CN109035477B - Forklift equipment state comprehensive evaluation method, device and system - Google Patents

Forklift equipment state comprehensive evaluation method, device and system Download PDF

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CN109035477B
CN109035477B CN201810723420.0A CN201810723420A CN109035477B CN 109035477 B CN109035477 B CN 109035477B CN 201810723420 A CN201810723420 A CN 201810723420A CN 109035477 B CN109035477 B CN 109035477B
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forklift
operation data
load
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equipment
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CN109035477A (en
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李德文
曹军杰
费振华
张雪吟
陈梦迟
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Zhongkong Technology Co ltd
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Zhejiang Supcon Technology Co Ltd
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    • GPHYSICS
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    • G07CTIME 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
    • G07C5/00Registering or indicating the working of vehicles
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    • GPHYSICS
    • G07CHECKING-DEVICES
    • G07CTIME 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
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Abstract

The invention discloses a comprehensive evaluation method, a device and a system for forklift equipment state, which are used for acquiring historical operation data of a forklift, preprocessing and extracting characteristic values through the historical operation data to obtain forklift equipment state description information, taking each dimension index in the forklift equipment state description information as a characteristic vector, respectively carrying out local quantization scoring on a plurality of characteristic vectors based on the historical operation data to generate a high-dimension characteristic vector as a forklift overall health degree evaluation vector, carrying out percentage weighted summation on each component of the high-dimension characteristic vector to obtain a percentage, taking the percentage as a forklift overall health degree evaluation value, and carrying out comprehensive evaluation on the forklift equipment state. According to the invention, when the state of the forklift equipment is comprehensively evaluated, a plurality of dimensional indexes are comprehensively considered, so that the comprehensive evaluation of the running state of the forklift can be realized.

Description

Forklift equipment state comprehensive evaluation method, device and system
Technical Field
The invention relates to the technical field of industrial equipment, in particular to a comprehensive evaluation method, device and system for forklift equipment states.
Background
A forklift is an industrial handling vehicle, and generally refers to a wheeled handling vehicle for handling, stacking, and short-distance transportation of piece pallet goods. The forklift is used as common equipment in industrial production, the working reliability of the forklift is particularly important, so that the monitoring and evaluation of the equipment state of the forklift are helpful for ensuring the normal use of the equipment, the service life of the equipment is prolonged, and the working efficiency of the equipment is ensured.
However, at present, no complete solution for comprehensive evaluation of the state of the forklift equipment exists.
Disclosure of Invention
In view of this, the invention discloses a method, a device and a system for comprehensively evaluating the state of forklift equipment, so as to comprehensively evaluate the state of the forklift equipment.
A comprehensive evaluation method for forklift equipment states comprises the following steps:
acquiring historical operating data of the forklift;
preprocessing and extracting characteristic values of the historical operating data to obtain forklift equipment state description information, wherein the forklift equipment state description information comprises a plurality of dimensionality indexes, and the dimensionality indexes comprise: any or all of the forklift load, the forklift working environment, the forklift power consumption, the forklift utilization rate, the forklift abnormal condition, the health state of the storage battery, the driver running condition and the motor running condition;
taking each dimension index in the state description information of the forklift equipment as a feature vector, respectively carrying out local quantitative scoring on a plurality of feature vectors based on the historical operating data, and generating high-dimensional feature vectors which are used as the evaluation vectors of the overall health degree of the forklift;
and carrying out percentage weighted summation on all components of the high-dimensional characteristic vector to obtain a percentage, and taking the percentage as the overall health evaluation value of the forklift to carry out comprehensive evaluation on the equipment state of the forklift.
Preferably, the forklift load is obtained by adopting a linear regression method for the historical operation data.
Preferably, the forklift load is obtained by deducing the historical operation data by adopting a mechanism model.
Preferably, the process of deriving the forklift load by using a mechanism model for the historical operating data includes:
obtaining a forklift mechanism model based on a lifting dynamic model of forklift movement and a walking dynamic model established based on current, torque and rotating speed of motor operation;
correcting the forklift mechanism model to obtain a corrected mechanism model;
obtaining a forklift load calculation model based on the correction mechanism model and the historical operation data;
and determining the forklift load according to the forklift load calculation model.
Preferably, the forklift working environment comprises: the running condition of the forklift is bumpy, the gradient of a running road surface, the running stability of the forklift, whether the forklift has a reversing condition or not and the distance for the forklift to carry one time of goods.
Preferably, the forklift abnormal condition includes: the method comprises the following steps of fault alarm classification statistical data, collision times, operation data before and after collision, overload records and abnormal road condition records, wherein the overload records comprise: overload time and number of times; the abnormal road condition record comprises the following steps: driving at an ultra-safe angle and abnormal road bump.
A forklift equipment state comprehensive evaluation device comprises:
the acquisition unit is used for acquiring historical operation data of the forklift;
the processing unit is used for preprocessing the historical operating data and extracting characteristic values to obtain forklift equipment state description information, wherein the forklift equipment state description information comprises a plurality of dimensional indexes, and the plurality of dimensional indexes comprise: any or all of the forklift load, the forklift working environment, the forklift power consumption, the forklift utilization rate, the forklift abnormal condition, the health state of the storage battery, the driver running condition and the motor running condition;
the high-dimensional feature vector generation unit is used for taking each dimension index in the forklift equipment state description information as a feature vector, respectively carrying out local quantitative scoring on the feature vectors based on the historical operating data, and generating a high-dimensional feature vector which is taken as a forklift overall health degree evaluation vector;
and the state evaluation unit is used for carrying out percentage weighted summation on each component of the high-dimensional characteristic vector to obtain a percentage, and taking the percentage as the overall health degree evaluation value of the forklift to carry out comprehensive evaluation on the equipment state of the forklift.
Preferably, the forklift load is obtained by adopting a linear regression method for the historical operation data.
Preferably, the forklift load is obtained by deducing the historical operation data by adopting a mechanism model.
Preferably, the process of deriving the forklift load by the processing unit through a mechanism model for the historical operating data specifically includes:
obtaining a forklift mechanism model based on a lifting dynamic model of forklift movement and a walking dynamic model established based on current, torque and rotating speed of motor operation;
correcting the forklift mechanism model to obtain a corrected mechanism model;
obtaining a forklift load calculation model based on the correction mechanism model and the historical operation data;
and determining the forklift load according to the forklift load calculation model.
Preferably, the forklift working environment comprises: the running condition of the forklift is bumpy, the gradient of a running road surface, the running stability of the forklift, whether the forklift has a reversing condition or not and the distance for the forklift to carry one time of goods.
Preferably, the forklift abnormal condition includes: the method comprises the following steps of fault alarm classification statistical data, collision times, operation data before and after collision, overload records and abnormal road condition records, wherein the overload records comprise: overload time and number of times; the abnormal road condition record comprises the following steps: driving at an ultra-safe angle and abnormal road bump.
A forklift device status assessment system, comprising: the system comprises forklift operation data acquisition equipment, a cloud server and a local server, wherein the local server comprises the forklift equipment state comprehensive evaluation device;
the forklift operation data acquisition equipment is arranged on a forklift, acquires operation data of the forklift and uploads the operation data to the cloud server;
the cloud server is used for storing historical operation data of the forklift, receiving a data acquisition instruction sent by the local server and sending the historical operation data of the forklift to the local server.
Preferably, the local server is configured to store the acquired historical operation data of the forklift in an HDFS distributed file system, and perform statistical analysis on the historical operation data of the forklift by using a MapReduce computing framework to obtain forklift device state description information.
Preferably, the forklift operation data acquisition device includes: a lift cylinder pressure sensor and an acceleration sensor.
According to the technical scheme, the invention discloses a comprehensive evaluation method, a comprehensive evaluation device and a comprehensive evaluation system for the state of forklift equipment, wherein historical operation data of a forklift is obtained, preprocessing and characteristic value extraction are carried out on the historical operation data to obtain the state description information of the forklift equipment, each dimension index in the state description information of the forklift equipment is used as a characteristic vector, a plurality of characteristic vectors are respectively subjected to local quantization scoring based on the historical operation data to generate a high-dimension characteristic vector, the high-dimension characteristic vector is used as an overall health evaluation vector of the forklift, percentage weighting summation is carried out on each component of the high-dimension characteristic vector to obtain a percentage, and the percentage is used as the overall health evaluation value of the forklift to carry out comprehensive evaluation on the state of the forklift equipment. In the invention, when the state of the forklift equipment is comprehensively evaluated, a plurality of dimensional indexes are comprehensively considered, including: fork truck load, fork truck operational environment, the fork truck consumption, the fork truck utilization ratio, the fork truck abnormal conditions, the health status of battery, arbitrary several kinds or whole in driver operational aspect and the motor operational aspect, therefore, can realize the comprehensive aassessment to fork truck operational state, thereby not only solved the required equipment state description information of further equipment data analysis such as fork truck maintenance, but also help fork truck manufacturer to realize the analysis to the fork truck long-term operation condition, and then auxiliary assembly's maintenance and design, ensure fork truck's normal use, prolong fork truck life, guarantee fork truck's work efficiency.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the disclosed drawings without creative efforts.
Fig. 1 is a flowchart of a comprehensive evaluation method for the state of forklift equipment, which is disclosed by the embodiment of the invention;
fig. 2 is a schematic structural diagram of a comprehensive evaluation device for the state of forklift equipment disclosed by the embodiment of the invention;
fig. 3 is a schematic structural diagram of a comprehensive evaluation system for the state of forklift equipment, which is disclosed by the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a comprehensive evaluation method, a device and a system for forklift equipment state, which are used for acquiring historical operation data of a forklift, preprocessing and characteristic value extraction are carried out on the historical operation data to obtain forklift equipment state description information, each dimension index in the forklift equipment state description information is used as a characteristic vector, a plurality of characteristic vectors are respectively subjected to local quantitative scoring based on the historical operation data to generate a high-dimension characteristic vector which is used as a forklift overall health degree evaluation vector, percentage weighted summation is carried out on each component of the high-dimension characteristic vector to obtain a percentage, and the percentage is used as a forklift overall health degree evaluation value to carry out comprehensive evaluation on the forklift equipment state. In the invention, when the state of the forklift equipment is comprehensively evaluated, a plurality of dimensional indexes are comprehensively considered, including: fork truck load, fork truck operational environment, the fork truck consumption, the fork truck utilization ratio, the fork truck abnormal conditions, the health status of battery, arbitrary several kinds or whole in driver operational aspect and the motor operational aspect, therefore, can realize the comprehensive aassessment to fork truck operational state, thereby not only solved the required equipment state description information of further equipment data analysis such as fork truck maintenance, but also help fork truck manufacturer to realize the analysis to the fork truck long-term operation condition, and then auxiliary assembly's maintenance and design, ensure fork truck's normal use, prolong fork truck life, guarantee fork truck's work efficiency.
Referring to fig. 1, a flow chart of a comprehensive evaluation method for a forklift device state disclosed in an embodiment of the present invention includes the steps of:
s101, acquiring historical operation data of the forklift;
wherein, the historical operating data of fork truck includes: obtaining a lifting motor feedback torque current, a lifting motor speed, a battery voltage, a walking motor current and a walking motor speed which are required by a forklift load by adopting a linear regression method;
the method comprises the following steps of adopting a mechanism model to deduce and calculate to obtain lifting oil cylinder pressure sensor data, acceleration sensor data and gyroscope sensor data required by a forklift load; motor speed, etc.
Step S102, preprocessing and characteristic value extracting are carried out on historical operation data to obtain state description information of the forklift equipment;
the preprocessing of the historical operating data mainly refers to the following steps: the method for cleaning the historical operation data specifically comprises the following steps: deleting irrelevant data and repeated data in the original data set, smoothing noise data, screening out data irrelevant to the mining subject, processing missing values and abnormal values and the like.
It should be noted that the manner or algorithm of each state description in the forklift device state description information may be applied to feature value extraction of the forklift historical operation data, and may also be applied to feature value extraction of the forklift current operation condition.
Wherein the forklift device state description information includes a plurality of dimensional indexes, the plurality of dimensional indexes including: the system comprises a plurality of optional components or all of the load of the forklift, the working environment of the forklift, the power consumption of the forklift, the utilization rate of the forklift, the abnormal condition of the forklift, the health state of a storage battery, the running condition of a driver and the running condition of a motor.
Feature value extraction is a method of transforming a set of measurements for a pattern to emphasize that the pattern has representative features.
In the embodiment, the states of the forklift equipment can be described from eight aspects, in practical application, the eight aspects can be refined into a plurality of small characteristic values, and the comprehensive analysis on the states of the forklift equipment can be realized by integrating the characteristic values, so that the evaluation on the states of the forklift is more comprehensive and ready.
In the following, eight aspects described in relation to the state of the forklift equipment are described in detail, in particular as follows:
1) forklift load
The estimation about the load of the forklift can be obtained by adopting a linear regression method or a mechanism model derivation calculation on the historical operation data of the forklift.
The linear regression method is a statistical analysis method that determines the interdependent quantitative relationship between two or more variables by using regression analysis in mathematical statistics.
The process of obtaining the forklift load by adopting a linear regression method for the historical operation data of the forklift is as follows:
and obtaining the load mass M of the forklift by adopting a linear regression method on the historical operation data.
M=(h1,h2,h3,h4,h5)(x1,x2,x3,x4,x5)T
Wherein h is1For the purpose of raising the motor feedback torque current, h2For raising motor speed, h3Is the battery voltage, h4For the current of the running motor, h5For the speed of the running motor, x1,x2,x3,x4,x5Are linear regression model parameters.
Wherein, the parameter x1,x2,x3,x4,x5The method can be obtained by performing linear regression on historical operation data of the forklift.
Specifically, the least square method is adopted to learn and identify the historical operation data of the forklift input into the linear regression model to obtain a parameter x1,x2,x3,x4,x5The load M can be calculated according to the linear regression model.
It should be noted that parameter identification is a technique that combines a theoretical model with experimental data for prediction, and determines a set of parameter values based on the experimental data and the established model so that the numerical results calculated by the model best fit the test data.
The parameter identification ground deduction formula adopted in the learning identification of the invention 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);
Figure BDA0001719011320000061
in the formula, x is the input of the forklift system and corresponds to h in the calculation formula of the forklift load mass M1~h5The value of (c) is the output of the forklift system, corresponding to the forklift load mass M, M is the recursion times,
Figure BDA0001719011320000071
for parameter estimation, K is the gain matrix, K (m +1) is the gain matrix at the (m +1) th recursion, P (m) is the intermediate process quantity, x (m +1) is the system input at the (m +1) th recursion, x isT(m +1) is the transpose of the system input in the (m +1) th recursion, xT(m) is the transpose of the system input at the mth iteration, x (m) is the system input at the mth iteration, P (m +1) is the intermediate process quantity,
Figure BDA0001719011320000072
is the estimated value of the parameter at the (m +1) th recursion,
Figure BDA0001719011320000073
is the parameter estimate at the mth iteration, and y (m +1) is the system output at the mth iteration.
Wherein, after confirming the fork truck load that fork truck working data corresponds, can carry out self-learning again and realize the correction to the parameter, fork truck working data includes: h is1For the purpose of raising the motor feedback torque current, h2For raising motor speed, h3Is the battery voltage, h4For the current of the running motor, h5The speed of the walking motor is the load of the forklift, namely the load mass M of the forklift.
Secondly, the process of deducing and calculating the forklift load by adopting a mechanism model for the historical operation data of the forklift is as follows:
obtaining a forklift load calculation model by combining historical operation data of the forklift and an operation mechanism of the forklift, and determining the forklift load based on the load calculation model, wherein the method specifically comprises the following steps: obtaining a forklift mechanism model based on a forklift movement lifting dynamic model (F ═ ma) and a walking dynamic model established based on the current, torque and rotating speed of motor operation; and correcting the forklift mechanism model to obtain a corrected mechanism model, obtaining a forklift load calculation model based on the corrected mechanism model and historical operation data, and determining the forklift load according to the forklift load calculation model.
The current, the torque and the rotating speed of the motor can be directly measured by corresponding sensors. The forklift is provided with a lifting oil cylinder pressure sensor and an acceleration sensor, and the acceleration sensor is used for measuring the walking acceleration of the forklift.
The walking dynamics model is as follows:
Fq-fvv-f·sign(v)·M=Ma
Fq=τR-fτ=kIq-fτ
Figure BDA0001719011320000074
wherein M is the total load of the vehicle body, a is the walking acceleration of the forklift, and FqFor lift cylinder pressure, fvThe coefficient of viscous friction during the running of the forklift, f is the coefficient of sliding friction, fτIs the coefficient of friction of the rotation of the motor, tau is the torque of the motor of the forklift, R is half of the wheel of the forkliftDiameter, IqFor current, k is a model parameter, v is the running speed of the forklift, sign (v) is the calculation of a mathematical function on v, and particularly when x is>0, sign (x) 1; when x is 0, sign (x) is 0; when x is<0,sign(x)=-1。
The lifting dynamic model is as follows:
F-mg-F-ma, wherein the thrust of the oil cylinder is F-kIqOr F ═ kP
And obtaining a forklift load mass calculation formula by identifying the parameter comprehensive friction coefficient f and the model parameter k, wherein the model parameter k is a known quantity.
The method comprises the steps of obtaining a forklift load calculation model based on a forklift mechanism model and historical operation data of the forklift, estimating a load maximum value and a load average value of the forklift within a preset time period by using the forklift load calculation model, and taking the load maximum value and the load average value as load conditions of the forklift within the preset time period.
2) Working environment of forklift
Firstly, the bumping degree of the operation environment of the forklift is controlled;
and taking the vertical acceleration measured by an acceleration sensor arranged on the forklift as the measurement of the bumping degree of the running environment of the forklift.
Secondly, operating the road surface gradient;
and taking the inclination angle measured by a gyroscope arranged on the forklift as the measurement of the gradient of the running road surface.
The forklift runs stably;
and taking the front and rear acceleration measured by an acceleration sensor arranged on the forklift as the measurement of whether the forklift runs stably or not.
Fourthly, whether the forklift has a backing condition or not;
the motor keeps corotation in the fork truck process of advancing, if the motor rotational speed negative value appears then indicate that the fork truck driver has the action of backing a car in handling, and this means that fork truck operational environment is comparatively narrow and small, exists the place that can not turn round.
Transporting the goods for one time by a forklift;
and determining the distance of the forklift for carrying one cargo based on the lifting motor current and the motor speed integral value. The method specifically comprises the following steps: and integrating the rotating speed of the traveling motor between the current peak values of the two adjacent lifting motors to estimate the distance of the forklift for carrying one time of goods.
It should be noted that the forklift working environment includes, but is not limited to, the five situations listed above, in practical applications, the forklift working environment may be any one or a combination of several of the five situations, and the determination condition of the forklift working environment may also be increased according to the practical situation, which is specifically determined according to the practical need, and the present invention is not limited herein.
3) Forklift power consumption
Firstly, the electric quantity used by the forklift;
secondly, the power consumption of the forklift: and multiplying the vector sum of the power supply voltage and the feedback current of the forklift to obtain the power consumption of the forklift.
4) Fork truck utilization ratio
Counting the power-on time of the forklift and the working time of a motor every day/month. Namely, the on-line rate and the operating rate of the forklift are used as the utilization rate evaluation indexes of the forklift.
And secondly, statistical data of the use rate (day/month) of each forklift every month.
5) Abnormal situation of forklift
Classifying and counting data of various fault alarms;
collision times and running data before and after collision (including drivers, motors, temperature, acceleration, driver operation records and the like);
③ recording overload, comprising: overload time and number of times;
fourthly, recording abnormal road conditions, including: driving at an ultra-safe angle and abnormal road bump.
6) State of health of storage battery
Current, voltage and electric quantity values of the battery;
and analyzing and evaluating the health state of the storage battery.
7) Driver behavior
Driver conventional data statistics, including: current maximum, voltage maximum, maximum temperature, etc.
And secondly, evaluating the control performance of the driver, which comprises the following steps: overshoot, response time, etc.
8) Operation conditions of the electric machine
The conventional data of the motor comprise: current maximum, voltage maximum, and maximum temperature, etc.
Secondly, evaluating the performance of the motor, comprising the following steps: the vibration of the rotating shaft.
It should be noted that, in this step, the feature values extracted from the historical operating data are: the characteristic values include but are not limited to eight listed characteristic values, and other characteristic values can be added according to actual needs, specifically according to the actual needs, and the invention is not limited herein.
Step S103, taking each dimension index in the forklift equipment state description information as a feature vector, respectively carrying out local quantitative scoring on a plurality of feature vectors based on historical operation data, and generating high-dimensional feature vectors which are used as forklift overall health degree evaluation vectors;
the process of respectively performing local quantitative scoring on a plurality of feature vectors based on historical operating data is a process of scoring the feature vectors according to empirical data, for example, if a certain forklift is always in an overload working state, one item of the working environment of the feature vector forklift can be rated as 50 points; when the forklift operates under good working conditions, one item of the working environment of the feature vector forklift can be rated as 90 points. The state of health of the battery can be scored inversely according to the age of the battery, and the like.
And S104, carrying out percentage weighted summation on all components of the high-dimensional characteristic vector to obtain a percentage, and carrying out comprehensive evaluation on the equipment state of the forklift by taking the percentage as the overall health evaluation value of the forklift.
Wherein, the weight of each component of the high-dimensional feature vector can be determined according to the actual requirement.
For example, five feature vectors of the forklift load, the forklift power consumption, the forklift utilization rate, the forklift abnormal condition and the health state of the storage battery are selected for comprehensive evaluation of the forklift equipment state, and the five feature vectors are scored according to the percentage in step S103 and respectively comprise: the load of the forklift is 80 minutes, the power consumption of the forklift is 90 minutes, the utilization rate of the forklift is 85 minutes, the abnormal condition of the forklift is 95 minutes, and the health state of the storage battery is 70 minutes; and then, setting the weighting ratio of the five terms to be 3:3:2:1:1 according to experience, and performing percentage weighted summation on the five eigenvectors to obtain the following percentages: 0.8 × 0.3+0.9 × 0.3+0.85 × 0.3+0.95 × 0.1+0.7 × 0.1 — 0.93, and therefore the evaluation value of the overall health of the forklift was 0.93.
In summary, the invention discloses a comprehensive evaluation method for forklift equipment state, which comprises the steps of obtaining historical operation data of a forklift, preprocessing and extracting characteristic values through the historical operation data to obtain forklift equipment state description information, taking each dimension index in the forklift equipment state description information as a characteristic vector, respectively carrying out local quantization scoring on a plurality of characteristic vectors based on the historical operation data to generate a high-dimension characteristic vector, taking the high-dimension characteristic vector as a forklift overall health evaluation vector, carrying out percentage weighted summation on each component of the high-dimension characteristic vector to obtain a percentage, taking the percentage as a forklift overall health evaluation value, and carrying out comprehensive evaluation on the forklift equipment state. In the invention, when the state of the forklift equipment is comprehensively evaluated, a plurality of dimensional indexes are comprehensively considered, including: fork truck load, fork truck operational environment, the fork truck consumption, the fork truck utilization ratio, the fork truck abnormal conditions, the health status of battery, arbitrary several kinds or whole in driver operational aspect and the motor operational aspect, therefore, can realize the comprehensive aassessment to fork truck operational state, thereby not only solved the required equipment state description information of further equipment data analysis such as fork truck maintenance, but also help fork truck manufacturer to realize the analysis to the fork truck long-term operation condition, and then auxiliary assembly's maintenance and design, ensure fork truck's normal use, prolong fork truck life, guarantee fork truck's work efficiency.
Corresponding to the embodiment of the method, the invention also discloses a comprehensive evaluation device for the state of the forklift equipment.
Referring to fig. 2, a schematic structural diagram of a comprehensive evaluation device for a state of a forklift device disclosed in an embodiment of the present invention includes:
an acquiring unit 201, configured to acquire historical operation data of a forklift;
wherein, the historical operating data of fork truck includes: obtaining a lifting motor feedback torque current, a lifting motor speed, a battery voltage, a walking motor current and a walking motor speed which are required by a forklift load by adopting a linear regression method;
the method comprises the following steps of adopting a mechanism model to deduce and calculate to obtain lifting oil cylinder pressure sensor data, acceleration sensor data and gyroscope sensor data required by a forklift load; motor speed, etc.
The processing unit 202 is used for preprocessing the historical operating data and extracting characteristic values to obtain state description information of the forklift equipment;
the preprocessing of the historical operating data mainly refers to the following steps: the method for cleaning the historical operation data specifically comprises the following steps: deleting irrelevant data and repeated data in the original data set, smoothing noise data, screening out data irrelevant to the mining subject, processing missing values and abnormal values and the like.
It should be noted that the manner or algorithm of each state description in the forklift device state description information may be applied to feature value extraction of the forklift historical operation data, and may also be applied to feature value extraction of the forklift current operation condition.
Wherein the forklift device state description information includes a plurality of dimensional indexes, the plurality of dimensional indexes including: the system comprises a plurality of optional components or all of the load of the forklift, the working environment of the forklift, the power consumption of the forklift, the utilization rate of the forklift, the abnormal condition of the forklift, the health state of a storage battery, the running condition of a driver and the running condition of a motor.
Feature value extraction is a method of transforming a set of measurements for a pattern to emphasize that the pattern has representative features.
In the embodiment, the states of the forklift equipment can be described from eight aspects, in practical application, the eight aspects can be refined into a plurality of small characteristic values, and the comprehensive analysis on the states of the forklift equipment can be realized by integrating the characteristic values, so that the evaluation on the states of the forklift is more comprehensive and ready.
In the following, eight aspects described in relation to the state of the forklift equipment are described in detail, in particular as follows:
1) forklift load
The estimation about the load of the forklift can be obtained by adopting a linear regression method or a mechanism model derivation calculation on the historical operation data of the forklift.
The linear regression method is a statistical analysis method that determines the interdependent quantitative relationship between two or more variables by using regression analysis in mathematical statistics.
The process of obtaining the forklift load by the processing unit 202 by using a linear regression method for the forklift historical operation data is as follows:
and obtaining the load mass M of the forklift by adopting a linear regression method on the historical operation data.
M=(h1,h2,h3,h4,h5)(x1,x2,x3,x4,x5)T
Wherein h is1For the purpose of raising the motor feedback torque current, h2For raising motor speed, h3Is the battery voltage, h4For the current of the running motor, h5For the speed of the running motor, x1,x2,x3,x4,x5Are linear regression model parameters.
Wherein, the parameter x1,x2,x3,x4,x5The method can be obtained by performing linear regression on historical operation data of the forklift.
Specifically, the least square method is adopted to learn and identify the historical operation data of the forklift input into the linear regression model to obtain a parameter x1,x2,x3,x4,x5The load M can be calculated according to the linear regression model.
It should be noted that parameter identification is a technique that combines a theoretical model with experimental data for prediction, and determines a set of parameter values based on the experimental data and the established model so that the numerical results calculated by the model best fit the test data.
The parameter identification ground deduction formula adopted in the learning identification of the invention 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);
Figure BDA0001719011320000121
in the formula, x is the input of the forklift system and corresponds to h in the calculation formula of the forklift load mass M1~h5The value of (c) is the output of the forklift system, corresponding to the forklift load mass M, M is the recursion times,
Figure BDA0001719011320000122
for parameter estimation, K is the gain matrix, K (m +1) is the gain matrix at the (m +1) th recursion, P (m) is the intermediate process quantity, x (m +1) is the system input at the (m +1) th recursion, x isT(m +1) is the transpose of the system input in the (m +1) th recursion, xT(m) is the transpose of the system input at the mth iteration, x (m) is the system input at the mth iteration, P (m +1) is the intermediate process quantity,
Figure BDA0001719011320000131
is the estimated value of the parameter at the (m +1) th recursion,
Figure BDA0001719011320000132
is the parameter estimate at the mth iteration, and y (m +1) is the system output at the mth iteration.
Wherein, after confirming the fork truck load that fork truck working data corresponds, can carry out self-learning again and realize the correction to the parameter, fork truck working data includes: h is1To lift electricityMachine feedback torque current, h2For raising motor speed, h3Is the battery voltage, h4For the current of the running motor, h5The speed of the walking motor is the load of the forklift, namely the load mass M of the forklift.
The process that the processing unit 202 deduces and calculates the forklift load by adopting a mechanism model for the forklift historical operation data is as follows:
obtaining a forklift load calculation model by combining historical operation data of the forklift and an operation mechanism of the forklift, and determining the forklift load based on the load calculation model, wherein the method specifically comprises the following steps: obtaining a forklift mechanism model based on a forklift movement lifting dynamic model (F ═ ma) and a walking dynamic model established based on the current, torque and rotating speed of motor operation; and correcting the forklift mechanism model to obtain a corrected mechanism model, obtaining a forklift load calculation model based on the corrected mechanism model and historical operation data, and determining the forklift load according to the forklift load calculation model.
The current, the torque and the rotating speed of the motor can be directly measured by corresponding sensors. The forklift is provided with a lifting oil cylinder pressure sensor and an acceleration sensor, and the acceleration sensor is used for measuring the walking acceleration of the forklift.
The walking dynamics model is as follows:
Fq-fvv-f·sign(v)·M=Ma
Fq=τR-fτ=kIq-fτ
Figure BDA0001719011320000133
wherein M is the total load of the vehicle body, a is the walking acceleration of the forklift, and FqFor lift cylinder pressure, fvThe coefficient of viscous friction during the running of the forklift, f is the coefficient of sliding friction, fτIs the coefficient of friction of the rotation of the motor, tau is the torque of the motor of the forklift, R is the radius of the wheels of the forklift, IqFor current, k is a model parameter, v is the running speed of the forklift, sign (v) is the calculation of a mathematical function on v, and particularly when x is>0,sign(x)1 is ═ 1; when x is 0, sign (x) is 0; when x is<0,sign(x)=-1。
The lifting dynamic model is as follows:
F-mg-F-ma, wherein the thrust of the oil cylinder is F-kIqOr F ═ kP
And obtaining a forklift load mass calculation formula by identifying the parameter comprehensive friction coefficient f and the model parameter k, wherein the model parameter k is a known quantity.
The method comprises the steps of obtaining a forklift load calculation model based on a forklift mechanism model and historical operation data of the forklift, estimating a load maximum value and a load average value of the forklift within a preset time period by using the forklift load calculation model, and taking the load maximum value and the load average value as load conditions of the forklift within the preset time period.
2) Working environment of forklift
Firstly, the bumping degree of the operation environment of the forklift is controlled;
and taking the vertical acceleration measured by an acceleration sensor arranged on the forklift as the measurement of the bumping degree of the running environment of the forklift.
Secondly, operating the road surface gradient;
and taking the inclination angle measured by a gyroscope arranged on the forklift as the measurement of the gradient of the running road surface.
The forklift runs stably;
and taking the front and rear acceleration measured by an acceleration sensor arranged on the forklift as the measurement of whether the forklift runs stably or not.
Fourthly, whether the forklift has a backing condition or not;
the motor keeps corotation in the fork truck process of advancing, if the motor rotational speed negative value appears then indicate that the fork truck driver has the action of backing a car in handling, and this means that fork truck operational environment is comparatively narrow and small, exists the place that can not turn round.
Transporting the goods for one time by a forklift;
and determining the distance of the forklift for carrying one cargo based on the lifting motor current and the motor speed integral value. The method specifically comprises the following steps: and integrating the rotating speed of the traveling motor between the current peak values of the two adjacent lifting motors to estimate the distance of the forklift for carrying one time of goods.
It should be noted that the forklift working environment includes, but is not limited to, the five situations listed above, in practical applications, the forklift working environment may be any one or a combination of several of the five situations, and the determination condition of the forklift working environment may also be increased according to the practical situation, which is specifically determined according to the practical need, and the present invention is not limited herein.
3) Forklift power consumption
Firstly, the electric quantity used by the forklift;
secondly, the power consumption of the forklift: and multiplying the vector sum of the power supply voltage and the feedback current of the forklift to obtain the power consumption of the forklift.
4) Fork truck utilization ratio
Counting the power-on time of the forklift and the working time of a motor every day/month. Namely, the on-line rate and the operating rate of the forklift are used as the utilization rate evaluation indexes of the forklift.
And secondly, statistical data of the use rate (day/month) of each forklift every month.
5) Abnormal situation of forklift
Classifying and counting data of various fault alarms;
collision times and running data before and after collision (including drivers, motors, temperature, acceleration, driver operation records and the like);
③ recording overload, comprising: overload time and number of times;
fourthly, recording abnormal road conditions, including: driving at an ultra-safe angle and abnormal road bump.
6) State of health of storage battery
Current, voltage and electric quantity values of the battery;
and analyzing and evaluating the health state of the storage battery.
7) Driver behavior
Driver conventional data statistics, including: current maximum, voltage maximum, maximum temperature, etc.
And secondly, evaluating the control performance of the driver, which comprises the following steps: overshoot, response time, etc.
8) Operation conditions of the electric machine
The conventional data of the motor comprise: current maximum, voltage maximum, and maximum temperature, etc.
Secondly, evaluating the performance of the motor, comprising the following steps: the vibration of the rotating shaft.
It should be particularly noted that, in the present embodiment, the feature values extracted from the historical operating data are: the characteristic values include but are not limited to eight listed characteristic values, and other characteristic values can be added according to actual needs, specifically according to the actual needs, and the invention is not limited herein.
A high-dimensional feature vector generation unit 203, configured to use each dimension index in the forklift device state description information as a feature vector, perform local quantization scoring on the plurality of feature vectors based on the historical operating data, and generate a high-dimensional feature vector, which is used as a forklift overall health assessment vector;
the process of respectively performing local quantitative scoring on a plurality of feature vectors based on historical operating data is a process of scoring the feature vectors according to empirical data, for example, if a certain forklift is always in an overload working state, one item of the working environment of the feature vector forklift can be rated as 50 points; when the forklift operates under good working conditions, one item of the working environment of the feature vector forklift can be rated as 90 points. The state of health of the battery can be scored inversely according to the age of the battery, and the like.
And the state evaluation unit 204 is configured to perform percentage weighted summation on each component of the high-dimensional feature vector to obtain a percentage, and perform comprehensive evaluation on the state of the forklift equipment by using the percentage as an evaluation value of the overall health degree of the forklift.
Wherein, the weight of each component of the high-dimensional feature vector can be determined according to the actual requirement.
For example, five feature vectors of the forklift load, the forklift power consumption, the forklift utilization rate, the forklift abnormal condition and the health state of the storage battery are selected for comprehensive evaluation of the forklift equipment state, and the five feature vectors are scored according to the percentage in step S103 and respectively comprise: the load of the forklift is 80 minutes, the power consumption of the forklift is 90 minutes, the utilization rate of the forklift is 85 minutes, the abnormal condition of the forklift is 95 minutes, and the health state of the storage battery is 70 minutes; and then, setting the weighting ratio of the five terms to be 3:3:2:1:1 according to experience, and performing percentage weighted summation on the five eigenvectors to obtain the following percentages: 0.8 × 0.3+0.9 × 0.3+0.85 × 0.3+0.95 × 0.1+0.7 × 0.1 — 0.93, and therefore the evaluation value of the overall health of the forklift was 0.93.
In summary, the invention discloses a comprehensive evaluation device for forklift equipment state, which is used for acquiring historical operation data of a forklift, preprocessing and characteristic value extraction are carried out on the historical operation data to obtain forklift equipment state description information, each dimension index in the forklift equipment state description information is used as a characteristic vector, local quantization scoring is respectively carried out on a plurality of characteristic vectors based on the historical operation data to generate a high-dimensional characteristic vector which is used as a forklift overall health degree evaluation vector, percentage weighting summation is carried out on each component of the high-dimensional characteristic vector to obtain a percentage, and the percentage is used as a forklift overall health degree evaluation value to carry out comprehensive evaluation on the forklift equipment state. In the invention, when the state of the forklift equipment is comprehensively evaluated, a plurality of dimensional indexes are comprehensively considered, including: fork truck load, fork truck operational environment, the fork truck consumption, the fork truck utilization ratio, the fork truck abnormal conditions, the health status of battery, arbitrary several kinds or whole in driver operational aspect and the motor operational aspect, therefore, can realize the comprehensive aassessment to fork truck operational state, thereby not only solved the required equipment state description information of further equipment data analysis such as fork truck maintenance, but also help fork truck manufacturer to realize the analysis to the fork truck long-term operation condition, and then auxiliary assembly's maintenance and design, ensure fork truck's normal use, prolong fork truck life, guarantee fork truck's work efficiency.
Corresponding to the embodiment of the system, the invention also discloses a forklift equipment state evaluation system.
Referring to fig. 3, a schematic structural diagram of a system for evaluating a state of a forklift device according to an embodiment of the present invention includes: the system comprises forklift operation data acquisition equipment 301, a cloud server 302 and a local server 303, wherein the local server 303 comprises the forklift equipment state evaluation device of the embodiment shown in fig. 2, and the cloud server 302 is respectively connected with the forklift operation data acquisition equipment 301 and the local server 303;
wherein,
the forklift operation data acquisition equipment 301 is used for being arranged on a forklift, acquiring operation data of the forklift and uploading the operation data of the forklift to the cloud server 302, and in practical application, the forklift operation data acquisition equipment 301 can transmit the acquired operation data of the forklift to the cloud server 302 through a wireless network WiFi/4G and the like.
The forklift operation data acquisition device 301 includes, but is not limited to, a lift cylinder pressure sensor and an acceleration sensor.
The cloud server 302 is configured to store historical operation data of the forklift, receive a data acquisition instruction sent by the local server 303, and send the historical operation data of the forklift to the local server 303.
The local server 303 may store the acquired historical operation data of the forklift in the HDFS distributed file system, and perform statistical analysis on the historical operation data of the forklift by using a MapReduce computing framework to obtain the state description information of the forklift device.
It should be noted that, the process of comprehensively evaluating the state of the forklift device by the local server 303 using the acquired historical operating data of the forklift may refer to the above corresponding embodiment, and details are not described here.
To sum up, the comprehensive evaluation system for the state of the forklift equipment disclosed by the invention comprises: the forklift operation data acquisition device 301, the cloud server 302 and the local server 303, the forklift operation data acquisition device 301 acquires the operation data of the forklift, and uploads it to the cloud server 302, the local server 303 obtains historical operating data of the forklift from the cloud server 302, preprocessing and extracting characteristic values through the historical operating data to obtain forklift equipment state description information, taking each dimension index in the forklift equipment state description information as a characteristic vector, respectively carrying out local quantitative scoring on a plurality of characteristic vectors based on the historical operating data to generate high-dimensional characteristic vectors as forklift overall health degree evaluation vectors, and carrying out percentage weighted summation on all components of the high-dimensional characteristic vector to obtain a percentage, and taking the percentage as the overall health evaluation value of the forklift to comprehensively evaluate the state of the forklift equipment. In the invention, when the state of the forklift equipment is comprehensively evaluated, a plurality of dimensional indexes are comprehensively considered, including: fork truck load, fork truck operational environment, the fork truck consumption, the fork truck utilization ratio, the fork truck abnormal conditions, the health status of battery, arbitrary several kinds or whole in driver operational aspect and the motor operational aspect, therefore, can realize the comprehensive aassessment to fork truck operational state, thereby not only solved the required equipment state description information of further equipment data analysis such as fork truck maintenance, but also help fork truck manufacturer to realize the analysis to the fork truck long-term operation condition, and then auxiliary assembly's maintenance and design, ensure fork truck's normal use, prolong fork truck life, guarantee fork truck's work efficiency.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (11)

1. A comprehensive evaluation method for the state of forklift equipment is characterized by comprising the following steps:
acquiring historical operation data of the forklift, wherein the historical operation data comprises: obtaining a lifting motor feedback torque current, a lifting motor speed, a battery voltage, a walking motor current and a walking motor speed which are required by a forklift load by adopting a linear regression method;
preprocessing and extracting characteristic values of the historical operating data to obtain forklift equipment state description information, wherein the forklift equipment state description information comprises a plurality of dimensionality indexes, and the dimensionality indexes comprise: any or all of the forklift load, the forklift working environment, the forklift power consumption, the forklift utilization rate, the forklift abnormal condition, the health state of the storage battery, the driver running condition and the motor running condition;
the forklift load is obtained by deducing the historical operation data by adopting a mechanism model, and the method comprises the following steps:
obtaining a forklift mechanism model based on a lifting dynamic model of forklift movement and a walking dynamic model established based on current, torque and rotating speed of motor operation;
correcting the forklift mechanism model to obtain a corrected mechanism model;
obtaining a forklift load calculation model based on the correction mechanism model and the historical operation data;
determining the forklift load according to the forklift load calculation model;
taking each dimension index in the state description information of the forklift equipment as a feature vector, respectively carrying out local quantitative scoring on a plurality of feature vectors based on the historical operating data, and generating high-dimensional feature vectors which are used as the evaluation vectors of the overall health degree of the forklift;
and carrying out percentage weighted summation on all components of the high-dimensional characteristic vector to obtain a percentage, and taking the percentage as the overall health evaluation value of the forklift to carry out comprehensive evaluation on the equipment state of the forklift.
2. The method for comprehensively evaluating the state of the forklift equipment according to claim 1, wherein the forklift load is obtained by a linear regression method on the historical operation data.
3. The forklift equipment state comprehensive evaluation method according to claim 1, wherein the forklift working environment comprises: the running condition of the forklift is bumpy, the gradient of a running road surface, the running stability of the forklift, whether the forklift has a reversing condition or not and the distance for the forklift to carry one time of goods.
4. The forklift equipment state comprehensive evaluation method according to claim 1, wherein the forklift abnormal condition includes: the method comprises the following steps of fault alarm classification statistical data, collision times, operation data before and after collision, overload records and abnormal road condition records, wherein the overload records comprise: overload time and number of times; the abnormal road condition record comprises the following steps: driving at an ultra-safe angle and abnormal road bump.
5. A forklift equipment state comprehensive evaluation device is characterized by comprising:
the device comprises an acquisition unit, a storage unit and a control unit, wherein the acquisition unit is used for acquiring historical operation data of the forklift, and the historical operation data comprises: obtaining a lifting motor feedback torque current, a lifting motor speed, a battery voltage, a walking motor current and a walking motor speed which are required by a forklift load by adopting a linear regression method;
the processing unit is used for preprocessing the historical operating data and extracting characteristic values to obtain forklift equipment state description information, wherein the forklift equipment state description information comprises a plurality of dimensional indexes, and the plurality of dimensional indexes comprise: any or all of the forklift load, the forklift working environment, the forklift power consumption, the forklift utilization rate, the forklift abnormal condition, the health state of the storage battery, the driver running condition and the motor running condition;
the forklift load is obtained by deducing the historical operation data by adopting a mechanism model, and the method comprises the following steps:
obtaining a forklift mechanism model based on a lifting dynamic model of forklift movement and a walking dynamic model established based on current, torque and rotating speed of motor operation;
correcting the forklift mechanism model to obtain a corrected mechanism model;
obtaining a forklift load calculation model based on the correction mechanism model and the historical operation data;
determining the forklift load according to the forklift load calculation model;
the high-dimensional feature vector generation unit is used for taking each dimension index in the forklift equipment state description information as a feature vector, respectively carrying out local quantitative scoring on the feature vectors based on the historical operating data, and generating a high-dimensional feature vector which is taken as a forklift overall health degree evaluation vector;
and the state evaluation unit is used for carrying out percentage weighted summation on each component of the high-dimensional characteristic vector to obtain a percentage, and taking the percentage as the overall health degree evaluation value of the forklift to carry out comprehensive evaluation on the equipment state of the forklift.
6. The device for comprehensively evaluating the state of forklift equipment according to claim 5, wherein the forklift load is obtained by a linear regression method on the historical operation data.
7. The forklift equipment state comprehensive evaluation device according to claim 5, wherein the forklift working environment includes: the running condition of the forklift is bumpy, the gradient of a running road surface, the running stability of the forklift, whether the forklift has a reversing condition or not and the distance for the forklift to carry one time of goods.
8. The forklift equipment state comprehensive evaluation device according to claim 5, wherein the forklift abnormal condition includes: the method comprises the following steps of fault alarm classification statistical data, collision times, operation data before and after collision, overload records and abnormal road condition records, wherein the overload records comprise: overload time and number of times; the abnormal road condition record comprises the following steps: driving at an ultra-safe angle and abnormal road bump.
9. A forklift device condition assessment system, comprising: the forklift operation data acquisition equipment, the cloud server and the local server, wherein the local server comprises the forklift equipment state comprehensive evaluation device as claimed in any one of claims 5-8;
the forklift operation data acquisition equipment is arranged on a forklift, acquires operation data of the forklift and uploads the operation data to the cloud server;
the cloud server is used for storing historical operation data of the forklift, receiving a data acquisition instruction sent by the local server and sending the historical operation data of the forklift to the local server.
10. The forklift equipment state evaluation system according to claim 9, wherein the local server is configured to store the acquired historical operation data of the forklift in an HDFS distributed file system, and perform statistical analysis on the historical operation data of the forklift by using a MapReduce computing framework to obtain forklift equipment state description information.
11. The forklift device status evaluation system according to claim 9, wherein the forklift operation data collecting device includes: a lift cylinder pressure sensor and an acceleration sensor.
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