-exclusive inclusion, so that the process, method, article or equipment for including a series of elements not only includes that
A little elements, but also including other elements that are not explicitly listed, or further include for this process, method, article or
The intrinsic element of equipment.In the absence of more restrictions, the element limited by sentence "including a ...", is not arranged
Except there is also other identical elements in the process, method, article or apparatus that includes the element.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (15)
1. a kind of fork truck equipment state comprehensive appraisal procedure characterized by comprising
Obtain the history data of fork truck；
Pretreatment and characteristics extraction are carried out to the history data, obtain fork truck equipment state description information, wherein institute
Stating fork truck equipment state description information includes multiple dimension indexs, and the multiple dimension index includes: fork truck load, fork truck work
Environment, fork truck power consumption, fork truck utilization rate, fork truck abnormal conditions, the health status of battery, driver operating condition and motor fortune
It is any several or whole in market condition；
Using each dimension index in the fork truck equipment state description information as a feature vector, transported based on the history
Row data carry out part respectively to multiple described eigenvectors and quantify marking, generate high-dimensional feature vector, as fork truck entirety
Health degree assesses vector；
Percentage weighted sum is carried out to each component of the high-dimensional feature vector, a percentage is obtained, by described hundred
Score carries out comprehensive assessment as fork truck holistic health degree assessed value, to fork truck equipment state.
2. fork truck equipment state comprehensive appraisal procedure according to claim 1, which is characterized in that the fork truck load is pair
The history data is obtained using linear regression method.
3. fork truck equipment state comprehensive appraisal procedure according to claim 1, which is characterized in that the fork truck load is pair
The history data is derived by using mechanism model.
4. fork truck equipment state comprehensive appraisal procedure according to claim 3, which is characterized in that for the history run
Data are derived by the process that the fork truck loads using mechanism model
The walking that lifting power model based on fork truck movement and electric current, torque and revolving speed based on motor operation are established is dynamic
Mechanical model obtains fork truck mechanism model；
The fork truck mechanism model is modified to obtain Correction Mechanism model；
Fork truck load computation model is obtained based on the Correction Mechanism model and the history data；
Computation model, which is loaded, according to the fork truck determines that fork truck loads.
5. fork truck equipment state comprehensive appraisal procedure according to claim 1, which is characterized in that the fork truck working environment
Include: whether jolt degree, operation road gradient, fork truck running stability, fork truck of fork truck running environment has reversing situation and fork
Vehicle carries the distance of a cargo.
6. fork truck equipment state comprehensive appraisal procedure according to claim 1, which is characterized in that the fork truck abnormal conditions
It include: fault alarm classifiction statistics, collision frequency, collision front and back operation data, overload recording and abnormal road conditions record, institute
Stating overload recording includes: surcharge preloading duration and overload number；The exception road conditions record includes: that super security standpoint traveling and road surface are different
Often jolt.
7. a kind of fork truck equipment state comprehensive assesses device characterized by comprising
Acquiring unit, for obtaining the history data of fork truck；
Processing unit obtains fork truck equipment state and retouches for carrying out pretreatment and characteristics extraction to the history data
State information, wherein the fork truck equipment state description information includes multiple dimension indexs, and the multiple dimension index includes: fork
Vehicle load, fork truck working environment, fork truck power consumption, fork truck utilization rate, fork truck abnormal conditions, the health status of battery, driver
It is any several or whole in operating condition and motor operation situation；
High-dimensional feature vector generation unit, for using each dimension index in the fork truck equipment state description information as
One feature vector carries out part to multiple described eigenvectors based on the history data respectively and quantifies marking, generates
High-dimensional feature vector assesses vector as fork truck holistic health degree；
State evaluation unit carries out percentage weighted sum for each component to the high-dimensional feature vector, obtains one
A percentage carries out comprehensive assessment to fork truck equipment state using the percentage as fork truck holistic health degree assessed value.
8. fork truck equipment state comprehensive according to claim 7 assesses device, which is characterized in that the fork truck load is pair
The history data is obtained using linear regression method.
9. fork truck equipment state comprehensive according to claim 7 assesses device, which is characterized in that the fork truck load is pair
The history data is derived by using mechanism model.
10. fork truck equipment state comprehensive according to claim 9 assesses device, which is characterized in that the processing unit pair
The history data is specifically included using the process that mechanism model is derived by the fork truck load:
The walking that lifting power model based on fork truck movement and electric current, torque and revolving speed based on motor operation are established is dynamic
Mechanical model obtains fork truck mechanism model；
The fork truck mechanism model is modified to obtain Correction Mechanism model；
Fork truck load computation model is obtained based on the Correction Mechanism model and the history data；
Computation model, which is loaded, according to the fork truck determines that fork truck loads.
11. fork truck equipment state comprehensive according to claim 7 assesses device, which is characterized in that the fork truck building ring
Border include: fork truck running environment jolt degree, operation road gradient, fork truck running stability, fork truck whether have reversing situation and
Carrying fork truck one plows the distance of cargo.
12. fork truck equipment state comprehensive according to claim 7 assesses device, which is characterized in that the fork truck exception feelings
Condition includes: that fault alarm classifiction statistics, collision frequency, collision front and back operation data, overload recording and abnormal road conditions record,
The overload recording includes: surcharge preloading duration and overload number；The exception road conditions record includes: super security standpoint traveling and road surface
Exception is jolted.
13. a kind of fork truck equipment state assessment system characterized by comprising fork truck operation data acquires equipment, Cloud Server
And local server, the local server include that fork truck equipment state comprehensive described in claim 7~12 any one is commented
Estimate device；
Fork truck operation data acquisition equipment acquires the operation data of fork truck for being arranged in fork truck, and by the operation
Data are uploaded to the Cloud Server；
The Cloud Server is used to store the history data of fork truck, and can receive the data that the local server is sent
The history data of fork truck is sent to the local server by acquisition instruction.
14. fork truck equipment state assessment system according to claim 13, which is characterized in that the local server is used for
The history data for the fork truck that will acquire is stored in HDFS distributed file system, and uses MapReduce Computational frame
It is for statistical analysis to the history data of fork truck, obtain fork truck equipment state description information.
15. fork truck equipment state assessment system according to claim 13, which is characterized in that the fork truck operation data is adopted
Collecting equipment includes: oil-lifting jar pressure sensor and acceleration transducer.
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