TW202219750A - Machine learning model training method, electronic device, controller, and storage medium - Google Patents

Abstract

The invention provides a model training method, which is applied to a model training system. The model training system includes several electronic devices and controllers. The electronic device collects data and sets the data as a training sample data set for training an initial machine learning model, and obtains a prediction accuracy of the trained machine learning model and a weight of each neuron of the model. The electronic device generates an update weight according to the prediction accuracy rate and the weight of each neuron of the model and updates the weight of each neuron correspondingly to the update weight. The invention also provides an electronic device, a controller, and a storage medium. The invention reduces a cost of machine learning model training and improves the accuracy of the machine learning model.

Description

Machine learning model training method, electronic device, controller and storage medium

The invention relates to the technical field of machine learning model training, in particular to a collaborative model training method based on an edge computing device, an electronic device, a controller and a storage medium.

With the development of artificial intelligence technology, reasoning and judgment through machine learning models have been applied to various fields, such as image recognition, smart manufacturing, medical diagnosis, logistics and transportation, and so on. Machine learning models are obtained by collecting a large number of data samples for training, and training machine learning models requires strong computing power and strong data processing capabilities. Generally, the work of training machine learning models is achieved through cloud computing environments. However, cloud computing Not only expensive, but also requires extremely high power consumption and consumption of network resources. On the other hand, when training a machine learning model, the accuracy of the machine learning model may not reach an ideal level due to the limitation of the number of training samples.

The present invention provides a model training method, electronic device, controller and storage medium to solve the above problems.

A first aspect of the present application provides a model training method, which is applied to a model training system, where the model training system includes a plurality of electronic devices and at least one controller, and each of the electronic devices is deployed with the same initial machine learning model , the model training method is applied to each of the electronic devices. The model training method includes: collecting data for training the initial machine learning model as a training sample data set; dividing the training sample data set into a training set and a verification set according to a preset ratio, and using the training set Train the initial machine learning model to obtain the trained machine learning model; verify the trained machine learning model through the verification set, obtain the prediction accuracy of the trained machine learning model, and the trained machine learning model Learning the weights between the neurons in the model, and sending the prediction accuracy and weight to the controller, so that the controller sends the prediction accuracy rates sent by the multiple electronic devices to the multiple electronic devices. Determine the update weight between the weights of , and send the update weight to the plurality of electronic devices; obtain the update weight sent by the controller, and calculate the weight between the neurons in the trained machine learning model. The corresponding update is the update weight.

Preferably, the data used for training the initial machine learning model within a preset time period is collected as the training sample data set; or a preset amount of data used for training the initial machine learning model is collected as the training sample dataset.

Preferably, the method further includes: receiving a restoration instruction, and restoring the trained machine learning model to an initial machine learning model, wherein the restoration instruction is performed when the prediction accuracy corresponding to the update weight is lower than the Generated when the prediction accuracy of the initial machine learning model.

Preferably, the electronic device to which the model training method is applied is an edge computing device.

A second aspect of the present application provides a model training method, which is applied to a model training system, where the model training system includes a plurality of electronic devices and at least one controller, and each of the electronic devices is deployed with the same initial machine learning model, The model training method is applied in the controller, and the model training method includes: generating a control instruction and sending the control instruction to each of the electronic devices, wherein the control instruction is used to trigger each of the electronic devices. The electronic device collects the training sample data set for training the initial machine learning model, and trains the initial machine learning model according to the training sample data set, and obtains the prediction accuracy of the trained machine learning model and the relationship between each neuron. Receive the prediction accuracy of the trained machine learning model sent by the multiple electronic devices and the weight between each neuron, and according to the preset rules and the prediction accuracy sent by the multiple electronic devices, The update weight is determined from the weights sent by the plurality of electronic devices, and the update weight is sent to each of the electronic devices, so that each of the electronic devices will send each neuron in the trained machine learning model The weight correspondence between them is updated to the update weight.

Preferably, the determining the update weight from the weights sent by the plurality of electronic devices according to the preset rule and the prediction accuracy rates sent by the plurality of electronic devices includes: selecting the highest among the plurality of prediction accuracy rates , and take the weight corresponding to the one with the highest prediction accuracy as the update weight.

Preferably, before sending the updated weight to each of the electronic devices, the method further comprises: comparing the prediction accuracy rate corresponding to the update weight with the prediction accuracy rate of the initial machine learning model; If the prediction accuracy rate corresponding to the weight is higher than the prediction accuracy rate of the initial machine learning model, the update weight is sent to the plurality of electronic devices; if the prediction accuracy rate corresponding to the update weight is lower than the initial machine learning model If the prediction accuracy of the learning model is determined, a restoration instruction is sent to the plurality of electronic devices, so that the plurality of electronic devices restore the respective trained machine learning models to the initial machine learning model according to the restoration instruction.

Preferably, the termination conditions of the model training method include any of the following: the training duration reaches a preset duration; the prediction accuracy of the trained machine learning model reaches a set standard; the number of training times reaches a preset value; instruction.

A third aspect of the present application provides an electronic device that is communicatively connected to a plurality of other electronic devices, each of the electronic devices is deployed with the same machine learning model, the electronic device includes a processor, and the processor is used to execute a memory The model training method described above is implemented when the computer program stored in the computer is used.

A fourth aspect of the present application provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements the aforementioned model training method.

The invention uses multiple edge computing devices that communicate with each other to collaboratively train the machine learning model, reduces the cost and power consumption of the machine learning model training, makes the network resources more reasonably used, and also continuously optimizes the machine learning model to improve the machine learning model. Accuracy of the learned model.

In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments may be combined with each other in the case of no conflict.

In the following description, many specific details are set forth in order to facilitate a full understanding of the present invention, and the described embodiments are only some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terms used herein in the description of the present invention are for the purpose of describing specific embodiments only, and are not intended to limit the present invention.

Example 1

Please refer to FIG. 1 , which is a schematic diagram of a model training system provided by a first embodiment of the present invention. As shown in FIG. 1 , in this embodiment, the model training system 100 includes a plurality of electronic devices 200 and a controller 300 , and the plurality of electronic devices 200 and the controller 300 are connected through a network and can communicate with each other. communication. The network may be a wired network or a wireless network, such as a fourth generation (4th Generation, 4G) mobile communication network, a fifth generation (5th Generation, 5G) mobile communication network, a wireless True (Wireless Fidelity, WIFI), Bluetooth, etc. The controller 300 may be, but not limited to, terminal devices such as desktop computers, notebook computers, servers, etc., and may also be cloud computing servers, which are not specifically limited in the present invention.

In the embodiment of the present application, the plurality of electronic devices 200 may be edge computing devices, and the edge computing devices have the ability to collect edge-side data of the Internet of Things or the Industrial Internet for training and optimizing machine learning models, and also It has intelligent computing ability based on machine learning. For example, the plurality of electronic devices 200 may be control devices located on different production lines in a factory on the Industrial Internet, or may be different computer devices or servers belonging to different users, which are not limited in this application.

In this embodiment of the present application, each of the electronic devices 200 is deployed with the same machine learning model, and the machine learning model is a trained machine learning model. The machine learning model may be, but is not limited to, a neural network model. The machine learning model includes a plurality of neurons, and the connections between neurons have corresponding weights. For the convenience of description, the embodiments of this application will describe the The machine learning model initially deployed in the electronic device 200 is called the initial machine learning model, and the weights in the initial machine learning model are collectively called the first weight W1.

In some embodiments, the machine learning model in each electronic device 200 may be obtained from a cloud storage device. For example, after the cloud computing device has trained the machine learning model, each electronic device 200 Obtain the trained machine learning model from the cloud storage device. In other embodiments, the machine learning model in each electronic device 200 may also be imported by the user. For example, after the user of each electronic device 200 purchases the same machine learning model, the The machine learning model is imported into each of the electronic devices 200 respectively.

In this embodiment of the present application, after deploying the initial machine learning model, each electronic device 200 may perform a model training function in response to a control instruction of the controller 300, wherein the model training function includes:

1) Immediately collect data for training the initial machine learning model as a training sample data set;

The data collected by the electronic device 200 may be the data generated during the operation of the device in the Internet of Things or the Industrial Internet, or the data generated during the operation of the electronic device 200 itself. For example, the electronic device 200 is a production device in a production line. , data that can be used to train the machine learning model will be generated during the production process, and the electronic device 200 will collect the data immediately as the training sample data set. In other embodiments, the data in the electronic device 20 as the training sample data set may also be imported by the user.

In one embodiment, after each electronic device 200 collects data within a preset period of time, the data collected within the preset period of time is used as a training sample data set, for example, data within 24 hours is collected ;

In another embodiment, each electronic device 200 collects a preset amount of data, and uses the preset amount of data as a training sample data set. For example, if the data is a picture of a product defect, a set of data is collected. After a thousand pictures of product defects, use the thousand pictures as a training sample data set;

2) Dividing the training sample data set into a training set and a verification set according to a preset ratio, and using the training set to train the initial machine learning model to obtain a trained machine learning model;

3) Verify the trained machine learning model through the verification set, obtain the prediction accuracy of the trained machine learning model, and the weights between neurons in the trained machine learning model (for the convenience of description) , hereinafter referred to as the second weight), and the prediction accuracy and the second weight are sent to the controller 300 . It can be understood that there will be multiple neurons in the machine learning model, and the connections between each neuron have corresponding weights. Therefore, the first weight, the second weight, and the update weight mentioned in this application all refer to the machine learning model. The weight between each neuron is a set of weights.

After receiving the prediction accuracy rates and the second weights sent by the multiple electronic devices, the controller 300 is further configured to, according to preset rules and the prediction accuracy rates sent by the multiple electronic devices 200, select the prediction accuracy from the multiple electronic devices 200 . One set of the second weights sent by each electronic device 200 is selected as the update weight, and the update weight is sent to each electronic device 200, so that the electronic device 200 updates the trained machine learning model according to the update weight. The preset rule may be to select the one with the highest prediction accuracy rate, or may be to select the one with the middle prediction accuracy rate.

For example, the controller 300 communicates with four electronic devices 200. For the convenience of description, the four electronic devices 200 are named as E1, E2, E3, and E4, respectively. The four electronic devices 200 collect data respectively in response to the control commands of the controller 300, and the obtained training sample data sets are D1, D2, D3, and D4, respectively. Wherein, the electronic device E1 trains the initial machine learning model through D1, and the prediction accuracy rate L1 of the machine learning model after the training is completed. The electronic device E2 trains the initial machine learning model through D2, and the prediction accuracy of the trained machine learning model is L2. Similarly, the electronic device E3 trains the initial machine learning model through D3 to obtain the trained machine learning model. The prediction accuracy of the model is L3, and the prediction accuracy of the machine learning model after the electronic device E4 is trained by D4 is L4. The controller 300 may select the one with the highest prediction accuracy rate according to a preset rule, and use the second weight corresponding to the one with the highest prediction accuracy rate as the update weight. For example, the prediction accuracy L4 is the highest, then the second weight of the machine learning model trained by the electronic device E4 is used as the update weight and sent to the other three electronic devices, so that the other three electronic devices can update their respective machine learning models according to the update weight. to update.

In the embodiment of the present application, updating the machine learning model according to the update weight refers to correspondingly replacing the weight of each neuron in the machine learning model with the update weight.

By adopting this technical solution, the electronic device 200 as an edge computing device has the ability to train a machine learning model without using a cloud computing environment, which saves costs and utilizes network resources more fully and reasonably. On the other hand, each electronic device 200 can obtain a training sample data set, and the initial machine learning model is subsequently trained and optimized through multiple electronic devices 200, which overcomes the accuracy of the machine learning model caused by insufficient training samples. The problem is not high, so that the accuracy of the model meets the needs of users.

In some optional implementation manners, before sending the updated weight to the electronic device 200, the controller 300 may further perform the following operations:

comparing the prediction accuracy rate corresponding to the update weight with the prediction accuracy rate of the initial machine learning model;

If the prediction accuracy rate corresponding to the update weight is higher than the prediction accuracy rate of the initial machine learning model, sending the update weight to the plurality of electronic devices 200;

If the prediction accuracy rate corresponding to the update weight is lower than the prediction accuracy rate of the initial machine learning model, a restoration instruction is sent to the plurality of electronic devices 200, so that the plurality of electronic devices 200 according to the restoration instruction , and restore the respective trained machine learning models to the original machine learning models.

By adopting this technical solution, when the accuracy of the trained machine learning model is lower than that of the initial machine learning model, the model can be restored to a higher accuracy, so as to avoid the problem that the accuracy of the machine learning model after the training of electronic equipment is reduced. This ensures the accuracy of the trained model.

Since the training of the machine learning model is a sustainable and continuously optimized process, in this embodiment of the present application, after the controller 300 sends the updated weights to the plurality of electronic devices 200, the control The controller 300 continues to generate control instructions to control the plurality of electronic devices 200 to repeatedly perform the aforementioned model training function, and the controller 300 also continues to perform the aforementioned functions of determining and sending updated weights. Through continuous training of the machine learning model, the accuracy of the model can be continuously improved.

In some embodiments, when the training duration reaches a preset duration, the controller 300 stops generating training to stop training the machine learning model. For example, when the training time reaches 30 days, the training of the model is ended.

In other embodiments, when the prediction accuracy of the trained machine learning model reaches a set standard, the training of the machine learning model is stopped. For example, stop training when the prediction accuracy of the trained machine learning model reaches 95%.

In still other embodiments, the training of the machine learning model is stopped when the number of training times reaches a preset value.

In still other embodiments, the training of the machine learning model may be stopped in response to the user's stop instruction.

Embodiment 2

As shown in FIG. 2 , it is a schematic diagram of a model training system provided by the second embodiment of the present application. In this embodiment, the model training system 100 includes a plurality of electronic devices 200, the plurality of electronic devices 200 communicate with each other through a network, and any one of the plurality of electronic devices 200 can be set as a controller. That is to say, the controller may also be an edge computing device, and the electronic device 200 set as the controller is used to perform the model training function as described in the previous embodiment in response to the user's operation, and is also used to respond to the use of the The operator operates to generate a control command, and sends the control command to other electronic devices 200 to control the other electronic devices 200 to perform the model training function described in the previous embodiment. The electronic device 200 set as the controller is further configured to receive the prediction accuracy rate and the second weight value of the trained machine learning model sent by other electronic devices 200 , and according to preset rules and the data sent by the other electronic devices 200 For prediction accuracy, select one of the second weights sent by the plurality of electronic devices 200 as the update weight, update the weight of the machine learning model in the controller, and send the update weight to each of the other electronic devices 200 , so that other electronic devices 200 update the machine learning model according to the update weight.

In this embodiment, the process of executing the model training function by the electronic device 200 is the same as that in the previous embodiment, which is not repeated here.

By adopting this technical solution, there is no need to set up a separate controller, and each electronic device 200 in the model training system 100 can be set as a controller according to actual application requirements under a certain agreement, and control other electronic devices to perform the model training function , so that the network resources can be used reasonably.

Based on the foregoing embodiment, a model training method provided by an embodiment of the present invention is described with reference to FIG. 3 .

FIG. 3 is a flowchart of a model training method provided by an embodiment of the present invention, where the model training method is applied to the electronic device 200 in the foregoing embodiment. According to different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted. For the convenience of description, only the parts related to the embodiments of the present invention are shown.

S301. The electronic device collects data for training the initial machine learning model as a training sample data set.

In one embodiment, the electronic device 200 collects data within a preset period of time as a training sample data set. In another embodiment, the electronic device 200 collects a preset amount of data as a training sample data set.

S302. Divide the training sample data set into a training set and a verification set according to a preset ratio, and use the training set to train the initial machine learning model to obtain a trained machine learning model.

S303, verify the trained machine learning model through the verification set, obtain the prediction accuracy of the trained machine learning model, and the weights between the neurons in the trained machine learning model, and assign all the The prediction accuracy and weight are sent to the controller.

After the electronic device sends the prediction accuracy and the weight to the controller, the controller selects a set of weights among the weights sent by the multiple electronic devices as the update weight according to the prediction accuracy sent by the multiple electronic devices , and send the updated weights to multiple electronic devices.

S304. Obtain the update weight sent by the controller, and update the weight of the corresponding neuron in the machine learning model according to the update weight.

In the embodiment of the present application, updating the machine learning model according to the update weight refers to correspondingly replacing the weight of each neuron in the machine learning model with the update weight.

Based on the foregoing embodiment, a model training method provided by another embodiment of the present invention is described with reference to FIG. 4 . The model training method described in FIG. 4 is applied to the controller 300 in the aforementioned embodiment. According to different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted. For the convenience of description, only the parts related to the embodiments of the present invention are shown.

S401. Generate a control instruction and send the control instruction to multiple electronic devices, where the control instruction is used to trigger the multiple electronic devices to collect a training sample data set for training an initial machine learning model, and generate a data set of training samples for training an initial machine learning model. The data set trains the initial machine learning model, and obtains the prediction accuracy of the trained machine learning model and the weight between each neuron;

S402. Receive the prediction accuracy rates of the respective trained machine learning models and the weights between neurons sent by the multiple electronic devices, and according to preset rules and the prediction accuracy rates sent by the multiple electronic devices, from A group of the weights sent by the plurality of electronic devices is selected as the update weight, and the update weight is sent to each electronic device, so that the electronic device updates the trained machine learning model according to the update weight.

The preset rules are as described above, and are not repeated here.

In some embodiments, in S402, before sending the update weight to each of the electronic devices, the method further includes:

comparing the prediction accuracy rate corresponding to the update weight with the prediction accuracy rate of the initial machine learning model;

If the prediction accuracy rate corresponding to the update weight is higher than the prediction accuracy rate of the initial machine learning model, sending the update weight to the plurality of electronic devices;

If the prediction accuracy rate corresponding to the update weight is lower than the prediction accuracy rate of the initial machine learning model, a restoration instruction is sent to the plurality of electronic devices, so that the plurality of electronic devices, according to the restoration instruction The respective trained machine learning models are restored to the original machine learning models.

In another embodiment, when the controller to which the model training method is applied is one of the plurality of electronic devices, in addition to performing steps S401-402, the controller also performs steps S301-S304 similar to those described above Steps: collecting data for training the initial machine learning model as a training sample data set; dividing the training sample data set into a training set and a verification set according to a preset ratio, and using the training set to train the initial machine learning model to obtain a trained machine learning model; verifying the trained machine learning model through the verification set, obtaining the prediction accuracy of the trained machine learning model, and each neural network in the trained machine learning model weights between elements. After receiving the prediction accuracy and weight sent by other electronic devices, select a set of weights among the weights according to the prediction accuracy and weight obtained by the controller itself and the prediction accuracy sent by multiple other electronic devices As the update weight, and the update weight is sent to the outside of the plurality of electronic devices, the controller also updates the machine learning model in the controller according to the update weight.

In the embodiment of the present application, the termination condition of the model training method includes any of the following:

The training time reaches the preset time;

The prediction accuracy of the trained machine learning model reaches the set standard;

The number of training sessions reaches a preset value; or

A stop command was received.

In a usage scenario of the embodiment of the present application, the plurality of electronic devices 200 may be computer devices belonging to different enterprise users. You can continue to train and optimize the model with your own training samples. On the one hand, training the model through the training samples collected by the user will help the user to keep the training samples confidential. On the other hand, the accuracy of the model can be continuously improved. On the other hand, the training of the model can be independent of cloud computing. environment, save costs, and rationally use network resources.

As shown in FIG. 5 , a schematic diagram of a hardware architecture of an electronic device according to an embodiment of the present application is shown.

The electronic device 200 includes a memory 201 , a processor 202 , a computer program 203 stored in the memory 201 and executable on the processor 202 , such as a model training program, and a communication unit 204 . When the processor 202 executes the computer program 203, the steps performed by the electronic device in the above-mentioned embodiment of the model training method are implemented.

Those skilled in the art can understand that the schematic diagram 5 is only an example of the electronic device 200 and does not constitute a limitation. The electronic device 200 may further include input and output devices, network access devices, bus bars, and the like.

The so-called processor 202 may be a central processing unit (Central Processing Unit, CPU), and may also include other general-purpose processors, digital signal processors (Digital Signal Processors, DSP), dedicated integrated circuits (Application Specific Integrated Circuit, ASIC) , Off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The processor 202 is the control center of the electronic device 200, and uses various interfaces and lines to connect various parts of the entire electronic device 200 .

The memory 201 can be used to store the computer program 203, and the processor 202 realizes the electronic program by running or executing the computer program stored in the memory 201 and calling the data stored in the memory 201. Various functions of the device 200 . The memory 201 may include an external storage medium or a memory. In addition, the memory 201 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (Secure Digital, SD) card, flash memory card (Flash Card), at least one disk memory device, flash memory device, or other volatile solid state memory device.

The communication unit 204 is used to establish a communication connection with other electronic devices and controllers, and the communication unit 204 may be a WIFI module, a Bluetooth module, or the like.

As shown in FIG. 6 , a schematic structural diagram of a controller 300 according to an embodiment of the present application is shown. The illustrated controller includes a communication unit 601, a memory 602, a processor 603, a computer program 604 stored in the memory 602 and executable on the processor 603, such as a model training program. When the processor 603 executes the computer program 604, the steps executed by the controller in the above-mentioned embodiment of the model training method are implemented.

It will be apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, but that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of the present invention is defined by the appended claims rather than the foregoing description, and is therefore intended to fall within the scope of the claims. All changes within the meaning and range of the equivalents of , are included in the present invention. Any reference sign in a claim should not be construed as limiting the claim to which it relates. Furthermore, it is clear that the word "comprising" does not exclude other units or steps and the singular does not exclude the plural. A plurality of units or computer devices stated in the claim for computer device may also be implemented by the same unit or computer device through software or hardware. The terms first, second, etc. are used to denote names and do not denote any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent substitutions can be made without departing from the spirit and scope of the technical solutions of the present invention.

100: Model Training System 200: Electronic Equipment 300: Controller 201, 602: Memory 202, 603: Processor 203, 604: Computer programs 204, 601: Communication unit

FIG. 1 is a schematic diagram of a model training system provided by an embodiment of the present invention.

FIG. 2 is a schematic diagram of a model training system provided by another embodiment of the present invention.

FIG. 3 is a schematic flowchart of a model training method provided by an embodiment of the present invention.

FIG. 4 is a schematic flowchart of a model training method provided by another embodiment of the present invention.

FIG. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

FIG. 6 is a schematic structural diagram of a controller according to an embodiment of the present invention.

Claims (11)

A model training method is applied to a model training system, the model training system includes a plurality of electronic devices and at least one controller, each of the electronic devices is deployed with the same initial machine learning model, and the model training method applies In each of the electronic settings, wherein the model training method includes: Collect data for training the initial machine learning model as a training sample data set; Dividing the training sample data set into a training set and a verification set according to a preset ratio, and using the training set to train the initial machine learning model to obtain a trained machine learning model; The trained machine learning model is verified by the verification set, the prediction accuracy of the trained machine learning model and the weights between neurons in the trained machine learning model are obtained, and the predicted The accuracy and weight are sent to the controller, so that the controller determines an update weight among the weights sent by the multiple electronic devices according to the prediction accuracy sent by the multiple electronic devices, and sends the updated weight to the plurality of electronic devices; Obtain the update weight sent by the controller, and update the weight correspondence between neurons in the trained machine learning model to the update weight. The model training method according to claim 1, wherein data for training the initial machine learning model within a preset time period is collected as the training sample data set; or a preset number of data for training the initial machine learning model is collected. The data of the initial machine learning model is used as the training sample data set. The model training method according to claim 2, wherein the method further comprises: receiving a restoration instruction, and restoring the trained machine learning model to an initial machine learning model, wherein the restoration instruction is performed after the update It is generated when the prediction accuracy rate corresponding to the weight is lower than the prediction accuracy rate of the initial machine learning model. The model training method according to any one of claim 1 to claim 3, wherein the electronic device to which the model training method is applied is an edge computing device. A model training method is applied to a model training system, the model training system includes a plurality of electronic devices and at least one controller, each of the electronic devices is deployed with the same initial machine learning model, and the model training method applies In the controller, wherein the model training method includes: Generate a control instruction and send the control instruction to each of the electronic devices, wherein the control instruction is used to trigger each of the electronic devices to collect a training sample data set for training an initial machine learning model, and according to the The initial machine learning model is trained by the training sample data set, and the prediction accuracy of the trained machine learning model and the weight between each neuron are obtained; Receive the prediction accuracy of the trained machine learning model and the weights between neurons sent by the multiple electronic devices, and obtain the prediction accuracy from the multiple electronic devices according to the preset rules and the prediction accuracy sent by the multiple electronic devices. The update weight is determined from the weights sent by each electronic device, and the update weight is sent to each of the electronic devices, so that each of the electronic devices can change the weights between the neurons in the trained machine learning model The corresponding update is the update weight. The model training method according to claim 5, wherein the updating weight is determined from the weights sent by the plurality of electronic devices according to the preset rule and the prediction accuracy rates sent by the plurality of electronic devices, comprising: : The one with the highest prediction accuracy is selected, and the weight corresponding to the one with the highest prediction accuracy is used as the update weight. The model training method according to claim 5, wherein before sending the updated weights to each of the electronic devices, the method further comprises: comparing the prediction accuracy rate corresponding to the update weight with the prediction accuracy rate of the initial machine learning model; If the prediction accuracy rate corresponding to the update weight is higher than the prediction accuracy rate of the initial machine learning model, sending the update weight to the plurality of electronic devices; If the prediction accuracy rate corresponding to the update weight is lower than the prediction accuracy rate of the initial machine learning model, a restoration instruction is sent to the plurality of electronic devices, so that the plurality of electronic devices, according to the restoration instruction The respective trained machine learning models are restored to the original machine learning models. The model training method according to any one of claim 5 to claim 7, wherein the termination condition of the model training method includes any of the following: The training time reaches the preset time; The prediction accuracy of the trained machine learning model reaches the set standard; The number of training sessions reaches a preset value; or A stop command was received. An electronic device, connected in communication with a plurality of other electronic devices, each of the electronic devices is deployed with the same machine learning model, the electronic device includes a processor, and the processor is used to execute a computer program stored in a memory. Implement the model training method described in any one of claim 1 to claim 4. A controller, the controller comprising a processor configured to implement the model training method according to any one of claim 5 to claim 8 when executing a computer program stored in a memory. A computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements the model training method as described in any one of claim 1 to claim 4, or as claimed in claim 5 to claim 4 The model training method described in any one of item 8.

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