CN109238735B - The malfunction monitoring diagnostic system of the electronic AGV of port cargo - Google Patents
The malfunction monitoring diagnostic system of the electronic AGV of port cargo Download PDFInfo
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- CN109238735B CN109238735B CN201810897308.9A CN201810897308A CN109238735B CN 109238735 B CN109238735 B CN 109238735B CN 201810897308 A CN201810897308 A CN 201810897308A CN 109238735 B CN109238735 B CN 109238735B
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- G—PHYSICS
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract
The present invention provides the malfunction monitoring diagnostic systems of the electronic AGV of port cargo a kind of, include AGV vehicle and server；The AGV vehicle operates on port and pier place；Sensor is installed on the AGV vehicle and/or port and pier place；Server generates malfunction monitoring diagnostic signal according to the monitoring signals of the sensor on AGV vehicle and/or port and pier place.The present invention realizes the online fault detection and fault diagnosis functions of the unmanned container carrier loader in harbour, greatly shortens the maintenance time of carrier vehicle, reduces maintenance cost, preferably meets the requirement of unmanned 24 hours high-efficient homeworks of harbour.
Description
Technical field
The present invention relates to the technical fields of automation harbour facilities health control, and in particular, to a kind of port cargo electricity
The malfunction monitoring diagnostic system of dynamic AGV.
Background technique
With the continuous continuous improvement increased with harbor automatic level of China's ports container quantity, the collection of unmanned guidance
Vanning transport trolley (hereinafter referred to as AGV) is more and more widely used in harbour.At harbour, AGV can be according to being
The path of system planning, using such as GPS, the guidance technologies such as electromagnetism carry container on harbour as desired.AGV makees
For one of the main carriers of container in automation harbour, ensure that its safe operation has extremely entire Port System
Importance and key.
Harbour container AGV has longevity of service, and the severe feature of working environment, in China, investment application time is short.
To its operation conditions carry out be monitored online and assessed by performance condition of the data to AGV related system, fault type into
Row judgement, has a very important role.
And the bearing in the rotation system of AGV, especially transmission system, its quality have pole for the operating status of AGV
Big influence.In order to ensure the normal operation of AGV transportation system in automation harbour, a kind of reliable and effective online bearing is researched and developed
Fault diagnosis and prediction system be it is extremely necessary, it can give warning in advance to equipment fault, facilitate maintenance personal to correlation
Equipment carries out high-efficient maintenance, and the high-efficiency operation at harbour is had a very important significance.The prior art is primarily present following three
Class defect:
(1) at present on the market without the online fault detection and diagnosis of any existing unmanned container carrier loader in harbour
System generally carries out offline inspection in turn to whole fleet according to maintenance plan, due to components longevity such as carrier loader itself bearings
Greatly, harbour working environment is complicated for life fluctuation, inspects periodically and not only needs to take a substantial amount of time and money, influences operation effect
Rate.Also it has no idea to realize the real time monitoring to failure, prepares maintenance components inventory on demand in advance, improve production efficiency.
(2) it is all based on laboratory standard greatly about parts reliability analysis and the related data and estimating method in service life at present
Under environment, and there are no a reliable models for each part life estimation under complex environment this for harbour, right at present
It is artificial neural network in the preferable method of part life estimation effect, training artificial neural network needs a large amount of training number
According to traditional off-line maintenance method has no idea to provide the data that trained artificial neural network needs.
(3) there are many existing syndrome check methods on the market at present and examine equipment, but its scope of application is mostly directed to
Fixed operating condition, under this complicated variable working condition environment in harbour, fault diagnosis effect is frequently not very ideal.And AGV conduct
The big machinery used in harbour complex situations, often doped with a large amount of ambient noise in vibration signal, to signal point
Analysis brings very big interference, and among these noises, a kind of signal is opened repeatedly in harbour operational process due to vehicle
It is dynamic to stop, i.e., bring linear FM signal LFM during acceleration-deceleration.
Summary of the invention
For the defects in the prior art, the object of the present invention is to provide the malfunction monitorings of the electronic AGV of port cargo a kind of
Diagnostic system.
The malfunction monitoring diagnostic system of the electronic AGV of the port cargo provided according to the present invention includes AGV vehicle and server；
The AGV vehicle operates on port and pier place；
Sensor is installed on the AGV vehicle and/or port and pier place；
Server generates malfunction monitoring and examines according to the monitoring signals of the sensor on AGV vehicle and/or port and pier place
Break signal.
Preferably, the AGV vehicle includes body modules, transmission module, motor module and battery module；
Below on any one more multiple position:
-- body modules；
-- transmission module；
-- motor module；
-- battery module；
-- port and pier place,
It is equipped with following any or appoints multiple sensors:
-- vibrating sensor；
-- humidity sensor；
-- temperature sensor；
-- voltage sensor；
-- current sensor.
Preferably, it is provided with the first signal processing module on AGV vehicle, is provided with second signal on the port and pier place
Processing module；
The server includes remote signal receiving module；
First signal processing module, second signal processing module respectively by AGV vehicle the monitoring signals of sensor, come
It is handled from the monitoring signals of sensor on port and pier place, obtains corresponding preprocessed data, and respectively will be corresponding
Preprocessed data is sent to remote signal receiving module.
Preferably, the server also includes remote failure monitoring, diagnosing module and data management module；
Remote failure monitoring, diagnosing module is according to the received preprocessed data of remote signal receiving module, and/or comes from number
According to the vehicle body operation history data of management module, malfunction monitoring diagnostic signal is generated.
Preferably, sensor is connected by bus with second signal processing module on AGV vehicle；
Sensor is connected by bus with second signal processing module on port and pier place；
First signal processing module and second signal processing module pass through wireless transmission form and remote signal receives mould
Block is communicated；
Remote signal receiving module is connected by wired forms with remote failure monitoring, diagnosing module；
The server also includes display module；
For the display module according to the malfunction monitoring diagnostic signal received, display is following any or appoints much information:
-- the fault message of AGV vehicle；
-- the health information of AGV vehicle；
-- the remaining life information of components on AGV vehicle.
Preferably, the remote failure monitoring, diagnosing module include it is following any one or appoint multiple modules:
Operational monitoring module: monitoring AGV vehicle overall operation situation；
Fault location and grade separation module: the position and severity that failure occurs on AGV vehicle are calculated；
Part life estimating module: the remaining life of components on AGV vehicle is estimated.
Preferably, fault location and grade separation module include bearing failure diagnosis module, the bearing failure diagnosis mould
Block includes with lower module:
Filter module: the original vibration signal for being included to preprocessed data is filtered, and obtains noise reduction vibration signal；
Reconstructed module: being reconstructed noise reduction vibration signal, obtains reconstruct vibration signal；
Diagnostic result obtains module: diagnosing to reconstruct vibration signal, obtains bearing failure diagnosis result.
Preferably, in the filter module, Fourier Transform of Fractional Order filtering is carried out to original vibration signal, is eliminated original
Linear frequency modulation noise in vibration signal；
The Fourier Transform of Fractional Order is realized by following formula:
In formula: fp(u) be noise reduction vibration signal, p be free variable fractional-order, and p ∈ (- 2,2], u be kernel function join
Number；
Kp(u, t) is Fourier transformation nuclear signal, and t is time-domain signal；
F (t) is original vibration signal；
AαFor pre-factor, α is rotation angle, and α ∈ (- 2 π, 2 π]；
J is the imaginary part of symbol；
N is integer；
δ () is Dirac function；
Sgn () is sign function.
Preferably, it in reconstructed module, is reconstructed using inverse fraction rank Fourier transform pairs noise reduction vibration signal.
Preferably, it includes with lower module that diagnostic result, which obtains module:
Module M1: the Hilbert transformation pair of reconstruct vibration signal is asked；
Module M2: to reconstruct vibration signal as real part, with Hilbert transformation to for imaginary part, analytic signal is constructed；
Module M3: envelope signal is obtained to analytic signal modulus；
Module M4: low-pass filtering is carried out to envelope signal and Fast Fourier Transform (FFT) finds out envelope spectrum, is obtained according to envelope spectrum
To modulating frequency, modulating frequency high order frequency harmonic wave and modulation function.
Compared with prior art, the present invention have it is following the utility model has the advantages that
1, the present invention realizes the online fault detection and fault diagnosis functions of the unmanned container carrier loader in harbour, greatly
The maintenance time for shortening carrier vehicle, reduce maintenance cost, preferably meet 24 hours high-efficient homeworks of unmanned harbour
It is required that.
2, the present invention can acquire relevant device data and environmental data in real time, provide number for the training of artificial neural network
According to equipment life is effectively estimated in realization, and effective management of whole fleet quality.
3, the present invention is filtered signal using Fourier Transform of Fractional Order, can preferably remove the back in vibration signal
Scape noise realizes efficient monitoring and diagnosis to failure by carrying out envelope spectrum analysis to filtered reconstruction signal.
Detailed description of the invention
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, other feature of the invention,
Objects and advantages will become more apparent upon:
Fig. 1 is the architecture diagram of the malfunction monitoring diagnostic system of the electronic AGV of port cargo provided by the invention；
Fig. 2 is bearing failure diagnosis flow chart；
Fig. 3 is Fourier Transform of Fractional Order schematic diagram, in figure: f0For the centre frequency of chirp class signal；
fmFor the modulation frequency of chirp class signal；
u0For projection value of the chirp class signal on fractional number order Fourier；
β is the angle of the time-frequency distributions of chirp class signal component and time shaft in measured signal；
Chirp class signal is linear frequency modulation class signal.
Specific embodiment
The present invention is described in detail combined with specific embodiments below.Following embodiment will be helpful to the technology of this field
Personnel further understand the present invention, but the invention is not limited in any way.It should be pointed out that the ordinary skill of this field
For personnel, without departing from the inventive concept of the premise, various modifications and improvements can be made.These belong to the present invention
Protection scope.
In the description of the present invention, it is to be understood that, term " on ", "lower", "front", "rear", "left", "right", " perpendicular
Directly ", the orientation or positional relationship of the instructions such as "horizontal", "top", "bottom", "inner", "outside" is orientation based on the figure or position
Relationship is set, is merely for convenience of description of the present invention and simplification of the description, rather than the device or element of indication or suggestion meaning are necessary
It with specific orientation, is constructed and operated in a specific orientation, therefore is not considered as limiting the invention.
As shown in Figure 1, the malfunction monitoring diagnostic system of the electronic AGV of port cargo provided by the invention, includes AGV vehicle and clothes
Business device；The AGV vehicle operates on port and pier place；Sensor is installed on the AGV vehicle and/or port and pier place；
Server generates malfunction monitoring diagnostic signal according to the monitoring signals of the sensor on AGV vehicle and/or port and pier place.
The AGV vehicle includes body modules, transmission module, motor module and battery module.Below on any one more multiple position:
Body modules, transmission module, motor module, battery module, port and pier place are equipped with following any or a variety of sensings
Device: vibrating sensor, humidity sensor, temperature sensor, voltage sensor, current sensor.
It is provided with the first signal processing module on AGV vehicle, second signal processing mould is provided on the port and pier place
Block；The server includes remote signal receiving module；First signal processing module, second signal processing module will come from respectively
Monitoring signals of sensor on AGV vehicle, the monitoring signals of sensor are handled on port and pier place, are obtained corresponding
Preprocessed data, and corresponding preprocessed data is sent to remote signal receiving module respectively.The server also includes remote
Journey malfunction monitoring diagnostic module and data management module；Remote failure monitoring, diagnosing module is received according to remote signal receiving module
Preprocessed data, and/or the vehicle body operation history data from data management module generates malfunction monitoring diagnostic signal.AGV
Sensor is connected by bus with second signal processing module on vehicle；Sensor passes through bus and the second letter on port and pier place
Number processing module is connected；First signal processing module and second signal processing module pass through wireless transmission form and remote signal
Receiving module is communicated；Remote signal receiving module is connected by wired forms with remote failure monitoring, diagnosing module.It is described
Server also includes display module；For the display module according to the malfunction monitoring diagnostic signal received, display is following any
Or appoint much information: the fault message of AGV vehicle；The health information of AGV vehicle；The remaining life information of components on AGV vehicle.
The remote failure monitoring, diagnosing module include it is following any one or appoint multiple modules: operational monitoring module: monitoring AGV vehicle is whole
Operating condition；Fault location and grade separation module: the position and severity that failure occurs on AGV vehicle are calculated；The components longevity
Life estimating module: the remaining life of components on AGV vehicle is estimated.
As shown in Fig. 2, fault location and grade separation module include bearing failure diagnosis module, the axis in embodiment
Holding fault diagnosis module includes with lower module: filter module: the original vibration signal for being included to preprocessed data is filtered,
Obtain noise reduction vibration signal；Reconstructed module: being reconstructed noise reduction vibration signal, obtains reconstruct vibration signal；Diagnostic result obtains
Modulus block: reconstruct vibration signal is diagnosed, bearing failure diagnosis result is obtained.
In the filter module, Fourier Transform of Fractional Order filtering is carried out to original vibration signal, eliminates original vibration letter
Linear frequency modulation noise in number, Fourier Transform of Fractional Order FRFT are a kind of unified video transformations, reflect signal in time domain
With the information on frequency domain, it indicates video information with unitary variant, not the interference of cross term: with conventional Fourier transform phase
Than since (it is more suitable for handling non-stationary signal transformation order p) more one free parameters, and since there are more mature
Fast discrete algorithm, FRFT can reasonable calculation amount limitation under obtain preferably analyzing result.As shown in figure 3, described
Fourier Transform of Fractional Order is realized by following formula:
In formula: fp(u) be noise reduction vibration signal, p be free variable fractional-order, and p ∈ (- 2,2], u be kernel function join
Number；Kp(u, t) is Fourier transformation nuclear signal, and t is time-domain signal；F (t) is original vibration signal；AαFor pre-factor, α is rotation
Gyration, and α ∈ (- 2 π, 2 π]；J is the imaginary part of symbol；N is integer；δ () is Dirac function；Sgn () is sign function.Kp
(u, t) is substantially the chirp signal that one group of frequency modulation rate is cot α, by change angle [alpha], can obtain different frequency modulation rates
Base.Once the frequency modulation rate of the chirp signal for needing to filter and certain group base is coincide, which also can form one on this group of base
A δ function, and since fractional fourier transform is a linear transformation, signal and the superimposed fractional order Fourier of noise become
Change be equal to respectively carry out fractional order transformation superposition, using this two o'clock can to signal in fractional number order Fourier into
Row filtering.Preferably, it in reconstructed module, is reconstructed using inverse fraction rank Fourier transform pairs noise reduction vibration signal.Actually answer
In, such as battery, motor, gearbox position can also be carried out by similar algorithm and specifically diagnosed.
Diagnostic result obtains module specific work steps and inspection principle is as follows, the rotating machineries failure such as rolling bearing
Generally there is periodically pulsing impact force, generate the modulation phenomenon of vibration signal, using the method for reconciling analysis, from signal
Extract modulation intelligence, analyze its intensity and the frequency it may determine that part injury degree and position.In embodiment, diagnostic result
Obtaining module includes with lower module: module M1: asking the Hilbert transformation pair of reconstruct vibration signal；Module M2: with reconstruct vibration letter
Number it is real part, with Hilbert transformation to for imaginary part, constructs analytic signal；Module M3: envelope letter is obtained to analytic signal modulus
Number；Module M4: low-pass filtering is carried out to envelope signal and Fast Fourier Transform (FFT) finds out envelope spectrum, is modulated according to envelope spectrum
Frequency, modulating frequency high order frequency harmonic wave and modulation function.
Preferred embodiment:
The malfunction monitoring diagnostic system of the electronic AGV of port cargo includes being mounted on the sensor of AGV vehicle body everywhere, is mounted on
Sensor on harbour, AGV cab signal preprocessing module, harbour place signal pre-processing module, remote signal receive center
With remote failure monitoring and diagnostic center.
Sensor on vehicle body is separately mounted to the frame module on AGV, transmission module, motor module and battery module.
Specifically sensor mainly includes vibrating sensor, temperature sensor, current sensor and voltage sensor.These sensors
It is respectively intended to the vibration data of acquisition vehicle body entirety, the vibration data and temperature number of machine driving module middle gear case, bearing etc.
According to, the voltage of driving motor, electric current, temperature and vibration data, the electric current of power battery pack, voltage and temperature data.
During normal vehicle operation, the collected data of sensor are sent to cab signal by bus and pre-process
Module, the pretreatment on basis, including amplification are carried out to signal, and filtering passes through pretreated data at harbour after debugging etc.
On the wireless network built, the remote signal transferred data on harbour receives center.
The meteorological acquisition unit on suitable location arrangements basis on harbour is disposed with temperature and humidity sensing on the unit
Device, for measuring the meteorological condition that AGV works on harbour.The signal of sensor passes through bus transfer to harbour place signal processing
Module, the module carry out the pretreatment on basis, including amplification to signal, and filtering passes through pretreated data after modulation etc.
The wireless network built on harbour, the remote signal transferred data on harbour receive center.
Remote signal receives center for after the signal received demodulation, sends a signal to remote failure monitoring and diagnosis
Center.The remote failure monitoring and diagnostic center receive simultaneously receives the data and come from port that center is sent from teledata
The data for the related AGV vehicle history run track that mouth AGV car transfer center is sent, analyze these data, in real time
Monitor the operating condition of AGV entirety on harbour, the health status of relevant device part, if break down and break down
Position.
The real-time monitoring of the operation vehicle health situation, it is characterized in that system is by the healthy shape of vehicle each in operational process
Condition real-time display submits administrative staff to handle or by transferring to it on the human-computer interaction interface of design, and to the vehicle to break down
He automatically processes program.The fault location and grade separation, it is characterized in that it can be pointed out that position that vehicle trouble occurs and tight
Weight degree.The estimation of the related components remaining life is predicted, it is characterized in that fault detection and diagnosis center is according to server
In historical data generate model, estimate the service life model of correlated parts, and pass through current sensor by intelligent algorithm
The collected spare parts logistics of device estimate the remaining life of correlated parts.
Specific embodiments of the present invention are described above.It is to be appreciated that the invention is not limited to above-mentioned
Particular implementation, those skilled in the art can make various deformations or amendments within the scope of the claims, this not shadow
Ring substantive content of the invention.In the absence of conflict, the feature in embodiments herein and embodiment can any phase
Mutually combination.
Claims (6)
1. a kind of malfunction monitoring diagnostic system of the electronic AGV of port cargo, which is characterized in that include AGV vehicle and server；It is described
AGV vehicle operates on port and pier place；
Sensor is installed on the AGV vehicle and/or port and pier place；
Server generates malfunction monitoring diagnosis letter according to the monitoring signals of the sensor on AGV vehicle and/or port and pier place
Number；
It is provided with the first signal processing module on AGV vehicle, is provided with second signal processing module on the port and pier place；
The server includes remote signal receiving module；
First signal processing module, second signal processing module respectively by AGV vehicle the monitoring signals of sensor, come from port
The monitoring signals of sensor are handled on mouth harbour place, obtain corresponding preprocessed data, and respectively by corresponding pre- place
Reason data are sent to remote signal receiving module；
The server also includes remote failure monitoring, diagnosing module and data management module；
Remote failure monitoring, diagnosing module is according to the received preprocessed data of remote signal receiving module, and/or comes from data pipe
The vehicle body operation history data of module is managed, malfunction monitoring diagnostic signal is generated；
Sensor is connected by bus with second signal processing module on AGV vehicle；
Sensor is connected by bus with second signal processing module on port and pier place；
First signal processing module and second signal processing module pass through wireless transmission form and remote signal receiving module into
Row communication；
Remote signal receiving module is connected by wired forms with remote failure monitoring, diagnosing module；
The server also includes display module；
For the display module according to the malfunction monitoring diagnostic signal received, display is following any or appoints much information:
-- the fault message of AGV vehicle；
-- the health information of AGV vehicle；
-- the remaining life information of components on AGV vehicle；
The remote failure monitoring, diagnosing module include it is following any one or appoint multiple modules:
Operational monitoring module: monitoring AGV vehicle overall operation situation；
Fault location and grade separation module: the position and severity that failure occurs on AGV vehicle are calculated；
Part life estimating module: the remaining life of components on AGV vehicle is estimated.
2. the malfunction monitoring diagnostic system of the electronic AGV of port cargo according to claim 1, which is characterized in that the AGV
Vehicle includes body modules, transmission module, motor module and battery module；
Below on any one more multiple position:
-- body modules；
-- transmission module；
-- motor module；
-- battery module；
-- port and pier place,
It is equipped with following any or appoints multiple sensors:
-- vibrating sensor；
-- humidity sensor；
-- temperature sensor；
-- voltage sensor；
-- current sensor.
3. the malfunction monitoring diagnostic system of the electronic AGV of port cargo according to claim 1, which is characterized in that failure is fixed
Position includes bearing failure diagnosis module with grade separation module, and the bearing failure diagnosis module includes with lower module:
Filter module: the original vibration signal for being included to preprocessed data is filtered, and obtains noise reduction vibration signal；
Reconstructed module: being reconstructed noise reduction vibration signal, obtains reconstruct vibration signal；
Diagnostic result obtains module: diagnosing to reconstruct vibration signal, obtains bearing failure diagnosis result.
4. the malfunction monitoring diagnostic system of the electronic AGV of port cargo according to claim 3, which is characterized in that the filter
In wave module, Fourier Transform of Fractional Order filtering is carried out to original vibration signal, eliminates the linear frequency modulation in original vibration signal
Noise；
The Fourier Transform of Fractional Order is realized by following formula:
In formula: fp(u) be noise reduction vibration signal, p be free variable fractional-order, and p ∈ (- 2,2], u is kernel functional parameter；
Kp(u, t) is Fourier transformation nuclear signal, and t is time-domain signal；
F (t) is original vibration signal；
AαFor pre-factor, α is rotation angle, and α ∈ (- 2 π, 2 π]；
J is the imaginary part of symbol；
N is integer；
δ () is Dirac function；
Sgn () is sign function.
5. the malfunction monitoring diagnostic system of the electronic AGV of port cargo according to claim 4, which is characterized in that reconstruct mould
In block, it is reconstructed using inverse fraction rank Fourier transform pairs noise reduction vibration signal.
6. the malfunction monitoring diagnostic system of the electronic AGV of port cargo according to claim 5, which is characterized in that diagnosis knot
It includes with lower module that fruit, which obtains module:
Module M1: the Hilbert transformation pair of reconstruct vibration signal is asked；
Module M2: to reconstruct vibration signal as real part, with Hilbert transformation to for imaginary part, analytic signal is constructed；
Module M3: envelope signal is obtained to analytic signal modulus；
Module M4: low-pass filtering is carried out to envelope signal and Fast Fourier Transform (FFT) finds out envelope spectrum, is adjusted according to envelope spectrum
Frequency, modulating frequency high order frequency harmonic wave and modulation function processed.
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| CN201810897308.9A CN109238735B (en) | 2018-08-08 | 2018-08-08 | The malfunction monitoring diagnostic system of the electronic AGV of port cargo |
| PCT/CN2019/094778 WO2020029727A1 (en) | 2018-08-08 | 2019-07-05 | Fault monitoring and diagnosis system for port freight electric agv |
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| CN109238735B (en) * | 2018-08-08 | 2019-10-08 | 上海交通大学 | The malfunction monitoring diagnostic system of the electronic AGV of port cargo |
| CN110012105A (en) * | 2019-04-12 | 2019-07-12 | 青岛港国际股份有限公司 | A kind of car-mounted terminal data decoding method and decoding apparatus |
| CN110186682B (en) * | 2019-07-08 | 2021-03-23 | 石家庄铁道大学 | Rolling bearing fault diagnosis method based on fractional order variation modal decomposition |
| CN110262362B (en) * | 2019-07-16 | 2021-01-12 | 上海快仓智能科技有限公司 | AGV working temperature monitoring method, system and device |
| CN112183290B (en) * | 2020-09-22 | 2023-02-24 | 北京邮电大学 | Mechanical fault diagnosis system based on SAsFFT algorithm |
| CN112578794B (en) * | 2020-12-12 | 2023-09-01 | 云南昆船智能装备有限公司 | AGV fault detection method, storage medium and system based on machine learning |
| CN113033663A (en) * | 2021-03-26 | 2021-06-25 | 同济大学 | Automatic container terminal equipment health prediction method based on machine learning |
| CN113607413A (en) * | 2021-08-26 | 2021-11-05 | 上海航数智能科技有限公司 | Bearing component fault monitoring and predicting method based on controllable temperature and humidity |
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| 基于FRFT滤波的轴承微弱故障特征提取;杨青乐 等;《军事交通学院学报》;20140731;第16卷(第7期);第48-52页 * |
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| Publication number | Publication date |
|---|---|
| WO2020029727A1 (en) | 2020-02-13 |
| CN109238735A (en) | 2019-01-18 |
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