WO2023134184A1 - 信息处理系统、方法、装置、设备及存储介质 - Google Patents

信息处理系统、方法、装置、设备及存储介质 Download PDF

Info

Publication number
WO2023134184A1
WO2023134184A1 PCT/CN2022/117643 CN2022117643W WO2023134184A1 WO 2023134184 A1 WO2023134184 A1 WO 2023134184A1 CN 2022117643 W CN2022117643 W CN 2022117643W WO 2023134184 A1 WO2023134184 A1 WO 2023134184A1
Authority
WO
WIPO (PCT)
Prior art keywords
digital twin
model
data provider
equipment
twin model
Prior art date
Application number
PCT/CN2022/117643
Other languages
English (en)
French (fr)
Inventor
官祥臻
展波
孙来超
刘杰
王培聪
王守森
杨凯
Original Assignee
工赋(青岛)科技有限公司
卡奥斯工业智能研究院(青岛)有限公司
海尔数字科技(青岛)有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 工赋(青岛)科技有限公司, 卡奥斯工业智能研究院(青岛)有限公司, 海尔数字科技(青岛)有限公司 filed Critical 工赋(青岛)科技有限公司
Priority to US18/566,999 priority Critical patent/US20240273380A1/en
Publication of WO2023134184A1 publication Critical patent/WO2023134184A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/098—Distributed learning, e.g. federated learning

Definitions

  • the present application belongs to the technical field of information processing, and specifically relates to an information processing system, method, device, equipment and storage medium.
  • Federated machine learning also known as federated learning (Federated Learning)
  • Federated machine learning can unite all parties for data usage and collaborative modeling without leaving the data locally, and has become a common method in privacy-preserving computing.
  • participants in the federated learning training include collaborators and various data providers.
  • the collaborator and each data provider jointly determine the initial model to be delivered, and conduct training based on the local data of each data provider.
  • there are many data providers and if the data volume of each data provider is relatively When it is large, there will be a problem of long training time.
  • the existing technology cannot obtain the trained global model in a short time, and has the problem of low efficiency.
  • the application provides an information processing system, method, device, equipment and storage Medium, by setting up a digital twin platform in the first data provider, simulating the operation process of the physical target equipment in the first data provider, obtaining a digital twin model, and then using the obtained digital twin model to guide the second data provider side, so that the second data provider can quickly and accurately determine the model parameters, thereby improving the training efficiency of the global model.
  • an embodiment of the present application provides an information processing system, the system includes: a first data provider participating in federated learning, a collaborator, and multiple second data providers;
  • the first data provider is used to generate and send a digital twin model to the collaborating party;
  • the digital twin model is obtained by simulating the operation process of the physical target device based on the digital twin platform;
  • the digital twin platform is set in the first In the data provider;
  • the digital twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • the collaborating party is used to receive the digital twin model sent by the first data provider, and send the digital twin model to each second data provider, and receive the model parameters uploaded by each second data provider;
  • the model parameters are aggregated to obtain a global model;
  • the second data provider is used to receive the digital twin model sent by the collaborating party, and train the digital twin model according to their respective local data to obtain model parameters, and send the model parameters to the collaborating party square.
  • an embodiment of the present application provides an information processing method, the method is applied to a first data provider, and the method includes:
  • the operation process of the entity target equipment is simulated and predicted, and the digital twin model is determined according to the prediction result;
  • the digital twin model is obtained by simulating the operation process of the entity target equipment based on the digital twin platform;
  • the digital twin platform is set In the first data provider;
  • the digital twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • the embodiment of the present application provides an information processing method, which is applied to a collaborating party, and the method includes:
  • the digital twin model sent by the first data provider; the digital twin model is obtained by simulating the operation process of the physical target device based on the digital twin platform; the digital twin platform is set in the first data provider; The digital twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • the model parameters uploaded by each second data provider are received, and the model parameters are aggregated to obtain the global model.
  • the embodiment of the present application provides an information processing method, which is applied to the second data provider, and the method includes:
  • the digital twin model sent by the collaborating party; the digital twin model is obtained by simulating the operation process of the physical target device based on the digital twin platform; the digital twin platform is set in the first data provider; the digital twin The twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • the embodiment of the present application provides an information processing device, the device is set at the first data provider, and the device includes:
  • the prediction module is configured to simulate and predict the operation process of the physical target equipment based on the digital twin platform
  • the determination module is configured to determine the digital twin model according to the prediction result; the digital twin model is obtained by simulating the operation process of the physical target device based on the digital twin platform; the digital twin platform is set in the first data provider; The digital twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • the sending module is configured to send the digital twin model to the collaborating party, so that the collaborating party sends the digital twin model to each second data provider.
  • the embodiment of the present application provides an information processing device, the device is set at the collaborating party; the device includes:
  • the receiving module is configured to receive the digital twin model sent by the first data provider; the digital twin model is obtained by simulating the operation process of the physical target device based on the digital twin platform; the digital twin platform is set at In a data provider; the digital twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • the sending module is configured to send the digital twin model to each second data provider, so that each second data provider uses local data to train the digital twin model to obtain model parameters;
  • the processing module is configured to receive model parameters uploaded by each second data provider, and aggregate the model parameters to obtain a global model.
  • the embodiment of the present application provides an information processing device, the device is set at the second data provider, and the device includes:
  • the receiving module is configured to receive the digital twin model sent by the collaborating party; the digital twin model is obtained by simulating the operation process of the physical target device based on the digital twin platform; In the square; the digital twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • the training module is configured to train the digital twin model according to respective local data to obtain model parameters, and send the model parameters to the collaborating party.
  • the embodiment of the present application provides an information processing device, including: a memory and at least one processor;
  • the memory stores computer-executable instructions
  • the at least one processor executes the computer-executed instructions stored in the memory, so that the at least one processor executes the information processing method according to any one of the second aspect, the third aspect and the fourth aspect.
  • the embodiment of the present application provides a computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, and when the processor executes the computer-executable instructions, the second aspect and the third aspect are implemented.
  • the information processing system, method, device, device, and storage medium provided by the embodiments of the present application, by setting up a digital twin platform in the first data provider, and through the digital twin platform, the entity target equipment The running process of the simulation test is carried out to obtain a digital twin model.
  • the digital twin model can reflect the relationship between the target result and the operating status of multiple devices that affect the target result, and then send the digital twin model to the collaborating party, so that the collaborating party can The digital twin model is sent to the second data provider, so that the second data provider uses local data to perform model training to obtain model parameters.
  • FIG. 1 is a schematic structural diagram of an information processing system provided by an embodiment of the present application.
  • FIG. 2 is a schematic flowchart of an information processing method provided in an embodiment of the present application.
  • Fig. 3 is a schematic flow chart of another information processing method provided by the embodiment of the present application.
  • FIG. 4 is a schematic flowchart of another information processing method provided in the embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of an information processing device provided in an embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of another information processing device provided by an embodiment of the present application.
  • FIG. 7 is a schematic structural diagram of another information processing device provided by an embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of an information processing device provided by an embodiment of the present application.
  • FIG. 1 is a schematic structural diagram of an information processing system provided by an embodiment of the present application.
  • the information processing system includes a first data provider, a coordinating party, and multiple second data providers.
  • the first data provider and the second data provider can be a plurality of institutions, here can be cigarette factories located in various regions, such as the first data provider is a cigarette factory located in A region, and the second data provider Cigarette factories located in area B, area C, and area D respectively.
  • the first data provider is a cigarette factory located in A region
  • the second data provider Cigarette factories located in area B, area C, and area D respectively.
  • their cigarette equipment is the same.
  • the weight stability of the produced cigarettes is also different.
  • each data provider uses local data to train the model through the method of federated learning, and the model training can be completed without the data being out of the local area, so that the Cigarette factories in each region control the operation of cigarette equipment based on the trained global model.
  • a digital twin platform is set up in a data provider (the first data provider), and a digital twin model is obtained by simulating the operation process of the physical target device (cigarette device), which can reflect the target result relationship with multiple equipment operating states that affect the target result, so that the digital twin model is sent to the collaborating party, so that the collaborating party sends the digital twin model to each second data provider, so that each second data
  • the provider can train on the basis of the digital twin model instead of training based on the initial model randomly set, thereby improving the efficiency of determining the global model.
  • Fig. 1 is a schematic structural diagram of an information processing system provided by an embodiment of the present application. As shown in Fig. 1, the system includes: a first data provider participating in federated learning, a collaborator and multiple second data providers;
  • the first data provider is used to generate and send a digital twin model to the collaborating party;
  • the digital twin model is obtained by simulating the operation process of the physical target device based on the digital twin platform;
  • the digital twin platform is set in the first In the data provider;
  • the digital twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • the collaborating party is used to receive the digital twin model sent by the first data provider, and send the digital twin model to each second data provider, and receive the model parameters uploaded by each second data provider;
  • the model parameters are aggregated to obtain a global model;
  • the second data provider is used to receive the digital twin model sent by the collaborating party, and train the digital twin model according to their respective local data to obtain model parameters, and send the model parameters to the collaborating party square.
  • the information processing system includes multiple data providers and collaborators.
  • the first data provider is equipped with a digital twin platform, which can conduct simulation tests on the operation process of the target equipment to obtain a digital twin model.
  • the digital twin platform is not set up in the second data provider.
  • the first data provider can be the Qingdao Cigarette Factory, and the digital twin platform can simulate the operation process of the cigarette equipment under different operating states in the cigarette factory to obtain the cigarettes corresponding to different operating states. Support weight stability, which is the target result.
  • the equipment operating status that affects the weight stability of cigarettes includes parameters such as the rotation speed of the needle roller, the negative pressure of the VE suction chamber, the positive pressure of the small fan, and the height of the baffle.
  • Multiple device operating states can be set to different values, and the corresponding cigarette weight stability can be obtained.
  • a digital twin model can be obtained based on the simulated data, and the digital twin model can reflect multiple equipment operating states that have a greater impact on the target result, as well as ideal setting values for multiple equipment operating states.
  • the first data provider after the first data provider obtains the digital twin model, it can send the obtained digital twin model to the collaborating party, and the collaborating party is used to send the digital twin model to each second data provider, and each second The respective local data is stored in the data provider.
  • each second data provider is Qingzhou Cigarette Factory, Jinan Cigarette Factory, etc.
  • the local data stored in the second data provider is the actual value of the equipment operating status and the corresponding cigarette weight stability.
  • the resulting digital twin model can be trained on local data to obtain model parameters.
  • the model parameters refer to the corrected ideal setting values of the operating states of each device obtained based on the respective local data, and the corresponding target results.
  • the digital twin model indicates that when the rotation speed of the needle roller is 1000 rpm, the cigarette weight stability is 95%, which has a good effect; after training based on the local data of a second data provider, the obtained When the corrected rotation speed of the needle roller is 1100 r/min, the cigarette weight stability is 98%, and the effect is good.
  • the training process is that when each second data provider knows that the needle roller speed indicated by the digital twin model is 1000 rpm, the cigarette weight stability can reach a good value, then the second data provider will use
  • the speed value of the needle roller is set at about 1000 rpm, such as 1100 rpm, 900 rpm, etc., so as to quickly obtain the corrected model parameters.
  • each second data provider can make adjustments on the basis of the benchmark value when performing model training.
  • the direction of training is provided during training, and the corrected model parameters can be quickly obtained.
  • the collaborating party may aggregate the model parameters to obtain the global model.
  • the operating state of each device in the global model is determined by integrating the model parameters provided by each second data provider, and the operating state of each device set in the global model is relative to the first data provider and each second data provider. It may not be optimal, but it is the optimal setting for the whole.
  • the global model is determined, and the model training process ends when the global model converges.
  • the information processing system includes: a first data provider participating in federated learning, a collaborator, and multiple second data providers; the first data provider is used to generate and send a digital twin to the collaborator model; the digital twin model is obtained by simulating the operation process of the physical target equipment based on the digital twin platform; the digital twin platform is set in the first data provider; the digital twin model is used to reflect the target result and influence The relationship between multiple equipment operating states of the target result; the collaborating party is used to receive the digital twin model sent by the first data provider, and send the digital twin model to each second data provider , and receive the model parameters uploaded by each second data provider; aggregate the model parameters to obtain the global model; the second data provider is used to receive the digital twin model sent by the collaborator, and according to their respective The local data of the digital twin model is trained to obtain model parameters, and the model parameters are sent to the collaborating party.
  • Fig. 2 is a schematic flow chart of an information processing method provided by an embodiment of the present application; as shown in Fig. 2, the method is applied to the first data provider, and the method includes:
  • Step S201 simulating and predicting the operation process of the physical target device based on the digital twin platform
  • Step S202 determine the digital twin model according to the prediction result; the digital twin model is obtained by simulating the operation process of the physical target equipment based on the digital twin platform; the digital twin platform is set in the first data provider; the digital twin The twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • Step S203 sending the digital twin model to the collaborating party.
  • a digital twin platform is set in the first data provider, and the digital twin platform can realize the simulation and prediction of the operation process of the physical target equipment. For example, simulate the cigarette weight stability of cigarette equipment under various equipment operating conditions to obtain a digital twin model.
  • the digital twin model is the optimal value for setting the operating state of the equipment.
  • the obtained digital twin model will be sent to collaborators. Since the cigarette equipment used by the second data provider is the same as that used by the first data provider, the twin digital model can be used to guide the model training process of each second data provider. Therefore, the digital twin model is sent to the collaborating party, so that the collaborating party sends it to the second data provider.
  • the physical target device is a cigarette machine; the operating status of the device includes: at least one of: needle roller speed, VE suction chamber negative pressure, small fan positive pressure and baffle height, device running time, failure rate item; the target result is cigarette weight stability.
  • the physical target equipment is the cigarette rolling machine
  • the target result is the cigarette weight stability.
  • the operating status of the equipment that affects the weight stability of cigarettes includes: at least one of the rotation speed of the needle roller, the negative pressure of the VE suction chamber, the positive pressure of the small fan, the height of the baffle, the operating time of the equipment, and the failure rate. Therefore, the digital twin model can be obtained by simulating the working process of the cigarette making machine. During the simulation process, the operating status of each device can be set to different values to obtain the corresponding cigarette weight stability.
  • the digital twin platform is used to simulate and predict the operation process of the physical target equipment, and the digital twin model is determined according to the prediction results, including:
  • the three-dimensional model is established based on the physical target device;
  • the digital twin model is obtained according to the current operating status of all equipment and corresponding prediction results.
  • control instructions for all current equipment operating states that affect the target result can be automatically generated, for example, including parameters such as needle roller speed, VE suction chamber negative pressure, small fan positive pressure, and baffle height instruction.
  • the three-dimensional model can be used to simulate the running process of the physical target device.
  • the three-dimensional model is a virtual model corresponding to the physical target device, which can model the predicted results of the physical target device under corresponding control instructions.
  • the algorithm based on deep learning can obtain the digital twin model. For example, the control command is used as the input of the deep learning algorithm, the prediction result is used as the output of the deep learning algorithm, and the digital twin model is obtained through the automatic learning process of the deep learning algorithm.
  • the above-mentioned process of determining the digital twin model has the advantage of being simple and fast, and can simulate the operation of the physical target equipment, so as to obtain the equipment operating status that affects the target result.
  • Fig. 3 is a schematic flow diagram of another information processing method provided by the embodiment of the present application; as shown in Fig. 3, the embodiment of the present application also provides an information processing method, which is applied to the collaborating party, and the method includes:
  • Step S301 receiving the digital twin model sent by the first data provider; the digital twin model is obtained by simulating the operation process of the physical target device based on the digital twin platform; the digital twin platform is set at the first data provider In the square; the digital twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • Step S302 sending the digital twin model to each second data provider, so that each second data provider uses local data to train the digital twin model to obtain model parameters;
  • Step S303 receiving model parameters uploaded by each second data provider, and performing aggregation processing on the model parameters to obtain a global model.
  • the digital twin model sent by the first data provider can be received, and the digital twin model can be sent to each second data provider.
  • the second data provider stores historical data of equipment operation, That is local data.
  • the digital twin model when issuing the digital twin model, it can be encrypted by an encryption algorithm, and the second data provider can use the stored public key to decrypt after obtaining the digital twin model.
  • the collaborating party can also send preset training times.
  • each second data provider After each second data provider receives the digital twin model, it can use local data to train the digital twin model, obtain model parameters, encrypt the model parameters, and send it to the collaborating party.
  • the collaborating party may perform aggregation processing based on the model parameters to obtain a global model.
  • sending the digital twin model to each second data provider includes:
  • receive model parameters sent by each second data provider including:
  • the updated model parameters are determined according to the modified value and the corresponding cigarette weight stability; the updated model parameters are the target values of the various equipment operating states .
  • each second data provider can use the local data to train only the operation status of the equipment to be verified this time, and obtain the model parameters corresponding to the operation status of the equipment to be verified this time.
  • the two parameters of the needle roller speed and the negative pressure of the VE suction chamber can be verified, that is, the stability of the cigarette weight can be obtained by modifying these two parameters, so as to obtain the optimal The modified value corresponding to the running status of the device to be verified this time.
  • the coordinating party can also receive the modified value of the equipment operation status of this verification sent by each second data provider, as well as the corresponding cigarette weight stability.
  • the updated model parameters are obtained by aggregating the modified values of the equipment operating status and the corresponding cigarette weight stability sent by each second data provider.
  • the updated model parameters are determined according to the modified value and the corresponding cigarette weight stability, including:
  • the addition result is multiplied by a preset coefficient, and the obtained multiplication result is determined as a model parameter in the global model corresponding to the operating state of the equipment to be verified to obtain an updated model parameter.
  • the modified value sent by each second data provider and the corresponding cigarette weight stability may be weighted and summed to obtain updated model parameters.
  • the modified value of the needle roller speed sent by each second data provider can be multiplied by the corresponding cigarette weight stability to obtain Multiple multiplication results are added, and the multiplication results are multiplied by a preset coefficient to obtain updated model parameters corresponding to the operating state of the equipment to be verified.
  • the specific setting value of the preset coefficient is not limited here, and may be the number of the second data providers.
  • a weighting coefficient can also be set for each multiplication result to obtain the product of the weighting coefficient and the multiplication result, and then add the products to determine the updated model parameter corresponding to the operating state of the equipment to be verified.
  • the method also includes:
  • the model parameters can be sent to the second data provider and the first data provider again, so that each The second data provider and the first data provider can train the digital twin model based on the local data again, obtain the model parameters uploaded by each data provider again and perform aggregation processing, and repeat the above process of model distribution and aggregation processing, until the global model converges.
  • the global model converges at this time, it means that the operation status of the equipment to be verified this time has been verified. You can set the operation status of the equipment to be verified next time, and repeat the above process in turn until the operation status of all devices is verified. , to get the final global model.
  • the convergence of the global model it may be determined that the number of training times has reached a preset number of training times, or that the target result corresponding to each first data provider or second data provider has reached a preset result.
  • the embodiment of the present application also provides an information processing method, which is applied to the second data provider, and the method includes:
  • Step S401 receiving the digital twin model sent by the collaborating party; the digital twin model is obtained by simulating the operation process of the physical target device based on the digital twin platform; the digital twin platform is set in the first data provider; The digital twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • Step S402 train the digital twin model according to their respective local data to obtain model parameters, and send the model parameters to the collaborating party;
  • the local data stored therein is different, and the target entity device may set different values for the device operation state during actual operation.
  • the training model can be trained based on local data, so that the obtained global model can meet the needs of various regions for setting the equipment operating status.
  • each second data provider can be guided to better train the model. It can be changed around the ideal setting value of each equipment operating state provided by the digital twin model, thus making the training process faster.
  • FIG. 5 is a schematic structural diagram of an information processing device 50 provided in an embodiment of the present application.
  • the device is set at the first data provider. As shown in FIG. 5 , the device includes:
  • the prediction module 510 is configured to simulate and predict the operation process of the entity target equipment based on the digital twin platform;
  • the determining module 520 is configured to determine the digital twin model according to the prediction result; the digital twin model is obtained by simulating the operation process of the physical target device based on the digital twin platform; the digital twin platform is set in the first data provider ; The digital twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • the sending module 530 is configured to send the digital twin model to the collaborating party.
  • the determining module 520 is specifically configured to:
  • the three-dimensional model is established based on the physical target device;
  • the digital twin model is obtained according to the current operating status of all equipment and corresponding prediction results.
  • the physical target device is a cigarette machine; the operating status of the device includes: at least one of: needle roller speed, VE suction chamber negative pressure, small fan positive pressure and baffle height, device running time, failure rate item; the target result is cigarette weight stability.
  • An information processing device provided in an embodiment of the present application can execute the information processing method of the present application applied to the first data provider, and has corresponding functional modules and beneficial effects for executing the method.
  • Fig. 6 is a schematic structural diagram of an information processing device 60 provided by an embodiment of the present application.
  • the device is set at the collaborating party. As shown in Fig. 6, the device includes:
  • the receiving module 610 is configured to receive the digital twin model sent by the first data provider; the digital twin model is obtained by performing a simulation test on the operating process of the physical target device based on the digital twin platform; the digital twin platform is set at In the first data provider; the digital twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • the sending module 620 is configured to send the digital twin model to each second data provider, so that each second data provider uses local data to train the digital twin model to obtain model parameters;
  • the processing module 630 is configured to receive model parameters uploaded by each second data provider, perform aggregation processing on the model parameters, and obtain a global model.
  • the sending module 620 is specifically configured as:
  • the receiving module 610 is specifically configured as:
  • processing module 630 is specifically configured as:
  • the updated model parameters are determined according to the modified value and the corresponding cigarette weight stability; the updated model parameters are the target values of the various equipment operating states .
  • processing module 630 is specifically configured to:
  • the addition result is multiplied by a preset coefficient, and the obtained multiplication result is determined as a model parameter in the global model corresponding to the operating state of the equipment to be verified to obtain an updated model parameter.
  • the sending module 620 is further configured to: send the updated model parameters to the first data providing method and each second data provider, and repeatedly execute the first data provider and the second data provider using The respective local data trains the digital twin model to obtain model parameters;
  • the receiving module 610 is also configured to receive the model parameters uploaded by the first data provider and each second data provider, perform aggregation processing on the model parameters, and obtain the process of the global model until it is determined that the global model converges;
  • the device further includes a determination module, configured to determine the operating status of the equipment to be verified next time after the global model converges until the verification of the operating states of the plurality of equipment is completed.
  • An information processing device provided in an embodiment of the present application can execute the information processing method of the present application applied to a collaborating party, and has corresponding functional modules and beneficial effects for executing the method.
  • FIG. 7 is a schematic structural diagram of an information processing device 70 provided in an embodiment of the present application.
  • the device is set at the second data provider. As shown in FIG. 7 , the device includes:
  • the receiving module 710 is configured to receive the digital twin model sent by the collaborating party; the digital twin model is obtained by simulating the operation process of the physical target device based on the digital twin platform; the digital twin platform is set in the first data In the provider; the digital twin model is used to reflect the relationship between the target result and multiple equipment operating states that affect the target result;
  • the training module 720 is configured to train the digital twin model according to respective local data to obtain model parameters, and send the model parameters to the collaborating party.
  • An information processing device provided in an embodiment of the present application can execute the information processing method of the present application applied to the second data provider, and has corresponding functional modules and beneficial effects for executing the method.
  • FIG. 8 is a schematic structural diagram of an information processing device 80 provided by an embodiment of the present application. As shown in FIG. 8 , the information processing device 80 includes: a memory 810 and at least one processor 820 .
  • the memory 810 stores computer-executable instructions
  • the at least one processor 820 executes the computer-implemented instructions stored in the memory 810, so that the at least one processor 820 executes to implement the present application.
  • the memory 810 and the processor 820 are connected through a bus 830 .
  • the present application also provides a readable storage medium, in which execution instructions are stored.
  • execution instructions are stored.
  • the implementation of the above-mentioned embodiments is realized. Prompt method.
  • the present application also provides a program product, which includes executable instructions, and the executable instructions are stored in a readable storage medium.
  • At least one processor of the information processing device may read the execution instruction from the readable storage medium, and the at least one processor executes the execution instruction so that the information processing apparatus implements the information processing methods provided in the foregoing various implementations.
  • the disclosed devices and methods may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of modules is only a logical function division. In actual implementation, there may be other division methods.
  • multiple modules or components can be combined or integrated. to another system, or some features may be ignored, or not implemented.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or modules may be in electrical, mechanical or other forms.
  • a module described as a separate component may or may not be physically separated, and a component shown as a module may or may not be a physical module, that is, it may be located in one place, or may also be distributed to multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional module in each embodiment of the present application may be integrated into one processing module, each module may exist separately physically, or two or more modules may be integrated into one module.
  • the above-mentioned integrated modules can be implemented in the form of hardware, or in the form of hardware plus software function modules.
  • the above-mentioned integrated modules implemented in the form of software function modules can be stored in a computer-readable storage medium.
  • the above-mentioned software function modules are stored in a storage medium, and include several instructions to enable a computer device (which may be a personal computer, server, or network device, etc.) or a processor (English: processor) to execute the methods of the various embodiments of the present application. partial steps.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (English: Read-Only Memory, abbreviated: ROM), random access memory (English: Random Access Memory, abbreviated: RAM), magnetic disk or optical disc, etc.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computer Hardware Design (AREA)
  • Geometry (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

本申请属于信息处理技术领域，具体涉及一种信息处理系统、方法、装置、设备及存储介质。本申请旨在解决无法在短时间内得到训练后的全局模型的问题。该系统包括：参与联邦学习的第一数据提供方、协作方和多个第二数据提供方；第一数据提供方用于生成并向协作方发送数字孪生模型；数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；协作方用于将数字孪生模型发送给各个第二数据提供方，以使第二数据提供方基于本地数据对数字孪生模型进行训练并接收相应的模型参数；并对所述模型参数进行聚合处理，得到全局模型，使得各个第二数据提供方能够在数字孪生模型的基础上进行训练。

Description

信息处理系统、方法、装置、设备及存储介质
本申请要求于2022年01月13日提交中国专利局、申请号为202210039186.6、申请名称为“信息处理系统、方法、装置、设备及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请属于信息处理技术领域，具体涉及一种信息处理系统、方法、装置、设备及存储介质。
背景技术
联邦机器学习(Federated machine learning)，又称联邦学习(Federated Learning)，能够在数据不出本地的前提下，联合各方进行数据使用和协同建模，成为隐私保护计算中的一种常用方法。
现有的联邦学习的训练方法中，参与联邦学习训练的包括协作方和各个数据提供方。在进行训练时，协作方与各个数据提供方共同确定下发的初始模型，基于各个数据提供方的本地数据进行训练，但是，数据提供方较多，且若每个数据提供方的数据量较大时，则会存在训练时间较长的问题。
因此，现有技术无法在短时间内得到训练后的全局模型，具有效率较低的问题。
发明内容
为了解决现有技术中的上述问题，即为了解决无法在短时间内得到训练后的全局模型，存在训练效率较低的问题，本申请提供了一种信息处理系统、方法、装置、设备及存储介质，通过在第一数据提供方中设置数字孪生平台，对第一数据提供方中的实体目标设备的运行过程进行模拟，得到数字孪生模型，再将得到的数字孪生模型去指导第二数据提供方，使得第二数据提供方能够快速准确的确定模型参数，进而提高全局模型的训练效率。
第一方面，本申请实施例提供了一种信息处理系统，所述系统包括：参与联邦学习的第一数据提供方、协作方和多个第二数据提供方；
所述第一数据提供方用于生成并向协作方发送数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
所述协作方用于接收所述第一数据提供方发送的数字孪生模型，并将所述数字孪生模型发送给各个第二数据提供方，并接收各个第二数据提供方上传的模型参数；对所述模型参数进行聚合处理，得到全局模型；
所述第二数据提供方用于接收所述协作方发送的数字孪生模型，并根据各自的本地数据对所述数字孪生模型进行训练以得到模型参数，并将所述模型参数发送给所述协作方。
第二方面，本申请实施例提供了一种信息处理方法，所述方法应用于第一数据提供方，所述方法包括：
基于数字孪生平台对实体目标设备运行过程进行模拟预测，根据预测结果确定数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
将所述数字孪生模型发送给协作方，以使协作方将所述数字孪生模型发送给各个第二数据提供方。
第三方面，本申请实施例提供一种信息处理方法，应用于协作方，所述方法包括：
接收所述第一数据提供方发送的数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
将所述数字孪生模型发送给各个第二数据提供方，以使各个第二数据提供方利用本地数据对所述数字孪生模型进行训练得到模型参数；
接收各个第二数据提供方上传的模型参数，对所述模型参数进行聚合处理，得到全局模型。
第四方面，本申请实施例提供一种信息处理方法，应用于第二数据提供方，所述方法包括：
接收所述协作方发送的数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
根据各自的本地数据对所述数字孪生模型进行训练以得到模型参数，并将所述模型参数发送给所述协作方；
重复执行上述步骤直至得到全局模型。
第五方面，本申请实施例提供一种信息处理装置，所述装置设置在第一数据提供方，所述装置包括：
预测模块，被配置为基于数字孪生平台对实体目标设备运行过程进行模拟预测；
确定模块，被配置为根据预测结果确定数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
发送模块，被配置为将所述数字孪生模型发送给协作方，以使协作方将所述数字孪生模型发送给各个第二数据提供方。
第六方面，本申请实施例提供一种信息处理装置，所述装置设置在协作方；所述装置包括：
接收模块，被配置为接收所述第一数据提供方发送的数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
发送模块，被配置为将所述数字孪生模型发送给各个第二数据提供方，以使各个第二数据提供方利用本地数据对所述数字孪生模型进行训练得到模型参数；
处理模块，被配置为接收各个第二数据提供方上传的模型参数，对所述模型参数进行聚合处理，得到全局模型。
第七方面，本申请实施例提供一种信息处理装置所述装置，所述装置设置在第二数据提供方，所述装置包括：
接收模块，被配置为接收所述协作方发送的数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
训练模块，被配置为根据各自的本地数据对所述数字孪生模型进行训练以得到模型参数，并将所述模型参数发送给所述协作方。
第八方面，本申请实施例提供一种信息处理设备，包括：存储器和至少一个处理器；
所述存储器存储计算机执行指令；
所述至少一个处理器执行所述存储器存储的计算机执行指令，使得所述至少一个处理器执行如第二方面、第三方面和第四方面任一项所述的信息处理方法。
第九方面，本申请实施例提供一种计算机可读存储介质，所述计算机可读存储介质中存储有计算机执行指令，当处理器执行所述计算机执行指令时，实现如第二方面、第三方面和第四方面任一项所述的信息处理方法。
本领域技术人员能够理解的是，本申请实施例提供的信息处理系统、方法、装置、设备及存储介质，通过在第一数据提供方中设置数字孪生平台，并通过数字孪生平台对实体目标设备的运行过程进行模拟测试以得到数字孪生模型，数字孪生模型可以反映目标结果与影响所述目标结果的多个设备运行状态之间的关系，再将数字孪生模型发送到协作方，使得协作方可以将数字孪生模型发送给第二数据提供方，使得第二数据提供方利用本地数据进行模型训练得到模型参数。
附图说明
下面参照附图来描述本申请的装箱任务处理方法、装置及设备的优选实施方式。此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本申请的原理。附图为：
图1是本申请实施例提供的一种信息处理系统的结构示意图；
图2是本申请实施例提供的一种信息处理方法的流程示意图；
图3是本申请实施例提供的另一种信息处理方法的流程示意图；
图4是本申请实施例提供的又一种信息处理方法的流程示意图；
图5是本申请实施例提供的一种信息处理装置的结构示意图；
图6是本申请实施例提供的另一种信息处理装置的结构示意图；
图7是本申请实施例提供的又一种信息处理装置的结构示意图；
图8是本申请实施例提供的一种信息处理设备的结构示意图。
具体实施方式
为使本申请的目的、技术方案和优点更加清楚，下面将结合本申请的实施例，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
下面以具体地实施例对本申请的技术方案以及本申请的技术方案如何解决上述技术问题进行详细说明。下面这几个具体的实施例可以相互结合，对于相同或相似的概念或过程可能在某些实施例中不再赘述。下面将结合附图，对本申请的实施例进行描述。
下面对本申请实施例的应用场景进行解释：
图1是本申请实施例提供的一种信息处理系统的结构示意图，如图1所示，所述信息处理系统包括第一数据提供方、协作方和多个第二数据提供方。其中，对于第一数据提供方和第二数据提供方可以为多个机构，此处可以为位于各个地区的卷烟厂，如第一数据提供方为位于A地区的卷烟厂、第二数据提供方分别为位于B地区、C地区和D地区的卷烟厂。对于各地区的卷烟厂来说，其卷烟设备均相同。当卷烟设备设置的设备运行状态不同时，生产的烟支重量稳定性也有所不同。为了研究设备运行状态对烟支重量稳定性的结果，通过联邦学习的方法实现各个数据提供方利用本地数据对模型进行训练，可以在保证数据不出本地的情况下完成模型的训练，以使的各个地区的卷烟厂均基于训练后的全局模型控制卷烟设备的运行。
在通过联邦机器学习对各个数据提供方进行训练时，当数据提供方较多且每个数据提供方对应的本地数据的数据量较大时，则在训练时存在训练时间较长的问题。
基于上述问题，考虑到各个数据提供方在进行模型训练时由于无法确定训练标准，使得训练时间较长。基于此，在一个数据提供方(第一数据提供方)中设置数字孪生平台，通过对实体目标设备(卷烟设备)的运行过程进行模拟，以得到数字孪生模型，该数字孪生模型可以反映目标结果与影响所述目标结果的多个设备运行状态之间的关系，从而将该数字孪生模型发送到协作方，使得协作方将该数字孪生模型发送给各个第二数据提供方，使得各个第二数据提供方在进行模型训练时能够在数字孪生模型的基础上进行训练，而非基于随机设置的初始模型进行训练，从而提高确定全局模型的效率。
图1是本申请实施例提供的一种信息处理系统的结构示意图，如图1所示，所述系统包括：参与联邦学习的第一数据提供方、协作方和多个第二数据提供方；
所述第一数据提供方用于生成并向协作方发送数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
所述协作方用于接收所述第一数据提供方发送的数字孪生模型，并将所述数字孪生模型发送给各个第二数据提供方，并接收各个第二数据提供方上传的模型参数；对所述模型参数进行聚合处理，得到全局模型；
所述第二数据提供方用于接收所述协作方发送的数字孪生模型，并根据各自的本地数据对所述数字孪生模型进行训练以得到模型参数，并将所述模型参数发送给所述协作方。
信息处理系统包括多个数据提供方和协作方，第一数据提供方中设置有数字孪生平台，可以对目标设备的运行过程进行模拟测试，以得到数字孪生模型。第二数据提供方中未设置数字孪生平台。以上述卷烟设备的运行过程为例，第一数据提供方可以为青岛卷烟厂，数字孪生平台可以对该卷烟厂中不同运行状态下卷烟设备的运行过程进行模拟，以得到不同运行状态对应的烟支重量稳定性，也就是目标结果。例如，影响烟支重量稳定性的设备运行状态包括：针辊转速、VE吸风室负压、小风机正压及挡板高度等参数。可以设置多个设备运行状态分别为不同的数值，并得到对应的烟支重量稳定性。经过第一数据提供方的处理，可以基于模拟数据得到数字孪生模型，数字孪生模型可以反映对目标结果影响较大的多个设备运行状态，以及多个设备运行状态的理想设置值。
其中，在第一数据提供方得到数字孪生模型后，可以将得到的数字孪生模型发送给协作方，所述协作方用于将该数字孪生模型发送给各个第二数据提供方，在各个第二数据提供方中存储有各自的本地数据。例如，各个第二数据提供方为青州卷烟厂、济南卷烟厂等，在第二数据提供方中存储的本地数据为真实的设备运行状态的数值以及对应的烟支重量稳定性。根据本地数据可以对得到的数字孪生模型进行训练，以得到模型参数。其中，模型参数是指基于各自的本地数据得到的修正后的各个设备运行状态的理想设置值，以及对应的目标结果。例如，数字孪生模型中指示当针辊转速为1000转/分时，烟支重量稳定性为95％，具有较好的效果；当基于某一第二数据提供方的本地数据的训练后，得到修正后的针辊转速为1100转/分时，烟支重量稳定性为98％，效果较好。其中，该训练过程为，各个第二数据提供方在得知数字孪生模型指示的针辊转速为1000转/分时，烟支重量稳定性可以 达到较好的数值，则在进行训练时会将针辊转速值设置在1000转/分左右，如1100转/分、900转/分等，从而快速得到修正后的模型参数。
其中，由于数字孪生模型已经确定了各个设备运行状态的基准值，使得各个第二数据提供方在进行模型训练时可以在基准值的基础上进行调整，数字孪生模型为各个第二数据提供方在进行训练时提供了训练的方向，能够快速得到修正后的模型参数。
其中，协作方在得到各个第二数据提供方上传的模型参数后，可以将模型参数进行聚合得到全局模型。其中，全局模型中每一设备运行状态为综合各个第二数据提供方提供的模型参数确定的，全局模型中设置的每一设备运行状态对于第一数据提供方和各个第二数据提供方来说可能不是最优的，但是对于整体来说为最优的设置方式。
经过上述协作方与各个第二数据提供方以及第一数据提供方的模型发送及训练的过程，确定全局模型，当全局模型收敛时结束模型的训练过程。
本申请实施例提供的信息处理系统，包括：参与联邦学习的第一数据提供方、协作方和多个第二数据提供方；所述第一数据提供方用于生成并向协作方发送数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；所述协作方用于接收所述第一数据提供方发送的数字孪生模型，并将所述数字孪生模型发送给各个第二数据提供方，并接收各个第二数据提供方上传的模型参数；对所述模型参数进行聚合处理，得到全局模型；所述第二数据提供方用于接收所述协作方发送的数字孪生模型，并根据各自的本地数据对所述数字孪生模型进行训练以得到模型参数，并将所述模型参数发送给所述协作方，通过在第一数据提供方中设置数字孪生平台，并得到数字孪生模型，从而基于该模型对各个第二数据提供方的训练过程进行指导，从而能够使得各个第二数据提供方在进行训练时能够快速准确的得到模型参数，从而快速得到全局模型，提高模型训练的效率。
图2是本申请实施例提供的一种信息处理方法的流程示意图；如图2所示，所述方法应用于第一数据提供方，所述方法包括：
步骤S201、基于数字孪生平台对实体目标设备运行过程进行模拟预测；
步骤S202、根据预测结果确定数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
步骤S203、将所述数字孪生模型发送给协作方。
其中，在第一数据提供方中设置有数字孪生平台，数字孪生平台可以实现对实体目标设备运行过程的模拟及预测。例如，模拟卷烟设备在各个设备运行状态下的烟支重量稳定性，从而得到数字孪生模型。数字孪生模型对于第一数据提供方来说为设置设备运行状态的最优数值。其中，得到的数字孪生模型会发送给协作方。由于第二数据提供方与第一数据提供方中所使用的卷烟设备相同，因此，可以将孪生数字模型来指导各个第二数据提供方的模型训练过程。因此，将数字孪生模型发送给协作方，使得协作方再将其发送给第二数据提供方。
可选的，所述实体目标设备为卷烟机；所述设备运行状态包括：针辊转速、VE吸风室负压、小风机正压及挡板高度、设备运行时间、故障率中的至少一项；所述目标结果为烟支重量稳定性。
对于烟支生产的过程来说，实体目标设备即为卷烟机，目标结果为烟支重量稳定性。而影响烟支重量稳定性的设备运行状态包括：针辊转速、VE吸风室负压、小风机正压及挡板高度、设备运行时间、故障率中的至少一项。因此，可以通过模拟卷烟机的工作过程来得到数字孪生模型。在模拟过程中，可以设置各个设备运行状态为不同数值，以得到对应的烟支重量稳定性。
下面对生成数字孪生模型的过程进行详细说明。
可选的，所述基于数字孪生平台对实体目标设备运行过程进行模拟预测，根据预测结果确定数字孪生模型，包括：
生成改变影响目标结果的当前所有设备运行状态的控制指令；
根据所述控制指令在三维模型中模拟实体目标设备的运行过程，并得到与所述控制指令对应的预测结果；所述三维模型是基于所述实体目标设备建立的；
基于深度学习算法根据所述当前所有设备运行状态以及对应的预测结果得到所述数字孪生模型。
其中，在生成数字孪生模型时，可以自动生成影响目标结果的当前所有设备运行状态的控制指令，例如，包含针辊转速、VE吸风室负压、小风机正压及挡板高度等参数的指令。基于该指令可以使得三维模型模拟实体目标设备的运行过程。三维模型是与实体目标设备对应的虚拟模型，可以模型实体目标设备在相应的控制指令下的预测结果。最后，基于深度学习的算法可以得到数字孪生模型，例如，将控制指令作为深度学习算法的输入，将预测结果作为深度学习算法的输出，通过深度学习算法的自动学习过程得到数字孪生模型。
上述确定数字孪生模型的过程具有简单快速的优点，可以在模拟实体目标设备的运行，从而得到影响目标结果的设备运行状态。
图3是本申请实施例提供的另一种信息处理方法的流程示意图；如图3所示，本申请实施例还提供一种信息处理方法，应用于协作方，所述方法包括：
步骤S301、接收所述第一数据提供方发送的数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
步骤S302、将所述数字孪生模型发送给各个第二数据提供方，以使各个第二数据提供方利用本地数据对所述数字孪生模型进行训练得到模型参数；
步骤S303、接收各个第二数据提供方上传的模型参数，对所述模型参数进行聚合处理，得到全局模型。
其中，对于协作方来说，可以接收第一数据提供方发送的数字孪生模型，并将该数字孪生模型发送给各个第二数据提供方，第二数据提供方中存储有设备运行的历史数据，也就是本地数据。
其中，在进行数字孪生模型的下发时，可以通过加密算法进行加密，第二数据提供方在得到数字孪生模型后，可以采用存储的公钥进行解密。此外，协作方还可以发送预设训练次数。
各个第二数据提供方在接收到数字孪生模型后，可以利用本地数据对数字孪生模型进行训练，得到模型参数，并将模型参数进行加密，并发送给协作方。
协作方在接收到各个第二数据提供方发送的模型参数后，可以基于模型参数进行聚合处理，以得到全局模型。
可选的，将所述数字孪生模型发送给各个第二数据提供方，包括：
将所述数字孪生模型和本次待校验的设备运行状态发送给各个第二数据提供方，以使各个第二数据提供方利用本地数据对所述数字孪生模型中所述本次待校验的设备运行状态进行训练，得到模型参数；
相应的，接收各个第二数据提供方分别发送的模型参数，包括：
接收所述各个第二数据提供方发送的所述本次待校验的设备运行状态的修改值，以及当所述设备运行状态为所述修改值时对应的烟支重量稳定性；
相应的，对所述模型参数进行聚合处理以得到全局模型，包括：
针对每一本次待校验的设备运行状态，根据所述修改值和对应的烟支重量稳定性确定更新后的模型参数；所述更新后的模型参数为所述各个设备运行状态的目标数值。
其中，由于影响目标设备运行的设备运行状态为多个，因此在每次对模型进行训练时，可以仅训练其中的若干个设备运行状态，并将其确定为本次待校验的设备运行状态。各个第二数据提供方可以利用本地数据仅对本次待校验的设备运行状进行训练，得到本次待校验的设备运行状态对应的模型参数。例如，在首次对模型进行训练时，可以对针辊转速、VE吸风室负压者两个参数进行校验，也就是通过修改这两个参数以得到烟支重量稳定性，从而得到最优的本次待校验的设备运行状态对应的修改值。
相应的，协作方还可以接收各个第二数据提供方发送的本次校验的设备运行状态的修改值，以及对应的烟支重量稳定性。通过对各个第二数据提供方发送的设备运行状态的修改值和对应的烟支重量稳定性进行聚合处理，以得到更新后的模型参数。
可选的，根据所述修改值和对应的烟支重量稳定性确定更新后的模型参数，包括：
针对每一次待校验的设备运行状态，将所述修改值以及对应的所述烟支重量稳定性相乘，并将每一第二数据提供方对应的相乘结果以及第一数据提供方对应的相乘结果相加，得到相加结果；
将所述相加结果与预设系数相乘，将得到的相乘结果确定为所述全局模型中与所述待校验的设备运行状态对应的模型参数以得到更新后的模型参数。
其中，在进行聚合处理时，可以将每一第二数据提供方发送的修改值和对应的烟支重量稳定性进行加权求和，以得到更新后的模型参数。具体的，对于每一待校验的设备运行状态来说，如针辊转速，可以将各个第二数据提供方发送的针辊转速的修改值，与对应的烟支重量稳定性相乘，得到多个相乘结果，并将各个相乘结果相加，再将相加结果与以预设系数相乘，以得到该待校验的设备运行状态对应的更新后的模型参数。其中，预设系数的具体设置数值此处不做限定，可以为第二数据提供方的个数。或者，还可以为每一相乘结果设置一个加权系数，得到加权系数与相乘结果的乘积，再将乘积相加确定为该待校验的设备运行状态对应的更新后的模型参数。
可选的，所述方法还包括：
将所述更新后的模型参数发送给第一数据提供方和各个第二数据提供方，并重复执行第一数据提供方和第二数据提供方利用各自本地数据对所述数字孪生模型进行训练得到模型参数，接收第一数据提供方和各个第二数据提供方上传的模型参数，对所述模型参数进行聚合处理，得到全局模型的过程，直至确定所述全局模型收敛；
当所述全局模型收敛后，确定下次待校验的设备运行状态，直至所述多个设备运行状态均校验完毕。
其中，在协作方得到与本次待校验的设备运行状态对应的更新后的模型参数后，可以将模型参数再次下发给各个第二数据提供方和 第一数据提供方，以使得各个第二数据提供方和第一数据提供方可以再次基于本地数据对数字孪生模型进行训练，并再次获取各个数据提供方上传的模型参数并进行聚合处理，重复执行上述模型下发及聚合处理的过程，直至全局模型收敛。当此时全局模型收敛时，表示本次待校验的设备运行状态已校验完毕，可以设置下次待校验的设备运行状态，依次重复上述过程，直至全部的设备运行状态均校验完毕，得到最终的全局模型。
其中，在确定全局模型收敛时可以为判断训练次数达到预先设置的训练次数，或者，各个第一数据提供方或第二数据提供方对应的目标结果达到预设结果。
如图4所示，本申请实施例还提供一种信息处理方法，应用于第二数据提供方，所述方法包括：
步骤S401、接收所述协作方发送的数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
步骤S402、根据各自的本地数据对所述数字孪生模型进行训练以得到模型参数，并将所述模型参数发送给所述协作方；
重复执行上述步骤直至得到全局模型。
对于各个第二数据提供方来说，其各自存储的本地数据不同，目标实体设备在实际运行过程中，可能会为设备运行状态设置不同的数值。基于本地数据可以对训练模型进行训练，使得到的全局模型可以满足各个地区对设备运行状态设置的需求。
通过接收协作方下发的基于第一数据提供方中的数字孪生平台生成的数字孪生模型可以指导各个第二数据提供方更好的对模型进行训练。可以在数字孪生模型提供的各个设备运行状态的理想设置值附近进行变动，从而使得训练过程更快。
图5是本申请实施例提供的一种信息处理装置50的结构示意图，所述装置设置在第一数据提供方，如图5所示，该装置包括：
预测模块510，被配置为基于数字孪生平台对实体目标设备运行过程进行模拟预测；
确定模块520，被配置为根据预测结果确定数字孪生模型； 所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
发送模块530，被配置为将所述数字孪生模型发送给协作方。
可选的，确定模块520具体被配置为：
生成改变影响目标结果的当前所有设备运行状态的控制指令；
根据所述控制指令在三维模型中模拟实体目标设备的运行过程，并得到与所述控制指令对应的预测结果；所述三维模型是基于所述实体目标设备建立的；
基于深度学习算法根据所述当前所有设备运行状态以及对应的预测结果得到所述数字孪生模型。
可选的，所述实体目标设备为卷烟机；所述设备运行状态包括：针辊转速、VE吸风室负压、小风机正压及挡板高度、设备运行时间、故障率中的至少一项；所述目标结果为烟支重量稳定性。
本申请实施例提供的一种信息处理装置，可以执行本申请应用于第一数据提供方的信息处理方法，具备执行方法相应的功能模块和有益效果。
图6本申请实施例提供的一种信息处理装置60的结构示意图，所述装置设置在协作方，如图6所示，该装置包括：
接收模块610，被配置为接收所述第一数据提供方发送的数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
发送模块620，被配置为将所述数字孪生模型发送给各个第二数据提供方，以使各个第二数据提供方利用本地数据对所述数字孪生模型进行训练得到模型参数；
处理模块630，被配置为接收各个第二数据提供方上传的模型参数，对所述模型参数进行聚合处理，得到全局模型。
可选的，发送模块620具体被配置为：
将所述数字孪生模型和本次待校验的设备运行状态发送给各个第二数据提供方，以使各个第二数据提供方利用本地数据对所述数字孪生模型中所述本次待校验的设备运行状态进行训练，得到模型参数；
相应的，接收模块610具体被配置为：
接收所述各个第二数据提供方发送的所述本次待校验的设备运行状态的修改值，以及当所述设备运行状态为所述修改值时对应的烟支重量稳定性；
相应的，处理模块630具体被配置为：
针对每一本次待校验的设备运行状态，根据所述修改值和对应的烟支重量稳定性确定更新后的模型参数；所述更新后的模型参数为所述各个设备运行状态的目标数值。
可选的，处理模块630具体被配置为：
针对每一次待校验的设备运行状态，将所述修改值以及对应的所述烟支重量稳定性相乘，并将每一第二数据提供方对应的相乘结果以及第一数据提供方对应的相乘结果相加，得到相加结果；
将所述相加结果与预设系数相乘，将得到的相乘结果确定为所述全局模型中与所述待校验的设备运行状态对应的模型参数以得到更新后的模型参数。
可选的，发送模块620还被配置为：将所述更新后的模型参数发送给第一数据提供方法和各个第二数据提供方，并重复执行第一数据提供方和第二数据提供方利用各自本地数据对所述数字孪生模型进行训练得到模型参数；
接收模块610还被配置为接收第一数据提供方和各个第二数据提供方上传的模型参数，对所述模型参数进行聚合处理，得到全局模型的过程，直至确定所述全局模型收敛；
所述装置还包括确定模块，当所述全局模型收敛后，用于确定下次待校验的设备运行状态，直至所述多个设备运行状态均校验完毕。
本申请实施例提供的一种信息处理装置，可以执行本申请应用于协作方的信息处理方法，具备执行方法相应的功能模块和有益 效果。
图7是本申请实施例提供的一种信息处理装置70的结构示意图，所述装置设置在第二数据提供方，如图7所示，该装置包括：
接收模块710，被配置为接收所述协作方发送的数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
训练模块720，被配置为根据各自的本地数据对所述数字孪生模型进行训练以得到模型参数，并将所述模型参数发送给所述协作方。
本申请实施例提供的一种信息处理装置，可以执行本申请应用于第二数据提供方的信息处理方法，具备执行方法相应的功能模块和有益效果。
图8是本申请实施例提供的一种信息处理设备80的结构示意图，如图8所示，信息处理设备80包括：存储器810和至少一个处理器820。
所述存储器810存储计算机执行指令；
所述至少一个处理器820执行所述存储器810存储的计算机执行指令，使得所述至少一个处理器820执行以实现本申请。
其中，存储器810和处理器820通过总线830连接。
相关说明可以对应参见图1-图4的步骤所对应的相关描述和效果进行理解，此处不做过多赘述。
本申请还提供一种可读存储介质，可读存储介质中存储有执行指令，当服务器的至少一个处理器执行该执行指令时，当计算机执行指令被处理器执行时，实现上述实施例中的提示方法。
本申请还提供一种程序产品，该程序产品包括可执行指令，该可执行指令存储在可读存储介质中。信息处理设备的至少一个处理器可以从可读存储介质读取该执行指令，至少一个处理器执行该执行指令使得信息处理装置实施上述各种实施方式提供的信息处理方法。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实 施例仅仅是示意性的，例如，模块的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个模块或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或模块的间接耦合或通信连接，可以是电性，机械或其它的形式。
作为分离部件说明的模块可以是或者也可以不是物理上分开的，作为模块显示的部件可以是或者也可以不是物理模块，即可以位于一个地方，或者也可以分布到多个网络模块上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能模块可以集成在一个处理模块中，也可以是各个模块单独物理存在，也可以两个或两个以上模块集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用硬件加软件功能模块的形式实现。
上述以软件功能模块的形式实现的集成的模块，可以存储在一个计算机可读取存储介质中。上述软件功能模块存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(英文：processor)执行本申请各个实施例方法的部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(英文：Read-Only Memory，简称：ROM)、随机存取存储器(英文：Random Access Memory，简称：RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
最后应说明的是：以上各实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述各实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：本领域技术人员容易理解的是，本申请的保护范围显然不局限于这些具体实施方式。在不偏离本申请的原理的前提下，本领域技术人员可以对相关技术特征进行等同的更改或替换，这些更改或替换之后的技术方案都将落入本申请的保护范围之内。

Claims (14)

  1. 一种信息处理系统，所述系统包括：参与联邦学习的第一数据提供方、协作方和多个第二数据提供方；
    所述第一数据提供方用于生成并向协作方发送数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
    所述协作方用于接收所述第一数据提供方发送的数字孪生模型，并将所述数字孪生模型发送给各个第二数据提供方，并接收各个第二数据提供方上传的模型参数；对所述模型参数进行聚合处理，得到全局模型；
    所述第二数据提供方用于接收所述协作方发送的数字孪生模型，并根据各自的本地数据对所述数字孪生模型进行训练以得到模型参数，并将所述模型参数发送给所述协作方。
  2. 一种信息处理方法，所述方法应用于第一数据提供方，所述方法包括：
    基于数字孪生平台对实体目标设备运行过程进行模拟预测，根据预测结果确定数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
    将所述数字孪生模型发送给协作方，以使所述协作方将所述数字孪生模型发送给各个第二数据提供方。
  3. 根据权利要求2所述的方法，其中所述基于数字孪生平台对实体目标设备运行过程进行模拟预测，根据预测结果确定数字孪生模型，包括：
    生成改变影响目标结果的当前所有设备运行状态的控制指令；
    根据所述控制指令在三维模型中模拟实体目标设备的运行过程，并得到与所述控制指令对应的预测结果；所述三维模型是基于所述实体目 标设备建立的；
    基于深度学习算法根据所述当前所有设备运行状态以及对应的预测结果得到所述数字孪生模型。
  4. 根据权利要求2或3所述的方法，其中，所述实体目标设备为卷烟机；所述设备运行状态包括：针辊转速、VE吸风室负压、小风机正压及挡板高度、设备运行时间、故障率中的至少一项；所述目标结果为烟支重量稳定性。
  5. 一种信息处理方法，应用于协作方，所述方法包括：
    接收所述第一数据提供方发送的数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
    将所述数字孪生模型发送给各个第二数据提供方，以使各个第二数据提供方利用本地数据对所述数字孪生模型进行训练得到模型参数；
    接收各个第二数据提供方上传的模型参数，对所述模型参数进行聚合处理，得到全局模型。
  6. 根据权利要求5所述的方法，其中，将所述数字孪生模型发送给各个第二数据提供方，包括：
    将所述数字孪生模型和本次待校验的设备运行状态发送给各个第二数据提供方，以使各个第二数据提供方利用本地数据对所述数字孪生模型中所述本次待校验的设备运行状态进行训练，得到模型参数；
    相应的，接收各个第二数据提供方分别发送的模型参数，包括：
    接收所述各个第二数据提供方发送的所述本次待校验的设备运行状态的修改值，以及当所述设备运行状态为所述修改值时对应的烟支重量稳定性；
    相应的，对所述模型参数进行聚合处理以得到全局模型，包括：
    针对每一本次待校验的设备运行状态，根据所述修改值和对应的烟支重量稳定性确定更新后的模型参数；所述更新后的模型参数为所述各个设备运行状态的目标数值。
  7. 根据权利要求6所述的方法，其中，根据所述修改值和对应的烟支重量稳定性确定更新后的模型参数，包括：
    针对每一次待校验的设备运行状态，将所述修改值以及对应的所述烟支重量稳定性相乘，并将每一第二数据提供方对应的相乘结果以及第一数据提供方对应的相乘结果相加，得到相加结果；
    将所述相加结果与预设系数相乘，将得到的相乘结果确定为所述全局模型中与所述待校验的设备运行状态对应的模型参数以得到更新后的模型参数。
  8. 根据权利要求7所述的方法，其中，所述方法还包括：
    将所述更新后的模型参数发送给第一数据提供方法和各个第二数据提供方，并重复执行第一数据提供方和第二数据提供方利用各自本地数据对所述数字孪生模型进行训练得到模型参数，接收第一数据提供方和各个第二数据提供方上传的模型参数，对所述模型参数进行聚合处理，得到全局模型的过程，直至确定所述全局模型收敛；
    当所述全局模型收敛后，确定下次待校验的设备运行状态，直至所述多个设备运行状态均校验完毕。
  9. 一种信息处理方法，应用于第二数据提供方，所述方法包括：
    接收所述协作方发送的数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
    根据各自的本地数据对所述数字孪生模型进行训练以得到模型参数，并将所述模型参数发送给所述协作方；
    重复执行上述步骤直至得到全局模型。
  10. 一种信息处理装置，所述装置设置在第一数据提供方，所述装置包括：
    预测模块，被配置为基于数字孪生平台对实体目标设备运行过程进行模拟预测；
    确定模块，被配置为根据预测结果确定数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
    发送模块，被配置为将所述数字孪生模型发送给协作方，以使协作方将所述数字孪生模型发送给各个第二数据提供方。
  11. 一种信息处理装置，所述装置设置在协作方；所述装置包括：
    接收模块，被配置为接收所述第一数据提供方发送的数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
    发送模块，被配置为将所述数字孪生模型发送给各个第二数据提供方，以使各个第二数据提供方利用本地数据对所述数字孪生模型进行训练得到模型参数；
    处理模块，被配置为接收各个第二数据提供方上传的模型参数，对所述模型参数进行聚合处理，得到全局模型。
  12. 一种信息处理装置，所述装置设置在第二数据提供方，所述装置包括：
    接收模块，被配置为接收所述协作方发送的数字孪生模型；所述数字孪生模型是基于数字孪生平台对实体目标设备运行过程进行模拟测试得到的；所述数字孪生平台设置在第一数据提供方中；所述数字孪生模型用于反映目标结果与影响所述目标结果的多个设备运行状态之间的关系；
    训练模块，被配置为根据各自的本地数据对所述数字孪生模型进行训练以得到模型参数，并将所述模型参数发送给所述协作方。
  13. 一种信息处理设备，包括：存储器和至少一个处理器；
    所述存储器存储计算机执行指令；
    所述至少一个处理器执行所述存储器存储的计算机执行指令，使 得所述至少一个处理器执行如权利要求3-4或5-8或9任一项所述的信息处理方法。
  14. 一种计算机可读存储介质，所述计算机可读存储介质中存储有计算机执行指令，当处理器执行所述计算机执行指令时，实现如权利要求3-9任一项所述的信息处理方法。
PCT/CN2022/117643 2022-01-13 2022-09-07 信息处理系统、方法、装置、设备及存储介质 WO2023134184A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US18/566,999 US20240273380A1 (en) 2022-01-13 2022-09-07 Information processing system, method, apparatus, device and storage medium

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202210039186.6A CN114492179B (zh) 2022-01-13 2022-01-13 信息处理系统、方法、装置、设备及存储介质
CN202210039186.6 2022-01-13

Publications (1)

Publication Number Publication Date
WO2023134184A1 true WO2023134184A1 (zh) 2023-07-20

Family

ID=81512436

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/117643 WO2023134184A1 (zh) 2022-01-13 2022-09-07 信息处理系统、方法、装置、设备及存储介质

Country Status (3)

Country Link
US (1) US20240273380A1 (zh)
CN (1) CN114492179B (zh)
WO (1) WO2023134184A1 (zh)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117111997A (zh) * 2023-10-08 2023-11-24 西安大合智能科技有限公司 一种fpga配置文件远程升级更新方法
CN117471979A (zh) * 2023-12-27 2024-01-30 成都川哈工机器人及智能装备产业技术研究院有限公司 一种基于数字孪生平台的数据采集处理方法及系统

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114492179B (zh) * 2022-01-13 2024-09-17 工赋(青岛)科技有限公司 信息处理系统、方法、装置、设备及存储介质
CN116306323B (zh) * 2023-05-19 2023-08-08 中南大学 一种数字孪生模型的确定方法、装置、终端设备及介质
CN117893181A (zh) * 2024-01-18 2024-04-16 香港理工大学深圳研究院 一种基于数字孪生与拓展现实的多人协作制造系统及方法

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190385043A1 (en) * 2018-06-19 2019-12-19 Adobe Inc. Asynchronously training machine learning models across client devices for adaptive intelligence
CN113159327A (zh) * 2021-03-25 2021-07-23 深圳前海微众银行股份有限公司 基于联邦学习系统的模型训练方法、装置、电子设备
CN113419857A (zh) * 2021-06-24 2021-09-21 广东工业大学 一种基于边缘数字孪生关联的联邦学习方法及系统
CN113435657A (zh) * 2021-07-09 2021-09-24 清华四川能源互联网研究院 数据集成处理方法、系统、能源管理系统、电子设备及计算机可读存储介质
CN113792423A (zh) * 2021-09-04 2021-12-14 苏州特比姆智能科技有限公司 一种tpm设备管理的数字孪生行为约束方法及系统
CN114492179A (zh) * 2022-01-13 2022-05-13 工赋(青岛)科技有限公司 信息处理系统、方法、装置、设备及存储介质

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111027165B (zh) * 2019-07-19 2022-05-03 北京航空航天大学 一种基于数字孪生的动力电池组管理系统及方法
CN111538598A (zh) * 2020-04-29 2020-08-14 深圳前海微众银行股份有限公司 联邦学习建模方法、装置、设备及可读存储介质
CN113435121B (zh) * 2021-06-30 2023-08-22 平安科技（深圳）有限公司 基于联邦学习的模型训练验证方法、装置、设备及介质
CN113627085B (zh) * 2021-08-20 2024-01-09 深圳前海微众银行股份有限公司 横向联邦学习建模优化方法、设备、介质

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190385043A1 (en) * 2018-06-19 2019-12-19 Adobe Inc. Asynchronously training machine learning models across client devices for adaptive intelligence
CN113159327A (zh) * 2021-03-25 2021-07-23 深圳前海微众银行股份有限公司 基于联邦学习系统的模型训练方法、装置、电子设备
CN113419857A (zh) * 2021-06-24 2021-09-21 广东工业大学 一种基于边缘数字孪生关联的联邦学习方法及系统
CN113435657A (zh) * 2021-07-09 2021-09-24 清华四川能源互联网研究院 数据集成处理方法、系统、能源管理系统、电子设备及计算机可读存储介质
CN113792423A (zh) * 2021-09-04 2021-12-14 苏州特比姆智能科技有限公司 一种tpm设备管理的数字孪生行为约束方法及系统
CN114492179A (zh) * 2022-01-13 2022-05-13 工赋(青岛)科技有限公司 信息处理系统、方法、装置、设备及存储介质

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117111997A (zh) * 2023-10-08 2023-11-24 西安大合智能科技有限公司 一种fpga配置文件远程升级更新方法
CN117111997B (zh) * 2023-10-08 2024-03-22 西安大合智能科技有限公司 一种fpga配置文件远程升级更新方法
CN117471979A (zh) * 2023-12-27 2024-01-30 成都川哈工机器人及智能装备产业技术研究院有限公司 一种基于数字孪生平台的数据采集处理方法及系统
CN117471979B (zh) * 2023-12-27 2024-03-08 成都川哈工机器人及智能装备产业技术研究院有限公司 一种基于数字孪生平台的数据采集处理方法及系统

Also Published As

Publication number Publication date
CN114492179A (zh) 2022-05-13
CN114492179B (zh) 2024-09-17
US20240273380A1 (en) 2024-08-15

Similar Documents

Publication Publication Date Title
WO2023134184A1 (zh) 信息处理系统、方法、装置、设备及存储介质
CN113609521B (zh) 一种基于对抗训练的联邦学习隐私保护方法及系统
CN113159327B (zh) 基于联邦学习系统的模型训练方法、装置、电子设备
CN111931242B (zh) 一种数据共享方法以及应用其的计算机设备及可读存储介质
US20230078061A1 (en) Model training method and apparatus for federated learning, device, and storage medium
CN113204787B (zh) 基于区块链的联邦学习隐私保护方法、系统、设备和介质
CN110490738A (zh) 一种混合联邦学习方法及架构
CN111598254A (zh) 联邦学习建模方法、设备及可读存储介质
CN112949865B (zh) 一种基于sigma协议的联邦学习贡献度评估方法
WO2019152193A1 (en) Chain of custody in gaming platforms
CN106650495A (zh) 一种文件验证方法及装置
CN110908893A (zh) 联邦学习的沙盒机制
CN113129149A (zh) 基于区块链和安全多方计算的交易风险识别方法和装置
CN108428175A (zh) 一种基于消费记录的大数据分析方法及系统
CN111553443A (zh) 裁判文书处理模型的训练方法、训练装置和电子设备
CN112101577A (zh) 基于XGBoost的跨样本联邦学习、测试方法、系统、设备和介质
CN111865595A (zh) 一种区块链的共识方法及装置
US20230334333A1 (en) Methods, apparatuses, and systems for training model by using multiple data owners
CN117077038B (zh) 决策树模型的模型隐私、数据隐私及模型一致性保护方法
Hossan et al. A Comprehensive Analysis of Blockchain Technology and Consensus Protocols Across Multilayered Framework
CN110175283B (zh) 一种推荐模型的生成方法及装置
CN114091057B (zh) 基于模型模拟的联邦学习安全审计方法及装置
CN115860741A (zh) 区块链数据加密验证方法、装置及系统
CN113392164B (zh) 构建纵向联邦树的方法、主服务器、业务平台和系统
CN113487041A (zh) 横向联邦学习方法、装置及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22919821

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 18566999

Country of ref document: US

NENP Non-entry into the national phase

Ref country code: DE