Data Measurement Method, Apparatus, Electronic Device, and Computer-Readable Medium

Abstract

A data measurement method, device, electronic device, and computer-readable medium, one embodiment of which includes: acquiring a dataset; inputting the dataset into a pre-trained deep learning network; and outputting a processing result, wherein the deep learning network is obtained by training a sample set. The training of the deep learning network includes: acquiring identity information of the target user in response to receiving a training request from the target user; verifying the identity information to determine whether the verification is successful; and controlling a target training engine to start training in response to determining that the identity information verification is successful; determining the processing result as a measurement result; and controlling a target device with a display function to display the measurement result. The present disclosure provides a method for inputting a dataset into a pre-trained deep learning network to obtain a measurement result that meets the user's needs, thereby satisfying the user's data calculation needs and providing convenience for the user's subsequent use of the data.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the technical field of computers, and more specifically, to data measurement methods, devices, electronic devices, and computer-readable media.

Background Art

[0002] With the development of Internet technology, people have entered the era of big data. In different industries in different fields, different data are generated, and people always use the obtained data for calculation to understand the development of the industry and industrial production. Due to the huge amount of data, generally, the needs of users for data calculation are met through several programs and service software. Thereby, an efficient and easy-to-manage data measurement method is required.

Summary of the Invention

[0003] The content part of the present disclosure is used to explain the ideas in a simple form, and these ideas will be described in detail in the subsequent specific embodiment part. The content part of the present disclosure is not intended to identify an important or essential component of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0004] Some embodiments of the present disclosure provide a data measurement method, a device, an electronic device, and a computer-readable media to solve the technical problems mentioned in the above background art part.

[0005] In a first aspect, some embodiments of the present disclosure provide a data measurement method including: obtaining a data set, processing the data set to obtain a processing result, determining the processing result as a measurement result, and controlling a target device having a display function to display the measurement result.

[0006] In a second aspect, some embodiments of the present disclosure provide a data measurement device including an acquisition unit arranged to acquire a data set, a processing unit arranged to process the data set and obtain a processing result, and a display unit arranged to determine the processing result as a measurement result and control a target device having a display function to display the measurement result.

[0007] In a third aspect, some embodiments of the present disclosure include one or more processors and a storage device storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors provide an electronic device that implements the method described in the first aspect.

[0008] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium storing a computer program, which realizes the method described in the first aspect when the program is executed by a processor.

[0009] One of the above embodiments of the present disclosure has the following beneficial effects. By inputting a data set into a pre-trained deep learning network, a measurement result that meets the user's needs can be obtained, satisfying the user's demand for data calculation and providing convenience for the user's subsequent data use.

Brief Description of the Drawings

[0010] Referring to the following specific embodiments with reference to the drawings, the above and other configurations, advantages and aspects of each embodiment of the present disclosure will become more apparent. Throughout the accompanying drawings, the same or similar reference numerals indicate the same or similar elements. It should be understood that the drawings are illustrative and the elements and components are not necessarily drawn to scale.

[0011]

Figure 1

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Embodiments for Carrying Out the Invention

[0012] Hereinafter, embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, providing these embodiments is for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only used for illustrative purposes and do not limit the protection scope of the present disclosure.

[0013] It should also be noted that, for ease of explanation, the drawings only show parts related to the related invention. When there is no interference, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0014] It should be noted that concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence relationship of the functions executed by these devices, modules or units.

[0015] It should be noted that the modifications of "one" and "a plurality" mentioned in the present disclosure are illustrative and not restrictive. Those skilled in the art should understand that, unless clearly pointed out in the context, it should be understood as "one or more".

[0016] The names of messages or information exchanged between multiple devices in the embodiments of the present disclosure are used only for illustrative purposes and do not limit the scope of these messages or information.

[0017] Hereinafter, the present invention will be described in detail with reference to the drawings and examples.

[0018] FIG. 1 is a schematic diagram of an application scenario of a data measurement method according to some embodiments of the present disclosure.

[0019] In the application scenario of FIG. 1, first, the computing device 101 can obtain the dataset 102. Then, the computing device 101 can input the dataset 102 into a pre-trained deep learning network and output a processing result 103. Finally, the computing device 101 can determine the processing result 103 as the measurement result 104. In addition, the computing device 101 can control a target device having a display function to display the measurement result 104.

[0020] It should be noted that the above computing device 101 may be hardware or software. When the computing device is hardware, it may be realized as a distributed cluster composed of multiple servers or terminal devices, or may be realized as a single server or a single terminal device. When the computing device is embodied as software, it can be installed on the above-exemplified hardware device. It may be realized, for example, as multiple software or software modules for providing distributed services, or may be realized as a single software or software module. It is not specifically limited here.

[0021] As can be understood, the number of computing devices in FIG. 1 is merely exemplary. Any number of computing devices may be provided according to the actual requirements.

[0022] Continuing to refer to FIG. 2, a flow 200 of some embodiments of the data measurement method according to the present disclosure is shown. This method can be executed by the computing device 101 in FIG. 1. This data measurement method includes the following steps.

[0023] Step 201, obtaining a data set.

[0024] In some embodiments, the execution entity of the data measurement method (the computing device 101 shown in FIG. 1) can obtain the data set by a wired connection method or a wireless connection method. For example, the above execution entity can receive the data set input by the user as the above data set. Also for example, the above execution entity is connected to other electronic devices by a wired connection method or a wireless connection method, and can obtain the data set in the database of the connected electronic device as the above data set.

[0025] It should be noted that the above wireless connection method includes, but is not limited to, 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future-developed wireless connection methods.

[0026] Step 202, inputting the data set into a pre-trained deep learning network and outputting a processing result.

[0027] In some embodiments, the above execution entity can input the data set into a pre-trained deep learning network and output a processing result. Here, the input of the deep learning network may be a data set, and the output may be a processing result. By way of example, the above deep learning network may be a Recurrent Neural Network (RNN), or may be a Long Short-Term Memory networks (LSTM).

[0028] By way of example, the data set may be "flue gas temperature, flue gas flow rate, flue gas humidity, steam flow rate, and the outlet temperature of the economizer". The output processing result may also be the boiler flue gas oxygen content.

[0029] In some embodiments, the training of the deep learning network includes, in response to receiving a training request from a target user, obtaining the identity information of the target user, verifying the identity information, and determining whether the verification is passed, and in response to determining that the verification of the identity information is passed, controlling a target training engine to start training.

[0030] In step 203, the processing result is determined as a measurement result, and a target device having a display function is controlled to display the measurement result.

[0031] In some embodiments, the above execution entity can determine the above processing result as a measurement result. And the execution entity can push the measurement result to a target device having a display function and control the target device to display the measurement result.

[0032] One of the above embodiments of the present disclosure has the following beneficial effects. By inputting a data set into a pre-trained deep learning network, a measurement result that meets the user's needs can be obtained. It meets the user's need for data calculation and provides convenience for the user's subsequent data use.

[0033] Continuing to refer to FIG. 3, a flowchart 300 of some embodiments of the training of the deep learning network of the data measurement method according to the present disclosure is shown. This method can be executed by the computing device 101 in FIG. 1. This data measurement method includes the following steps.

[0034] In step 301, in response to receiving the training request of the target user, obtain the identity information of the target user.

[0035] In some embodiments, the execution entity of the data measurement method (for example, the computing device 101 shown in FIG. 1) can obtain the identity information of the target user in response to receiving the training request of the target user. Here, the training request may be a command to start training for the model. The target user may be a user who has a training request and has been verified by preset registration, authentication, etc.

[0036] In step 302, verify the identity information and determine whether the verification is passed.

[0037] In some embodiments, the execution entity can verify the identity information and determine whether the verification is passed. By way of example, the execution entity searches a pre-constructed identity information base based on the identity information and determines whether the identity information exists in the identity information base. In response to determining the existence, the execution entity can determine that the verification is successful.

[0038] In step 303, in response to determining that the verification of the identity information is passed, control the target training engine to start training.

[0039] In some embodiments, in response to determining that the verification of the identity information is passed, the execution entity can control the target training engine to start training. The training engine may be an engine that supports various algorithm selection modules and provides support for the training of the deep learning network according to the needs of different service scenarios.

[0040] Step 304: In response to detecting a selection operation on the training model in the training model library of the target user, verify the training engine and determine whether the verification is passed.

[0041] In some embodiments, in response to detecting a selection operation on the training model in the training model library of the target user, the executing entity can verify the target training engine. Here, the training model library may be a set of training models for meeting the needs of the user selected by the user. By way of example, the executing entity can perform permission verification on the target training engine and determine whether the target training engine has the permission to support the training of the training model selected by the target user.

[0042] By way of example, the training model library may be "Training Model A, Training Model B, Training Model C". The training permission of the target engine may be "Training Model A and Training Model C". If the training model selected by the target user is "Training Model B", the executing entity can determine that the verification of the target training engine fails. Conversely, the executing entity can determine that the verification of the target training engine passes.

[0043] Step 305: In response to determining that the verification of the target training engine passes, transfer the initial model to the terminal device of the target user.

[0044] In some embodiments, upon determining that the target training engine has passed the verification, the executing entity can transfer the initial model to the terminal device of the target user. Here, the initial model may be a model that is not trained or a model that does not meet a predetermined condition after being trained. The initial model may be a model having a deep neural network structure. The pre-trained feature extraction model may be a pre-trained neural network model for extracting features. This neural network model can have various existing neural network structures. For example, the neural network structure may be a Convolutional Neural Network (CNN). The initial model can employ eXtreme Gradient Boosting (XGBoost). The storage location of the initial model is also not limited in the present disclosure.

[0045] Step 306, using the obtained training sample set, train the initial model to obtain an initial model after training is completed.

[0046] In some embodiments, the execution entity can start training the initial model by using the obtained training sample set, and the training process is as follows. In step 1, a training sample is selected from the training sample set, where the training sample includes a sample data set and a sample processing result. In step 2, the execution entity can input the sample data set in the training sample into the initial model. In step 3, the output processing result is compared with the sample processing result to obtain a processing result loss value. In step 4, the execution entity can compare the processing result loss value with a preset threshold to obtain a comparison result. In step 5, based on the comparison result, it is determined whether the initial model has completed training. In step 6, in response to the completion of the training of the initial training model, the initial model is determined as the initial model after training completion. Here, the obtained training sample set may be the local data of the terminal device of the target user.

[0047] The loss value of the processing result may be a value obtained from a loss function that takes the output processing result and the corresponding sample processing result as parameters and is executed. Here, the loss function (such as the mean squared loss function, the exponential loss function, etc.) is generally used to estimate the degree of disagreement between the predicted value of the model (such as the sample processing result corresponding to the sample data set) and the true value (such as the processing result obtained in the above steps). This is a non-negative real-valued function. Generally, the smaller the loss function, the higher the robustness of the model. The loss function can be set according to actual needs. By way of example, the loss function may be the cross-entropy loss function.

[0048] In some preferred embodiments of some embodiments, in response to determining that the training of the initial model is not completed, the method further includes adjusting relevant parameters in the initial model, reselecting samples from the training sample set, using the adjusted initial model as the initial model, and continuing to execute the training step.

[0049] In some preferred embodiments of some embodiments, the initial model that has completed training can ensure continuous uploading and downloading between the terminal device and the target training engine on the premise of a compression protocol and a security protocol, and continuously iteratively update the initial model that has completed training.

[0050] Step 307, using the target training engine to integrate at least one model stored in the terminal device with the initial model that has completed training to obtain a joint training model.

[0051] In some embodiments, the execution entity can use the target training engine to integrate at least one model stored in the terminal device with the initial model that has completed training to obtain a joint training model.

[0052] In some preferred embodiments of some embodiments, in response to detecting a joint termination request from the target user, the method further includes controlling the target training engine to stop training, and storing the joint training model at the time of stopping training in the target model library. Here, the execution entity can generate an interface for the joint training model and then store the joint training model after the interface is generated in the target model library. The execution entity can store the training record and the state information during training related to the joint training model in the cloud database.

[0053] In some preferred embodiments of some embodiments, the method further includes obtaining a query interface in response to detecting a query operation of the target user, extracting historical records and status information of a model whose interface is the same as the query interface from the target model library, and controlling the target device to display the historical records and the status information. Here, the historical record may be information for each training in the model training process.

[0054] As can be seen from FIG. 3, compared with the description of some embodiments corresponding to FIG. 2, the flow 300 of the data measurement method in some embodiments corresponding to FIG. 3 embodies the step of how to train a deep learning network and obtain a co-training model. Thereby, the solution means described in these embodiments can obtain measurement results that meet the user's needs by processing the data set. It meets the user's demand for data calculation and provides convenience for the user's subsequent data use. In addition, performing measurement calculation on the data by the co-training model can greatly avoid errors during manual calculation and obtain accurate measurement results. The user can select a training model for different service scenarios, improve the utilization rate of the model, and the generated co-training model also better meets the user's needs, improving the user experience in terms of side.

[0055] Referring further to FIG. 4, as an implementation of the above method in each of the above figures, the present disclosure provides some embodiments of a data measurement device, and these device embodiments correspond to the above method embodiments of FIG. 2, and the device can be specifically applied to various electronic devices.

[0056] As shown in FIG. 4, the data measurement device 400 of some embodiments includes an acquisition unit 401, a processing unit 402, and a display unit 403. Here, the acquisition unit 401 is arranged to acquire a data set, and the processing unit 402 is arranged to input the data set into a pre-trained deep learning network and output a processing result. Here, the deep learning network is obtained by training a sample set. The training of the deep learning network includes, in response to receiving a training request from a target user, acquiring identity information of the target user, verifying the identity information, and determining whether the verification is passed. In response to determining that the verification of the identity information is passed, controlling a target training engine to start training. The display unit 403 is arranged to determine the processing result as a measurement result and control a target device having a display function to display the measurement result.

[0057] In some preferred embodiments of some embodiments, the training of the deep learning network includes, in response to detecting a selection operation on a training model in a training model library of the target user, verifying the target training engine and determining whether the verification is passed. In response to determining that the verification of the target training engine is passed, transferring an initial model to a terminal device of the target user, using the acquired training sample set to train the initial model, and obtaining an initial model after training is completed. Using the target training engine to integrate at least one model stored in the terminal device with the initial model after training is completed to obtain a co-training model.

[0058] In some preferred embodiments of some embodiments, the training samples in the training sample set include a sample data set and a sample processing result, and the deep learning network is obtained by training with the sample data set as an input and the sample processing result as a desired output.

[0059] In some preferred embodiments of some embodiments, in response to further detecting a joint end request of the target user, the data measurement device 400 is configured to control the target training engine to stop training, and store the joint training model at the time of stopping training in the target model library.

[0060] In some preferred embodiments of some embodiments, in response to further detecting a query operation of the target user, the data measurement device 400 obtains a query interface, extracts a history record and status information of a model in which the interface is the same as the query interface from the target model library, and is configured to control the target device to display the history record and the status information.

[0061] As can be understood, each unit described in the device 400 corresponds to each step in the method described with reference to FIG. 2. Accordingly, the operations, features, and beneficial effects generated described in the above method are similarly applicable to the device 400 and the units included therein, and the description thereof is omitted here.

[0062] Referring to FIG. 5 below, a schematic structural diagram of an electronic device (for example, the computing device 101 in FIG. 1) 500 for implementing some embodiments of the present disclosure is shown. The server shown in FIG. 5 is only an example and should not impose any restrictions on the functions and usage scopes of the embodiments of the present disclosure.

[0063] As shown in FIG. 5, the electronic device 500 can include a processing device (e.g., a central processing unit, a graphics processor, etc.) 501, which can execute various appropriate operations and processes based on a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. Various programs and data necessary for the operation of the electronic device 500 are further stored in the RAM 503. The processing device 501, the ROM 502, and the RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0064] Generally, the following devices can be connected to the I / O interface 505. For example, it includes input devices 506 such as a touch screen, a touch panel, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc., and includes output devices 507 such as a liquid crystal display (LCD), a speaker, a vibrator, etc., and includes a storage device 508 such as a magnetic tape, a hard disk, etc., and a communication device 509. The communication device 509 can allow the electronic device 500 to communicate wirelessly or wired with other devices to exchange data. FIG. 5 shows an electronic device 500 having various devices, but it should be understood that it is not required to implement or include all the shown devices. Alternatively, it may be implemented or include more or less devices. Each block shown in FIG. 5 may represent one device or, if necessary, a plurality of devices.

[0065] In particular, according to some embodiments of the present disclosure, the process described with reference to the above flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such some embodiments, the computer program can be downloaded and installed from a network by the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above functions limited to the methods of some embodiments of the present disclosure are executed.

[0066] It should be noted that the computer-readable media in some embodiments of the present disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium include, but are not limited to, electrical connections having one or more conductors, portable computer magnetic disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact magnetic disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium that includes or stores a program, and the program may be used by or in combination with a command execution system, apparatus, or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal included in a baseband or transferred as part of a carrier, in which computer-readable program code is carried. Such a propagated data signal may adopt various forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may be any computer-readable medium other than the computer-readable storage medium, and the computer-readable signal medium can transmit, propagate, or transfer a program for use by or in combination with a command execution system, apparatus, or device. The program code included in the computer-readable medium can be transferred via any suitable medium, including, but not limited to, electric wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0067] In some embodiments, the client and the server can communicate using any network protocol known currently or developed in the future, such as HTTP (Hyper Text Transfer Protocol), and can interconnect with digital data communication of any format or medium (e.g., a communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), and any network known currently or developed in the future.

[0068] The computer-readable medium may be included in the above device, may not be installed in the electronic device, and may exist independently. One or more programs are carried on the computer-readable medium. When the one or more programs are executed by the electronic device, the electronic device is caused to obtain a data set, input the data set into a pre-trained deep learning network, and output a processing result. Here, the deep learning network is obtained by training a sample set, and the training of the deep learning network includes obtaining the identity information of the target user in response to receiving the training request of the target user, verifying the identity information, determining whether the verification is passed, controlling the target training engine to start training in response to determining that the verification of the identity information is passed, determining the processing result as a measurement result, and controlling a target device having a display function to display the measurement result.

[0069] Computer program code can be written in one or more programming languages or combinations thereof to execute the operations of some embodiments of the present disclosure. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on a user computer, partially on a user computer, executed as an independent software package, partially on a user computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or may be connected to an external computer (e.g., connected via the Internet by an Internet service provider).

[0070] Flowcharts and block diagrams in the drawings illustrate the possible system architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram can represent a module, a program segment, or a portion of code, and the module, program segment, or portion of code includes executable instructions for implementing one or more predetermined logical functions. It should be noted that in some alternative implementations, the functions marked in the blocks may occur in an order different from the order marked in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, which is determined by the relevant functions. It should be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a system of dedicated hardware for performing a predetermined function or operation, or may be implemented by a combination of dedicated hardware and computer commands.

[0071] The units described in some embodiments of the present disclosure may be implemented in software or in hardware. The described units may be provided in a processor. For example, the processor may be described as including an acquisition unit, a processing unit, and a display unit. Here, the names of these units do not limit the unit itself in some cases. For example, the acquisition unit may be further described as "a unit for acquiring a data set".

[0072] The functions described above in the text may be at least partially executed by one or more hardware logic units. For example, without limitation, exemplary hardware logic units that can be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0073] The above description is only for some preferred embodiments of the present disclosure and the applied technical principles. As will be understood by those skilled in the art, the scope of the invention according to the embodiments of the present disclosure is not limited to the technical solutions formed by specific combinations of the above technical features, and at the same time, without departing from the concept of the above invention, other technical solutions formed by arbitrarily combining the above technical features or their equivalent features should be included. For example, the above features are those obtained by mutually replacing with technical features having similar functions (but not limited thereto) disclosed in the embodiments of the present disclosure.

Claims

1. Obtaining a dataset; Inputting the dataset into a pre-trained deep learning network and outputting a processing result; Determining the processing result as a measurement result and controlling a target device having a display function to display the measurement result, wherein the deep learning network is obtained by training a sample set, and the training of the deep learning network includes: In response to receiving a training request from a target user, obtaining identity information of the target user; Verifying the identity information and determining whether the verification is passed; In response to determining that the verification of the identity information is passed, controlling a target training engine to start training; In response to detecting a selection operation on a training model in the training model library of the target user, verifying the target training engine and determining whether the verification is passed; In response to determining that the verification of the target training engine is passed, transferring an initial model to the terminal device of the target user; Using the obtained training sample set to train the initial model and obtaining an initial model after training is completed; Using the target training engine to integrate at least one model stored in the terminal device with the initial model after training is completed to obtain a joint training model. A data measurement method characterized by including the above.

2. The training model library includes multiple types of training models. The target training engine has the authority to train one or more predetermined training models. Verifying the target training engine and determining whether the verification is passed is to determine whether the training model selected by the target user is a training model that the target training engine has the authority to train. If it is a training model that the target training engine does not have the authority to train, it is determined that the verification of the target training engine fails. The method according to claim 1.

3. The training sample in the training sample set includes a sample data set and a sample processing result, and the deep learning network is obtained by training with the sample data set as an input and the sample processing result as a desired output. The method according to claim 2, wherein.

4. In response to detecting a joint end request of the target user, controlling the target training engine to stop training, and further storing a joint training model at the time of stopping training in a target model library. The method according to claim 1, characterized by further comprising.

5. The method is In response to detecting a query operation of the target user, obtaining a query interface, Extracting a history record and status information of a model whose interface is the same as the query interface from the target model library, and controlling the target device to display the history record and the status information. The method according to claim 4, characterized by further comprising.

6. An acquisition unit arranged to acquire a data set, A processing unit arranged to input the data set into a pre-trained deep learning network and output a processing result A display unit arranged to determine the processing result as a measurement result and control a target device having a display function to display the measurement result, and The deep learning network is obtained by training a sample set, and the training of the deep learning network is In response to receiving a training request of the target user, obtaining identity information of the target user, Verifying the identity information and determining whether the verification is passed, In response to determining that the verification of the identity information is passed, controlling the target training engine to start training, In response to detecting a selection operation on a training model in the training model library of the target user, verifying the target training engine and determining whether the verification is passed, In response to determining that the target training engine has passed the verification, transferring the initial model to the terminal device of the target user; Using the obtained training sample set to train the initial model and obtaining an initial model after training is completed; Using the target training engine to integrate at least one model stored in the terminal device with the initial model after training is completed to obtain a co-training model A data measurement device characterized by including the above.

7. One or more processors; A storage device storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to claim 1. An electronic device characterized by this.

8. A computer-readable medium storing a computer program, characterized in that the computer program implements the method according to claim 1 when executed by a processor. ​

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