CN109598348A

CN109598348A - A kind of image pattern obtains, model training method and system

Info

Publication number: CN109598348A
Application number: CN201710896985.4A
Authority: CN
Inventors: 马星辰
Original assignee: Beijing Orion Star Technology Co Ltd
Current assignee: Beijing Orion Star Technology Co Ltd
Priority date: 2017-09-28
Filing date: 2017-09-28
Publication date: 2019-04-09
Anticipated expiration: 2037-09-28
Also published as: CN109598348B

Abstract

The embodiment of the invention provides a kind of acquisition of image pattern, model training method and systems, image pattern acquisition methods are applied to the processing equipment communicated to connect with multiple mechanical arms, processing equipment includes multiple first dedicated cpus for reading image data and multiple second dedicated cpus for image real time transfer, camera is installed on each mechanical arm, this method comprises: obtaining the image data of each camera acquisition and storage in real time；Multiple first dedicated cpus recycle respectively executes reading process；Multiple second dedicated cpus carry out data processing according to preset data processing method, to the image data that multiple first dedicated cpus have been read, and obtain the corresponding image pattern of each image data.As it can be seen that processing equipment uses Stream Processing mode, image pattern can be obtained with real time processed images data, not need re -training machine learning model when obtaining new image data, greatly shorten the model training time.

Description

A kind of image pattern obtains, model training method and system

Technical field

The present invention relates to model training technical field, obtained more particularly to a kind of image pattern, model training method and System.

Background technique

Requirement with the continuous popularization of the mechanical arm crawl technology based on data-driven, for mechanical arm crawl precision It is higher and higher.The acquisition of image pattern is an important component in the mechanical arm crawl technology based on data-driven, only Have and obtain a large amount of image pattern, the training of these image patterns could be passed through and obtain the accurately engineering for mechanical arm crawl Practise model.

Currently, the acquisition modes of image pattern generally comprise: the camera collection image data by being installed on mechanical arm, According to the needs of machine learning model training, image data is handled, for example, color of image variation, matrixing etc. Reason, obtains image pattern.After obtaining great amount of images sample in the same way, it can train to obtain for mechanical arm crawl Machine learning model.

Using above-mentioned image pattern acquisition modes, after machine learning model is completed in training, if mechanical arm installation is taken the photograph As head collects new image data, then must be handled new image data to obtain corresponding image pattern, merge Into above-mentioned great amount of images sample, the training of machine learning model, very cost model training time are then re-started.

Summary of the invention

The embodiment of the present invention be designed to provide a kind of image pattern obtain, model training method and system, to shorten The model training time.Specific technical solution is as follows:

In a first aspect, being applied to logical with multiple mechanical arms the embodiment of the invention provides a kind of image pattern acquisition methods Believe the processing equipment of connection, the processing equipment includes multiple first dedicated cpus for reading image data and for image Multiple second dedicated cpus of data processing are equipped with camera on each mechanical arm, which comprises

The image data of each camera acquisition and storage are obtained in real time；

The multiple first dedicated cpu recycles respectively executes reading process, wherein the reading process are as follows: from being stored Image data in read the image data of the first pre-set image quantity；

The multiple second dedicated cpu has read the multiple first dedicated cpu according to preset data processing method Image data carry out data processing, obtain the corresponding image pattern of each image data, wherein the preset data processing Mode is preset according to the needs of machine learning model training.

Optionally, the multiple second dedicated cpu is divided into N group, wherein first group of CPU includes the of the first preset quantity Two dedicated cpus, N group CPU include the second dedicated cpu of N preset quantity；

The default processing mode includes: first group of CPU according to the first pre-set image processing mode, to the multiple first The image data that dedicated cpu has been read is handled, and corresponding first processing result of each image data is obtained, and N group CPU is obtained (N-1) processing result is taken, and acquired processing result is handled using N pre-set image processing mode, is obtained every The corresponding image pattern of a image data.

Optionally, first group of CPU is according to the first pre-set image processing mode, to the multiple first dedicated cpu The image data of reading is handled, and corresponding first processing result of each image data is obtained, and N group CPU obtains (N-1) Processing result, and acquired processing result is handled using N pre-set image processing mode, obtain each image data The step of corresponding image pattern, comprising:

First group of CPU is according to the first pre-set image processing mode, the picture number read to the multiple first dedicated cpu According to being handled, corresponding first processing result of each image data is obtained, first processing result is put into the first result Queue, N group CPU obtains (N-1) processing result from (N-1) result queue, and uses N pre-set image processing mode Acquired processing result is handled, the corresponding image pattern of each image data is obtained, described image sample is put into Final result queue.

Optionally, the default processing mode includes:

The multiple second dedicated cpu is preset processing mode using pre-set image and has been read the multiple first dedicated cpu The image data taken carries out data processing, obtains the corresponding image numerical example of each image data, wherein the pre-set image is pre- If processing mode includes: the first pre-set image processing mode, the second pre-set image processing mode to N pre-set image processing side Formula.

Optionally, described the step of obtaining the image data and storage that each camera acquires in real time, comprising:

The image data of each camera acquisition is obtained in real time；

Described image data are stored, and adjust the storage order of all image datas.

In a first aspect, being applied to and multiple mechanical arm communication links the embodiment of the invention provides a kind of model training method The processing equipment connect, the processing equipment include for reading multiple first dedicated cpus of image data, at image data Multiple second dedicated cpus and GPU of reason are equipped with camera on each mechanical arm, which comprises

The multiple second dedicated cpu has read the multiple first dedicated cpu according to preset data processing method Image data carry out data processing, obtain the corresponding image pattern of each image data, wherein the preset data processing Mode is preset according to the needs of machine learning model training；

The GPU obtains described image sample, is joined using acquired image pattern to the machine learning model Number training.

Optionally, the quantity of the GPU is multiple；

The GPU obtains described image sample, is joined using acquired image pattern to the machine learning model The step of number training, comprising:

Each GPU obtains the image pattern of default sample size, using acquired image pattern to the machine learning Model is trained, and the training result based on remaining GPU, adjusts the parameter of the machine learning model.

First group of CPU is according to the first pre-set image processing mode, the picture number read to the multiple first dedicated cpu According to being handled, corresponding first processing result of each image data is obtained, first processing result is put into the first result Queue, N group CPU obtains (N-1) processing result from (N-1) result queue, and uses N pre-set image processing mode Acquired processing result is handled, the corresponding image pattern of each image data is obtained, described image sample is put into Final result queue；

The GPU obtains described image sample from the final result queue；

Parameter training is carried out to the machine learning model using acquired image pattern.

Optionally, the default processing mode includes:

The image data of each camera acquisition is obtained in real time；

The third aspect, the embodiment of the invention provides a kind of image pattern obtain system, the system comprises with multiple machines The storage equipment and processing equipment of tool arm communication connection, the processing equipment include for reading multiple the first of image data specially With CPU and for multiple second dedicated cpus of image real time transfer, camera is installed on each mechanical arm, in which:

The storage equipment, for obtaining image data and the storage of each camera acquisition in real time；

The multiple first dedicated cpu is respectively used to circulation and executes reading process, wherein the reading process are as follows: from institute The image data of the first pre-set image quantity is read in the image data of storage；

The multiple second dedicated cpu is used for according to preset data processing method, to the multiple first dedicated cpu The image data read carries out data processing, obtains the corresponding image pattern of each image data, wherein the preset number It is preset according to the needs of machine learning model training according to processing mode.

First group of CPU, for having read the multiple first dedicated cpu according to the first pre-set image processing mode The image data taken is handled, and corresponding first processing result of each image data is obtained；

The N group CPU, for obtaining (N-1) processing result, and using N pre-set image processing mode to being obtained The processing result taken is handled, and the corresponding image pattern of each image data is obtained.

Optionally, first group of CPU is specifically used for according to the first pre-set image processing mode, dedicated to the multiple first The image data that CPU has been read is handled, and obtains corresponding first processing result of each image data, described first is handled As a result it is put into the first result queue；

N group CPU is specifically used for obtaining (N-1) processing result from (N-1) result queue, and default using N Image procossing mode handles acquired processing result, obtains the corresponding image pattern of each image data, will be described Image pattern is put into final result queue.

Optionally, the multiple second dedicated cpu, for presetting processing mode to the multiple first using pre-set image The image data that dedicated cpu has been read carries out data processing, obtains the corresponding image numerical example of each image data, wherein institute Stating pre-set image and presetting processing mode includes: that the first pre-set image processing mode, the second pre-set image processing mode are default to N Image procossing mode.

Optionally, the storage equipment, specifically for obtaining the image data of each camera acquisition in real time；Described in storage Image data, and adjust the storage order of all image datas.

Fourth aspect, the embodiment of the invention provides a kind of model training systems, the system comprises with multiple mechanical arms The storage equipment and processing equipment of communication connection, the processing equipment include dedicated for reading multiple the first of image data CPU, multiple second dedicated cpus and GPU for image real time transfer are equipped with camera on each mechanical arm, in which:

The multiple second dedicated cpu is used for according to preset data processing method, to the multiple first dedicated cpu The image data read carries out data processing, obtains the corresponding image pattern of each image data, wherein the preset number It is preset according to the needs of machine learning model training according to processing mode；

The GPU, for obtaining described image sample, using acquired image pattern to the machine learning model into Row parameter training.

Optionally, the quantity of the GPU is multiple；

The GPU obtains the image pattern of default sample size specifically for each GPU, utilizes acquired image sample This is trained the machine learning model, and the training result based on remaining GPU, adjusts the ginseng of the machine learning model Number.

First group of CPU, for having read the multiple first dedicated cpu according to the first pre-set image processing mode The image data taken is handled, and corresponding first processing result of each image data is obtained

N group CPU is specifically used for obtaining (N-1) processing result from (N-1) result queue, and default using N Image procossing mode handles acquired processing result, obtains the corresponding image pattern of each image data, will be described Image pattern is put into final result queue；

The GPU is specifically used for the GPU from the final result queue, obtains described image sample；Using being obtained The image pattern taken carries out parameter training to the machine learning model.

Optionally, the multiple second dedicated cpu is specifically used for presetting processing mode to the multiple using pre-set image The image data that first dedicated cpu has been read carries out data processing, obtains the corresponding image numerical example of each image data, In, it includes: the first pre-set image processing mode, the second pre-set image processing mode to that the pre-set image, which presets processing mode, N pre-set image processing mode.

The embodiment of the invention also provides a kind of processing equipment, including processor, communication interface, memory, communication bus, Multiple first dedicated cpus and multiple second dedicated cpus, wherein processor, communication interface, memory, multiple first dedicated cpus Mutual communication, the processing equipment and multiple mechanical arm communication links are completed by communication bus with multiple second dedicated cpus It connects, camera is installed on each mechanical arm；

Memory, for storing computer program；

Processor when for executing the program stored on memory, obtains the picture number of each camera acquisition in real time According to and store；

Multiple first dedicated cpus are respectively used to circulation and execute reading process, wherein the reading process are as follows: from being stored Image data in read the image data of the first pre-set image quantity；

Multiple second dedicated cpus, for having read the multiple first dedicated cpu according to preset data processing method The image data taken carries out data processing, obtains the corresponding image pattern of each image data, wherein at the preset data Reason mode is preset according to the needs of machine learning model training.

The embodiment of the invention also provides another processing equipments, including processor, communication interface, memory, communication are always Line, multiple first dedicated cpus, multiple second dedicated cpus and GPU, wherein processor, communication interface, memory, multiple first Dedicated cpu and multiple second dedicated cpus complete mutual communication, the processing equipment and multiple mechanical arms by communication bus It communicates to connect, camera is installed on each mechanical arm；

Memory, for storing computer program；

Processor when for executing the program stored on memory, obtains the image data of each camera acquisition simultaneously Storage；

Multiple second dedicated cpus, for having read the multiple first dedicated cpu according to preset data processing method The image data taken carries out data processing, obtains the corresponding image pattern of each image data, wherein at the preset data Reason mode is preset according to the needs of machine learning model training；

GPU joins the machine learning model using acquired image pattern for obtaining described image sample Number training.

The embodiment of the invention also provides a kind of computer readable storage medium, the computer readable storage medium memory Computer program is contained, the computer program realizes above-mentioned image pattern acquisition methods step when being executed by processor.

The embodiment of the invention also provides another computer readable storage medium, in the computer readable storage medium It is stored with computer program, the computer program realizes above-mentioned model training method step when being executed by processor.

In scheme provided in an embodiment of the present invention, processing equipment obtains the image data of each camera acquisition in real time and deposits Storage, multiple first dedicated cpus recycles execution reading process respectively, multiple second dedicated cpus according to preset data processing method, Data processing is carried out to the image data that multiple first dedicated cpus have been read, obtains the corresponding image pattern of each image data. As it can be seen that processing equipment uses Stream Processing mode, the image data of each camera acquisition can be handled in real time, obtains image sample This does not need re -training machine learning model when obtaining new image data, contracts significantly for machine learning model training The short model training time.

Detailed description of the invention

In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with It obtains other drawings based on these drawings.

Fig. 1 is a kind of flow chart of image pattern acquisition methods provided by the embodiment of the present invention；

Fig. 2 is a kind of schematic diagram that processing mode is preset in embodiment illustrated in fig. 1；

Fig. 3 is a kind of flow chart of model training method provided by the embodiment of the present invention；

Fig. 4 is a kind of structural schematic diagram of image pattern acquisition system provided by the embodiment of the present invention；

Fig. 5 is a kind of structural schematic diagram of model training systems provided by the embodiment of the present invention；

Fig. 6 is the structural schematic diagram of the first processing equipment provided by the embodiment of the present invention；

Fig. 7 is the structural schematic diagram of second of processing equipment provided by the embodiment of the present invention.

Specific embodiment

Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other Embodiment shall fall within the protection scope of the present invention.

In order to shorten the training time of machine learning model, the embodiment of the invention provides a kind of image pattern acquisition methods And system, model training method and system, processing equipment and computer readable storage medium.

A kind of image pattern acquisition methods are provided for the embodiments of the invention first below to be introduced.

A kind of image pattern acquisition methods provided by the embodiment of the present invention can be applied to and multiple mechanical arm communication links The processing equipment connect, the processing equipment may include multiple first dedicated cpu (Central for reading image data Processing Unit, central processing unit) and for image real time transfer multiple second dedicated cpus.Pacify on each mechanical arm Equipped with camera, to acquire image data.

As shown in Figure 1, a kind of image pattern acquisition methods, applied to the processing equipment communicated to connect with multiple mechanical arms, The processing equipment includes multiple first dedicated cpus for reading image data and for multiple the second of image real time transfer Dedicated cpu is equipped with camera on each mechanical arm, which comprises

S101 obtains the image data of each camera acquisition and storage in real time；

S102, the multiple first dedicated cpu recycles respectively executes reading process, wherein the reading process are as follows: from institute The image data of the first pre-set image quantity is read in the image data of storage；

S103, the multiple second dedicated cpu is according to preset data processing method, to the multiple first dedicated cpu The image data read carries out data processing, obtains the corresponding image pattern of each image data, wherein the preset number It is preset according to the needs of machine learning model training according to processing mode.

As it can be seen that processing equipment obtains the picture number of each camera acquisition in real time in scheme provided in an embodiment of the present invention According to and store, multiple first dedicated cpus recycle respectively execute reading process, multiple second dedicated cpus according to preset data at Reason mode carries out data processing to the image data that multiple first dedicated cpus have been read, obtains the corresponding figure of each image data Decent.Processing equipment uses Stream Processing mode, can handle the image data of each camera acquisition in real time, and acquisition is used for The image pattern of machine learning model training, does not need re -training machine learning model when obtaining new image data, greatly It is big to shorten the model training time.

In above-mentioned steps S101, processing equipment can obtain the image data of each camera acquisition in real time, and will figure As data are stored, for example, can store in hard disk.It is understood that the camera for being each installed on mechanical arm is equal In acquisition image data, what processing equipment stored is all image datas of camera acquisition.

In above-mentioned steps S102, multiple first dedicated cpus can recycle execution reading process respectively, wherein the reading Processing are as follows: the image data of the first pre-set image quantity is read from the image data stored.That is, each first is special The image data of the first pre-set image quantity is constantly read from the image data stored with CPU.

Multiple second dedicated cpus can then read multiple first dedicated cpus according to preset data processing method Image data carries out data processing, obtains the corresponding image pattern of each image data.Wherein, the preset data processing method Be according to machine learning model training needs it is preset, for example, may include conversion process, pixel of the image data to matrix The image procossings modes such as value processing, characteristic area calibration processing, are not specifically limited herein.

Wherein, above-mentioned first pre-set image quantity can be according to processing capacity and the image data institute of the first dedicated cpu The factors such as the memory accounted for determine, for example, can be 10,15,20 etc., be not specifically limited herein.In one embodiment, The image data for the first pre-set image quantity that one dedicated cpu is read from the image data stored, with farthest full The processing capacity of each second dedicated cpu of foot, utilizes the resource of each second dedicated cpu farthest.

For example, each second dedicated cpu can handle 5 image datas simultaneously, 10 the second dedicated cpus are shared, then The image data for the first pre-set image quantity that first dedicated cpu is read from the image data stored every time is at least 50 It is a, the processing capacity of each second dedicated cpu can be made full use of in this way, maximize resource utilization.

It should be noted that above-mentioned steps S101- step S103 is not necessarily according to the suitable of step S101- step S103 Sequence executes, but is performed simultaneously step S101- step S103, forms the mode of Stream Processing, as long as each camera acquisition figure As data, then processing equipment can obtain the image data of each camera acquisition and storage, at the same time, step in real time S102 and step S103 is also being carried out, and reaches the state of dynamic equilibrium, can handle the image of each camera acquisition in real time Data continuously obtain the image pattern for machine learning model training, and then can constantly training machine learn Model greatly shortens the model training time.

Data processing is promoted as a kind of embodiment of the embodiment of the present invention in order to further increase resource utilization Speed, above-mentioned multiple second dedicated cpus can be divided into N group, wherein first group of CPU includes the second dedicated of the first preset quantity CPU, N group CPU include the second dedicated cpu of N preset quantity.

Correspondingly, above-mentioned default processing mode may include: first group of CPU according to the first pre-set image processing mode, it is right The image data that the multiple first dedicated cpu has been read is handled, and the corresponding first processing knot of each image data is obtained Fruit, N group CPU obtain (N-1) processing result, and using N pre-set image processing mode to acquired processing result into Row processing, obtains the corresponding image pattern of each image data.

The processing mode as needed for image data may be a variety of different processing modes, so in order to meet quick standard Above-mentioned multiple second dedicated cpus can be divided into N group according to the processing mode needed for image data by process demand really, and one As in the case of, it is identical as the quantity of the type of required processing mode to the number of packet of the second dedicated cpu, that is to say, that this Each group of CPU of sample can carry out a kind of processing to image data.For example, processing mode needed for image data is 5 kinds, then just Above-mentioned multiple second dedicated cpus can be divided into 5 groups, each group of CPU carries out a kind of processing to image data.

For the quantity of the second dedicated cpu included by each group of CPU, that is, the first preset quantity, the second present count Amount ... N preset quantity, can according to the specific type of the first pre-set image processing mode to N pre-set image processing mode and The factors such as required memory are determined.If memory needed for certain pre-set image processing mode is larger, corresponding The quantity of two dedicated cpus can be more, corresponding if memory needed for certain pre-set image processing mode is smaller The quantity of second dedicated cpu can be less.

For example, it needs to include 4 kinds to the processing mode that image data carries out, i.e. the first pre-set image processing mode is extremely 4th pre-set image processing mode, memory needed for 4 kinds of processing modes successively increases, in order to guarantee that all second dedicated cpus exist Carry out data processing when resource utilization highest, according to needed for the processing capacity of the second dedicated cpu and every kind of processing mode in It deposits and is calculated, the quantitative proportion of the second dedicated cpu needed for four kinds of processing modes is 1:2:3:4, then, if second is dedicated The quantity of CPU is 80, then first group of CPU may include 8 the second dedicated cpus, second group of CPU may include 16 second Dedicated cpu, third group CPU may include 24 the second dedicated cpus, and the 4th group of CPU may include 36 the second dedicated cpus.

It should be noted that the processing that above-mentioned N group CPU carries out the processing result handled by previous group CPU It carrying out simultaneously, that is to say, that the image data that first group of CPU has constantly read multiple first dedicated cpus is handled, The first processing result is obtained, at the same time, second group of CPU to N group CPU is also in the processing knot handled from upper one group of CPU Processing result is obtained in fruit, and the processing result of acquisition is handled using pre-set image processing mode.

As it can be seen that multiple second dedicated cpus are divided into N group in the present embodiment, it is ensured that steadily carried out to image data Processing, the processing of image data can be carried out with highest processing speed by guaranteeing each second dedicated cpu, improve the utilization of resources Rate can also shorten the time of following model training.

As a kind of embodiment of the embodiment of the present invention, above-mentioned first group of CPU according to the first pre-set image processing mode, The image data read to the multiple first dedicated cpu is handled, and obtains corresponding first processing of each image data As a result, N group CPU obtains (N-1) processing result, and using N pre-set image processing mode to acquired processing result The step of being handled, obtaining each image data corresponding image pattern may include:

Since multiple second dedicated cpus are divided into N group, and data processing is carried out simultaneously, so in order to make process flow more Smoothly stablize, avoids the occurrence of chaotic and lead to handle mistake, as shown in Fig. 2, first group of CPU is according to the first pre-set image processing side Formula, the image data read to multiple first dedicated cpus are handled, and obtain the corresponding first processing knot of each image data After fruit, which can be put into the first result queue, in turn, second group of CPU can be from the first result queue The first processing result is obtained, and is handled according to first processing result of the second pre-set image processing mode to acquisition, according to this Analogize, N group CPU can then obtain (N-1) processing result from (N-1) result queue, and using at N pre-set image Reason mode handles acquired processing result, obtains the corresponding image pattern of each image data, then by these figures Decent is put into final result queue.It is subsequent to carry out machine learning model using the image pattern in final result queue Training.

As it can be seen that processing result can be put into corresponding processing result queue by every group of CPU in the present embodiment, next group CPU can obtain processing result from processing result queue and be handled, and process flow can be made more smoothly to stablize, avoided It causes confusion and causes to handle mistake.

As a kind of embodiment of the embodiment of the present invention, above-mentioned default processing mode may include:

In one embodiment, multiple second dedicated cpus can preset processing mode to multiple the using pre-set image The image data that one dedicated cpu has been read carries out data processing, obtains the corresponding image numerical example of each image data.

Wherein, it may include: the first pre-set image processing mode, at the second pre-set image that pre-set image, which presets processing mode, Reason mode is to N pre-set image processing mode.That is, what each second dedicated cpu can read the first dedicated cpu Image data carries out the default processing of N kind.For example, pre-set image processing mode shares 5 kinds, i.e. the first pre-set image processing mode is extremely 5th pre-set image processing mode, then after each second dedicated cpu obtains the image data that the first dedicated cpu has been read, just The image data can be handled according to the first pre-set image processing mode, then to obtained processing result according to second Pre-set image processing mode is handled, and so on, it is finally handled according to the 5th pre-set image processing mode, obtains figure Decent.

After obtaining above-mentioned image pattern, image pattern can be put into final result queue by the second dedicated cpu, so as to subsequent Used when machine learning model training.

As a kind of embodiment of the embodiment of the present invention, the above-mentioned image data for obtaining each camera acquisition in real time is simultaneously The step of storage may include:

The image data of each camera acquisition is obtained in real time；Described image data are stored, and adjust all image datas Storage order.

Processing equipment can obtain the image data of each camera acquisition in real time, then store acquired picture number According to, and adjust the storage order of all image datas.Specifically, since the quantity of mechanical arm is generally more, camera acquisition Image data quantity it is also just more, when the first dedicated cpu reads image data, be also stored in processing equipment and do not read The image data taken, while processing equipment is also constantly obtaining image data, in turn, the image that processing equipment is got in storage After data, the storage order of the image data and the stored image data not being read that can will acquire is adjusted.

In this way, the first dedicated cpu when reading image data, can read the picture number that processing equipment has just stored According to, can also read current time pre-processing device storage image data, in this way, if the camera shooting installed on mechanical arm The factors such as the acquisition environment of head change, and may include acquisition ring in the image pattern handled by the second dedicated cpu The corresponding image pattern of data acquired after the variation of the factors such as border, in turn, the mechanics obtained using the training of these image patterns The needs of Current mechanical arm use environment and situation can be met by practising model, play the role of new and old image pattern mixing, can To improve the accuracy for the rote learning model that training obtains.

In order to shorten the training time of machine learning model, the embodiment of the invention also provides a kind of model training methods.

A kind of model training method is provided for the embodiments of the invention below to be introduced.

A kind of model training method provided by the embodiment of the present invention can be applied to and multiple mechanical arms communication connections Processing equipment, the processing equipment may include for reading multiple first dedicated cpus of image data, for image real time transfer Multiple second dedicated cpus and GPU (Graphics Processing Unit, graphics processor).It is installed on each mechanical arm There is camera, to acquire image data.

As shown in figure 3, a kind of model training method, described applied to the processing equipment communicated to connect with multiple mechanical arms Processing equipment includes for reading multiple first dedicated cpus of image data, for the multiple second dedicated of image real time transfer CPU and GPU is equipped with camera on each mechanical arm, which comprises

S301 obtains the image data of each camera acquisition and storage in real time；

S302, the multiple first dedicated cpu recycles respectively executes reading process, wherein the reading process are as follows: from institute The image data of the first pre-set image quantity is read in the image data of storage；

S303, the multiple second dedicated cpu is according to preset data processing method, to the multiple first dedicated cpu The image data read carries out data processing, obtains the corresponding image pattern of each image data, wherein the preset number It is preset according to the needs of machine learning model training according to processing mode；

S304, the GPU obtain described image sample, using acquired image pattern to the machine learning model into Row parameter training.

As it can be seen that processing equipment obtains the picture number of each camera acquisition in real time in scheme provided in an embodiment of the present invention According to and store, multiple first dedicated cpus recycle respectively execute reading process, multiple second dedicated cpus according to preset data at Reason mode carries out data processing to the image data that multiple first dedicated cpus have been read, obtains the corresponding figure of each image data Decent, GPU obtains image pattern, carries out parameter training to machine learning model using acquired image pattern.Processing is set It is standby to use Stream Processing mode, the image data of each camera acquisition can be handled in real time, obtained and be used for machine learning model Trained image pattern does not need re -training machine learning model when obtaining new image data, greatly shortens model instruction Practice the time.

In above-mentioned steps S301, processing equipment can obtain the image data of each camera acquisition in real time, and will figure As data are stored, for example, can store in hard disk.It is understood that the camera for being each installed on mechanical arm is equal In acquisition image data, what processing equipment stored is all image datas of camera acquisition.

In above-mentioned steps S302, multiple first dedicated cpus can recycle execution reading process respectively, wherein the reading Processing are as follows: the image data of the first pre-set image quantity is read from the image data stored.That is, each first is special The image data of the first pre-set image quantity is constantly read from the image data stored with CPU.

In above-mentioned steps S304, the image pattern that available second dedicated cpu of GPU is handled, using acquired Image pattern parameter training is carried out to the machine learning model that constructs in advance, and then required machine learning mould can be obtained Type.

It should be noted that above-mentioned steps S301- step S304 is not necessarily according to the suitable of step S301- step S303 Sequence executes, but is performed simultaneously step S301- step S304, forms the mode of Stream Processing, as long as each camera acquisition figure As data, then processing equipment can obtain the image data of each camera acquisition and storage, at the same time, step in real time S302- step S304 is also being carried out, and reaches the state of dynamic equilibrium, can handle the picture number of each camera acquisition in real time According to, continuously image pattern of the acquisition for machine learning model training, and constantly training machine learning model, significantly Shorten the model training time.

As a kind of embodiment of the embodiment of the present invention, the quantity of above-mentioned GPU can be multiple；

Above-mentioned GPU obtains described image sample, is joined using acquired image pattern to the machine learning model Trained step is counted, may include:

Processing equipment may include multiple GPU, in this case, the figure of each available default sample size of GPU Decent, and machine learning model is trained using acquired image pattern.Wherein, default sample size can basis The factors such as the processing capacity of GPU and the data volume of each image pattern are determined, for example, can be 5,10,15 etc., This is not specifically limited.Certainly, the quantity for the image pattern that each GPU is obtained is most can make full use of the resource of GPU It is good, it can be further improved resource utilization in this way, shorten the model training time.

Due to above-mentioned multiple GPU simultaneously machine learning model is trained, so each GPU to machine learning model into When row training, the parameter of machine learning model can be adjusted based on the training result of remaining GPU.It can pass through between multiple GPU Communication bus and communication interface etc. are communicated, and to know the training result of remaining GPU, and adjust machine based on the training result The parameter of learning model.

For example, processing equipment includes 4 GPU, then 4 GPU obtain image pattern simultaneously, while the figure obtained is utilized The training of decent progress machine learning model, and communicate with each other, the training result of remaining GPU is obtained, in turn, adjusts engineering Practise the parameter of model.

As it can be seen that processing equipment includes multiple GPU in the present embodiment, multiple GPU simultaneously instruct machine learning model Practice, further increase model training efficiency, shortens the model training time.

Correspondingly, above-mentioned default processing mode includes: first group of CPU according to the first pre-set image processing mode, to described The image data that multiple first dedicated cpus have been read is handled, and corresponding first processing result of each image data is obtained, the N group CPU obtain (N-1) processing result, and using N pre-set image processing mode to acquired processing result at Reason, obtains the corresponding image pattern of each image data.

As it can be seen that multiple second dedicated cpus are divided into N group in the present embodiment, it is ensured that steadily carried out to image data Processing, the processing of image data can be carried out with highest processing speed by guaranteeing each second dedicated cpu, improve the utilization of resources Rate shortens the time of model training.

Since multiple second dedicated cpus are divided into N group, and data processing is carried out simultaneously, so in order to make process flow more Smoothly stablize, avoids the occurrence of chaotic and lead to handle mistake, first group of CPU is according to the first pre-set image processing mode, to multiple The image data that first dedicated cpu has been read is handled, can be with after obtaining corresponding first processing result of each image data First processing result is put into the first result queue, in turn, from second group of CPU can obtain first from the first result queue Reason as a result, and handled according to first processing result of the second pre-set image processing mode to acquisition, and so on, N group CPU can then obtain (N-1) processing result from (N-1) result queue, and using N pre-set image processing mode to institute The processing result of acquisition is handled, and is obtained the corresponding image pattern of each image data, is then put into these image patterns Final result queue.

Correspondingly, above-mentioned GPU obtains described image sample, using acquired image pattern to the machine learning model The step of progress parameter training, may include:

The GPU obtains described image sample from the final result queue；Using acquired image pattern to institute It states machine learning model and carries out parameter training.

N group CPU handled acquired processing result using N pre-set image processing mode, and will be obtained Each image pattern is put into final result queue, and in turn, GPU can obtain image pattern, in turn from the final result queue Parameter training is carried out to machine learning model using acquired image pattern.

As it can be seen that processing result can be put into corresponding processing result queue by every group of CPU in the present embodiment, next group CPU can obtain processing result from processing result queue and be handled, and GPU can obtain image from final result queue Sample carries out the parameter training of machine learning model, process flow can be made more smoothly to stablize, and avoids the occurrence of chaotic and causes Handle mistake.

As a kind of embodiment of the embodiment of the present invention, above-mentioned default processing mode includes:

After obtaining above-mentioned image pattern, image pattern can be put into final result queue by the second dedicated cpu, so as into When the training of row machine learning model, GPU can obtain image pattern from final result queue, join to machine learning model Number adjustment.

In this way, the first dedicated cpu when reading image data, can read the picture number that processing equipment has just stored According to the image data that processing equipment between current time stores can also be read, in this way, if the camera shooting installed on mechanical arm The factors such as the acquisition environment of head change, and may include acquisition ring in the image pattern handled by the second dedicated cpu The corresponding image pattern of data acquired after the variation of the factors such as border, in turn, the mechanics obtained using the training of these image patterns The needs of Current mechanical arm use environment and situation can be met by practising model, play the role of new and old image pattern mixing, can To improve the accuracy for the rote learning model that training obtains.

Corresponding to above-mentioned image pattern acquisition methods, the embodiment of the invention also provides a kind of image patterns to obtain system.

It it is provided for the embodiments of the invention a kind of image pattern below obtains system and be introduced.

As shown in figure 4, a kind of image pattern obtains system, the system comprises the storages communicated to connect with multiple mechanical arms Equipment 410 and processing equipment 420, the processing equipment 420 include multiple first dedicated cpus 4201 for reading image data With multiple second dedicated cpus 4202 for image real time transfer, camera is installed on each mechanical arm, in which:

The storage equipment 410, for obtaining image data and the storage of each camera acquisition in real time；

The multiple first dedicated cpu 4201 is respectively used to circulation and executes reading process, wherein the reading process are as follows: The image data of the first pre-set image quantity is read from the image data stored；

The multiple second dedicated cpu 4202 is used for according to preset data processing method, dedicated to the multiple first The image data that CPU has been read carries out data processing, obtains the corresponding image pattern of each image data, wherein described default Data processing method be according to machine learning model training needs it is preset.

As it can be seen that storage equipment obtains the picture number of each camera acquisition in real time in scheme provided in an embodiment of the present invention According to and store, multiple first dedicated cpus recycle respectively execute reading process, multiple second dedicated cpus according to preset data at Reason mode carries out data processing to the image data that multiple first dedicated cpus have been read, obtains the corresponding figure of each image data Decent.Processing equipment uses Stream Processing mode, can handle the image data of each camera acquisition in real time, and acquisition is used for The image pattern of machine learning model training, does not need re -training machine learning model when obtaining new image data, greatly It is big to shorten the model training time.

As a kind of embodiment of the embodiment of the present invention, above-mentioned multiple second dedicated cpus can be divided into N group, wherein the One group of CPU includes the second dedicated cpu of the first preset quantity, and N group CPU includes the second dedicated cpu of N preset quantity；

Above-mentioned first group of CPU, for having read the multiple first dedicated cpu according to the first pre-set image processing mode The image data taken is handled, and corresponding first processing result of each image data is obtained；

Above-mentioned N group CPU, for obtaining (N-1) processing result, and using N pre-set image processing mode to being obtained The processing result taken is handled, and the corresponding image pattern of each image data is obtained.

As a kind of embodiment of the embodiment of the present invention, above-mentioned first group of CPU is specifically used for according to the first pre-set image Processing mode, the image data read to the multiple first dedicated cpu are handled, and it is corresponding to obtain each image data First processing result is put into the first result queue by the first processing result；

As a kind of embodiment of the embodiment of the present invention, above-mentioned multiple second dedicated cpus, for pre- using pre-set image If processing mode carries out data processing to the image data that the multiple first dedicated cpu has been read, each image data is obtained Corresponding image numerical example, wherein it includes: the first pre-set image processing mode, second that the pre-set image, which presets processing mode, Pre-set image processing mode is to N pre-set image processing mode.

As a kind of embodiment of the embodiment of the present invention, above-mentioned storage equipment, specifically for obtaining each camera shooting in real time The image data of head acquisition；Described image data are stored, and adjust the storage order of all image datas.

Storage equipment can obtain the image data of each camera acquisition in real time, then store acquired image data, And adjust the storage order of all image datas.Specifically, since the quantity of mechanical arm is generally more, the figure of camera acquisition As the quantity of data is also more, when the first dedicated cpu reads image data, stores in equipment and be also stored with the figure not being read As data, while storing equipment and also constantly obtaining image data, in turn, the image data that storage equipment is got in storage Afterwards, the storage order of the image data that can be will acquire and the stored image data not being read is adjusted.

In this way, the first dedicated cpu when reading image data, can read the image that storage equipment has just stored, The image data that equipment storage is stored before current time can also be read, in this way, if the camera installed on mechanical arm Factors such as acquisition environment when changing, may include acquisition ring in the image pattern handled by the second dedicated cpu The corresponding image pattern of data acquired after the variation of the factors such as border, in turn, the mechanics obtained using the training of these image patterns The needs of Current mechanical arm use environment and situation can be met by practising model, play the role of new and old image pattern mixing, can To improve the accuracy for the rote learning model that training obtains.

Corresponding to above-mentioned model training method, the embodiment of the invention also provides a kind of model training systems.

A kind of model training systems are provided for the embodiments of the invention below to be introduced.

As shown in figure 5, a kind of model training systems, the system comprises the storage equipment communicated to connect with multiple mechanical arms 510 and processing equipment 520, the processing equipment 520 includes for reading multiple first dedicated cpus 5201 of image data, using In being equipped with camera on multiple second dedicated cpus 5202 and GPU5203 of image real time transfer, each mechanical arm, in which:

The storage equipment 510, for obtaining image data and the storage of each camera acquisition in real time；

The multiple first dedicated cpu 5201 is respectively used to circulation and executes reading process, wherein the reading process are as follows: The image data of the first pre-set image quantity is read from the image data stored；

The multiple second dedicated cpu 5202 is used for according to preset data processing method, dedicated to the multiple first The image data that CPU has been read carries out data processing, obtains the corresponding image pattern of each image data, wherein described default Data processing method be according to machine learning model training needs it is preset；

The GPU5203, for obtaining described image sample, using acquired image pattern to the machine learning mould Type carries out parameter training.

As it can be seen that storage equipment obtains the picture number of each camera acquisition in real time in scheme provided in an embodiment of the present invention According to and store, multiple first dedicated cpus recycle respectively execute reading process, multiple second dedicated cpus according to preset data at Reason mode carries out data processing to the image data that multiple first dedicated cpus have been read, obtains the corresponding figure of each image data Decent, GPU obtains image pattern, carries out parameter training to machine learning model using acquired image pattern.Processing is set It is standby to use Stream Processing mode, the image data of each camera acquisition can be handled in real time, obtained and be used for machine learning model Trained image pattern does not need re -training machine learning model when obtaining new image data, greatly shortens model instruction Practice the time.

As a kind of embodiment of the embodiment of the present invention, above-mentioned multiple second dedicated cpus are divided into N group, wherein first group CPU includes the second dedicated cpu of the first preset quantity, and N group CPU includes the second dedicated cpu of N preset quantity；

Above-mentioned first group of CPU, for having read the multiple first dedicated cpu according to the first pre-set image processing mode The image data taken is handled, and corresponding first processing result of each image data is obtained

As a kind of embodiment of the embodiment of the present invention, above-mentioned multiple second dedicated cpus are specifically used for using default figure Data processing is carried out to the image data that the multiple first dedicated cpu has been read as presetting processing mode, obtains each image The corresponding image numerical example of data, wherein the pre-set image preset processing mode include: the first pre-set image processing mode, Second pre-set image processing mode is to N pre-set image processing mode.

The embodiment of the invention also provides a kind of processing equipments, as shown in fig. 6, include processor 601, communication interface 602, Memory 603, communication bus 604, multiple first dedicated cpus 605 and multiple second dedicated cpus 606, wherein processor 601, Communication interface 602, memory 603, multiple first dedicated cpus 605 and multiple second dedicated cpus 606 are complete by communication bus 604 At mutual communication, the processing equipment and multiple mechanical arms are communicated to connect, and are equipped with camera on each mechanical arm；

Memory 603, for storing computer program；

Processor 601 when for executing the program stored on memory 603, obtains each camera acquisition in real time Image data simultaneously stores；

Multiple first dedicated cpus 605 are respectively used to circulation and execute reading process, wherein the reading process are as follows: from institute The image data of the first pre-set image quantity is read in the image data of storage；

Multiple second dedicated cpus 606 are used for according to preset data processing method, to the multiple first dedicated cpu The image data of reading carries out data processing, obtains the corresponding image pattern of each image data, wherein the preset data Processing mode is preset according to the needs of machine learning model training.

The communication bus that above-mentioned processing equipment is mentioned can be Peripheral Component Interconnect standard (Peripheral Component Interconnect, PCI) bus or expanding the industrial standard structure (Extended Industry Standard Architecture, EISA) bus etc..The communication bus can be divided into address bus, data/address bus, control bus etc..For just It is only indicated with a thick line in expression, figure, it is not intended that an only bus or a type of bus.

Communication interface is for the communication between above-mentioned processing equipment and other equipment.

Memory may include random access memory (Random Access Memory, RAM), also may include non-easy The property lost memory (Non-Volatile Memory, NVM), for example, at least a magnetic disk storage.Optionally, memory may be used also To be storage device that at least one is located remotely from aforementioned processor.

Above-mentioned processor can be general processor, including central processing unit (Central Processing Unit, CPU), network processing unit (Network Processor, NP) etc.；It can also be digital signal processor (Digital Signal Processing, DSP), it is specific integrated circuit (Application Specific Integrated Circuit, ASIC), existing It is field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete Door or transistor logic, discrete hardware components.

Wherein, above-mentioned multiple second dedicated cpus can be divided into N group, wherein first group of CPU includes the first preset quantity Second dedicated cpu, N group CPU include the second dedicated cpu of N preset quantity；

Wherein, above-mentioned first group of CPU has read the multiple first dedicated cpu according to the first pre-set image processing mode The image data taken is handled, and corresponding first processing result of each image data is obtained, and N group CPU is obtained at (N-1) Reason as a result, and acquired processing result is handled using N pre-set image processing mode, obtain each image data pair The step of image pattern answered, may include:

Wherein, above-mentioned default processing mode may include:

Wherein, above-mentioned the step of obtaining the image data and storage that each camera acquires in real time, comprising:

The image data of each camera acquisition is obtained in real time；

The embodiment of the invention also provides another processing equipments, as shown in fig. 7, comprises processor 701, communication interface 702, memory 703, communication bus 704, multiple first dedicated cpus 705, multiple second dedicated cpus 706 and GPU707, wherein Processor 701, communication interface 702, memory 703, multiple first dedicated cpus 705 and multiple second dedicated cpus 706 pass through logical Letter bus 704 completes mutual communication, and the processing equipment and multiple mechanical arms are communicated to connect, are equipped on each mechanical arm Camera；

Memory 703, for storing computer program；

Processor 701 when for executing the program stored on memory 703, obtains the image of each camera acquisition Data simultaneously store；

Multiple first dedicated cpus 705 are respectively used to circulation and execute reading process, wherein the reading process are as follows: from institute The image data of the first pre-set image quantity is read in the image data of storage；

Multiple second dedicated cpus 706 are used for according to preset data processing method, to the multiple first dedicated cpu The image data of reading carries out data processing, obtains the corresponding image pattern of each image data, wherein the preset data Processing mode is preset according to the needs of machine learning model training；

GPU707, for obtaining described image sample, using acquired image pattern to the machine learning model into Row parameter training.

Wherein, the quantity of above-mentioned GPU can be multiple；

The GPU obtains described image sample from the final result queue；

Wherein, above-mentioned default processing mode may include:

Wherein, above-mentioned the step of obtaining the image data and storage that each camera acquires in real time, may include:

The image data of each camera acquisition is obtained in real time；

The embodiment of the invention also provides a kind of computer readable storage medium, the computer readable storage medium memory Computer program is contained, the computer program performs the steps of when being executed by processor

As it can be seen that when computer program is executed by processor, being obtained in real time each in scheme provided by the embodiment of the present invention The image data and storage, multiple first dedicated cpus of camera acquisition recycle execution reading process respectively, and multiple second is dedicated CPU carries out data processing according to preset data processing method, to the image data that multiple first dedicated cpus have been read, and obtains The corresponding image pattern of each image data.Processing equipment uses Stream Processing mode, can handle each camera in real time and adopt The image data of collection obtains the image pattern for machine learning model training, and weight is not needed when obtaining new image data New training machine learning model, greatly shortens the model training time.

Wherein, above-mentioned default processing mode may include:

The image data of each camera acquisition is obtained in real time；

The embodiment of the invention also provides another computer readable storage medium, in the computer readable storage medium It is stored with computer program, the computer program performs the steps of when being executed by processor

As it can be seen that when computer program is executed by processor, being obtained in real time each in scheme provided by the embodiment of the present invention The image data and storage, multiple first dedicated cpus of camera acquisition recycle execution reading process respectively, and multiple second is dedicated CPU carries out data processing according to preset data processing method, to the image data that multiple first dedicated cpus have been read, and obtains The corresponding image pattern of each image data, GPU obtains image pattern, using acquired image pattern to machine learning model Carry out parameter training.Processing equipment uses Stream Processing mode, can handle the image data of each camera acquisition in real time, obtain It takes in the image pattern of machine learning model training, does not need re -training machine learning mould when obtaining new image data Type greatly shortens the model training time.

Wherein, the quantity of above-mentioned GPU can be multiple；

The GPU obtains described image sample from the final result queue；

Wherein, above-mentioned default processing mode may include:

The image data of each camera acquisition is obtained in real time；

It should be noted that for above system, processing equipment and computer readable storage medium embodiment, due to It is substantially similar to embodiment of the method, so being described relatively simple, related place is referring to the part explanation of embodiment of the method It can.

Need further exist for explanation, herein, relational terms such as first and second and the like be used merely to by One entity or operation are distinguished with another entity or operation, without necessarily requiring or implying these entities or operation Between there are any actual relationship or orders.Moreover, the terms "include", "comprise" or its any other variant meaning Covering non-exclusive inclusion, so that the process, method, article or equipment for including a series of elements not only includes that A little elements, but also including other elements that are not explicitly listed, or further include for this process, method, article or The intrinsic element of equipment.In the absence of more restrictions, the element limited by sentence "including a ...", is not arranged Except there is also other identical elements in the process, method, article or apparatus that includes the element.

Each embodiment in this specification is all made of relevant mode and describes, same and similar portion between each embodiment Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for system reality For applying example, since it is substantially similar to the method embodiment, so being described relatively simple, related place is referring to embodiment of the method Part explanation.

The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the scope of the present invention.It is all Any modification, equivalent replacement, improvement and so within the spirit and principles in the present invention, are all contained in protection scope of the present invention It is interior.

Claims

1. a kind of image pattern acquisition methods, which is characterized in that applied to the processing equipment communicated to connect with multiple mechanical arms, institute State processing equipment include multiple first dedicated cpus for reading image data and for image real time transfer multiple second specially With CPU, camera is installed on each mechanical arm, which comprises

The multiple first dedicated cpu recycles respectively executes reading process, wherein the reading process are as follows: from the figure stored Image data as reading the first pre-set image quantity in data；

The multiple second dedicated cpu is according to preset data processing method, the figure read to the multiple first dedicated cpu As data progress data processing, the corresponding image pattern of each image data is obtained, wherein the preset data processing method It is preset according to the needs of machine learning model training.

2. the method as described in claim 1, which is characterized in that the multiple second dedicated cpu is divided into N group, wherein first group CPU includes the second dedicated cpu of the first preset quantity, and N group CPU includes the second dedicated cpu of N preset quantity；

The default processing mode includes: first group of CPU according to the first pre-set image processing mode, dedicated to the multiple first The image data that CPU has been read is handled, and obtains corresponding first processing result of each image data, and N group CPU obtains the (N-1) processing result, and acquired processing result is handled using N pre-set image processing mode, obtain each figure As the corresponding image pattern of data.

3. method according to claim 2, which is characterized in that first group of CPU according to the first pre-set image processing mode, The image data read to the multiple first dedicated cpu is handled, and obtains corresponding first processing of each image data As a result, N group CPU obtains (N-1) processing result, and using N pre-set image processing mode to acquired processing result The step of being handled, obtaining each image data corresponding image pattern, comprising:

The image data that first group of CPU has been read according to the first pre-set image processing mode, to the multiple first dedicated cpu into Row processing, obtains corresponding first processing result of each image data, first processing result is put into the first result queue, N group CPU obtains (N-1) processing result from (N-1) result queue, and using N pre-set image processing mode to institute The processing result of acquisition is handled, and obtains the corresponding image pattern of each image data, described image sample is put into finally Result queue.

4. the method as described in claim 1, which is characterized in that the default processing mode includes:

The multiple second dedicated cpu presets what processing mode had read the multiple first dedicated cpu using pre-set image Image data carries out data processing, obtains the corresponding image numerical example of each image data, wherein the default place of the pre-set image Reason mode includes: the first pre-set image processing mode, the second pre-set image processing mode to N pre-set image processing mode.

5. method according to any of claims 1-4, which is characterized in that the figure for obtaining each camera acquisition in real time The step of as data and storing, comprising:

The image data of each camera acquisition is obtained in real time；

6. a kind of model training method, which is characterized in that applied to the processing equipment communicated to connect with multiple mechanical arms, the place Managing equipment includes for reading multiple first dedicated cpus of image data, for multiple second dedicated cpus of image real time transfer And GPU, camera is installed on each mechanical arm, which comprises

The multiple second dedicated cpu is according to preset data processing method, the figure read to the multiple first dedicated cpu As data progress data processing, the corresponding image pattern of each image data is obtained, wherein the preset data processing method It is preset according to the needs of machine learning model training；

The GPU obtains described image sample, carries out parameter instruction to the machine learning model using acquired image pattern Practice.

7. a kind of image pattern obtains system, which is characterized in that the system comprises the storages communicated to connect with multiple mechanical arms Equipment and processing equipment, the processing equipment include multiple first dedicated cpus for reading image data and for picture number According to multiple second dedicated cpus of processing, camera is installed on each mechanical arm, in which:

The multiple first dedicated cpu is respectively used to circulation and executes reading process, wherein the reading process are as follows: from being stored Image data in read the image data of the first pre-set image quantity；

The multiple second dedicated cpu, for having read the multiple first dedicated cpu according to preset data processing method The image data taken carries out data processing, obtains the corresponding image pattern of each image data, wherein at the preset data Reason mode is preset according to the needs of machine learning model training.

8. a kind of model training systems, which is characterized in that the system comprises the storage equipment communicated to connect with multiple mechanical arms And processing equipment, the processing equipment include for reading multiple first dedicated cpus of image data, for image real time transfer Multiple second dedicated cpus and GPU, camera is installed on each mechanical arm, in which:

The multiple second dedicated cpu, for having read the multiple first dedicated cpu according to preset data processing method The image data taken carries out data processing, obtains the corresponding image pattern of each image data, wherein at the preset data Reason mode is preset according to the needs of machine learning model training；

The GPU joins the machine learning model using acquired image pattern for obtaining described image sample Number training.

9. a kind of processing equipment, which is characterized in that specially including processor, communication interface, memory, communication bus, multiple first With CPU and multiple second dedicated cpus, wherein processor, communication interface, memory, multiple first dedicated cpus and multiple second Dedicated cpu completes mutual communication by communication bus, and the processing equipment and multiple mechanical arms communicate to connect, each machinery Camera is installed on arm；

Memory, for storing computer program；

Processor when for executing the program stored on memory, obtains the image data of each camera acquisition simultaneously in real time Storage；

Multiple first dedicated cpus are respectively used to circulation and execute reading process, wherein the reading process are as follows: from the figure stored Image data as reading the first pre-set image quantity in data；

Multiple second dedicated cpus, for having been read to the multiple first dedicated cpu according to preset data processing method Image data carries out data processing, obtains the corresponding image pattern of each image data, wherein the preset data processing side Formula is preset according to the needs of machine learning model training.

10. a kind of processing equipment, which is characterized in that specially including processor, communication interface, memory, communication bus, multiple first With CPU, multiple second dedicated cpus and GPU, wherein processor, communication interface, memory, multiple first dedicated cpus and multiple Second dedicated cpu completes mutual communication by communication bus, and the processing equipment and multiple mechanical arms communicate to connect, each Camera is installed on mechanical arm；

Memory, for storing computer program；

Processor when for executing the program stored on memory, obtains the image data of each camera acquisition and storage；

Multiple second dedicated cpus, for having been read to the multiple first dedicated cpu according to preset data processing method Image data carries out data processing, obtains the corresponding image pattern of each image data, wherein the preset data processing side Formula is preset according to the needs of machine learning model training；

GPU carries out parameter instruction to the machine learning model using acquired image pattern for obtaining described image sample Practice.