A processing method, device and system

Abstract

The present application provides a processing method, apparatus and system. The method includes: a first processing unit obtains first monitoring data of a first sensing unit, and the first sensing unit monitors target parameters provided by a monitoring power supply to a target processing unit, where the target parameters are used to indicate the performance of the target processing unit in an operating state; the first processing unit obtains second monitoring data of an intelligent engine, and the operating intelligent engine manages a target program currently running; the first processing unit matches the first monitoring data and the second monitoring data with an intelligent scenario model to determine the usage scenario of the electronic device; and the first processing unit adjusts the target parameters of the target processing unit based on the usage scenario.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a processing method, apparatus, and system. Background Art

[0002] In order to meet the different usage requirements of users, current electronic devices are provided with multiple performance modes. Under different performance modes, the operating performance of the hardware of the electronic device is different.

[0003] However, currently, the performance mode needs to be manually switched by the user, so that the parameters of the hardware of the electronic device are adjusted with the switching of the performance mode, resulting in low accuracy and flexibility in the adjustment of the parameters of the hardware of the electronic device. Summary of the Invention

[0004] This application provides the following technical solutions:

[0005] On the one hand, this application provides a processing method, the method includes:

[0006] A first processing unit obtains first monitoring data of a first sensing unit, and the first sensing unit monitors target parameters provided by a power supply to a target processing unit, and the target parameters are used to indicate the performance of the target processing unit in an operating state;

[0007] The first processing unit obtains second monitoring data of an intelligent engine, and the intelligent engine in an operating state manages a target program currently running;

[0008] The first processing unit matches the first monitoring data and the second monitoring data with an intelligent scenario model to determine the usage scenario of the electronic device;

[0009] The first processing unit adjusts the target parameters of the target processing unit based on the usage scenario.

[0010] The method further includes:

[0011] The first processing unit sets management software for controlling the target processing unit through the intelligent engine.

[0012] The intelligent scenario model is an intelligent neural network model, and the first processing unit is a chip with computing power.

[0013] The method further includes:

[0014] If the accuracy of the usage scenario of the electronic device determined by the intelligent neural network model does not meet the accuracy threshold, then control the usage scenario of the electronic device to be a general usage scenario.

[0015] The method further includes:

[0016] The intelligent engine obtains first information of the electronic device, and the first information represents parameter information of the usage status of the electronic device in the general usage scenario;

[0017] The intelligent engine sends the first information to the cloud server, so that the first server obtains the first information from the cloud server, updates the intelligent neural network model based on the first information, and sends the updated intelligent neural network model to the cloud server for storage.

[0018] The intelligent neural network model is obtained in the following manner, including:

[0019] The intelligent engine sends a first request to the cloud server and obtains neural network model version information and a neural network model download address returned by the cloud server in response to the first request;

[0020] If the neural network model version information indicates that the current neural network model in the cloud server is newer than the neural network model of the electronic device, the intelligent engine obtains the current neural network model in the cloud server based on the neural network model download address, and uses the current neural network model as the intelligent neural network model.

[0021] Another aspect of this application provides a processing device, including:

[0022] A first acquisition module, configured to acquire first monitoring data of a first sensing unit, where the first sensing unit monitors target parameters provided by a monitoring power supply to a target processing unit, and the target parameters are used to indicate the performance of the target processing unit in an operating state;

[0023] A second acquisition module, configured to acquire second monitoring data of the intelligent engine, and the intelligent engine in an operating state manages a target program currently running;

[0024] A determination module, configured to determine the usage scenario of the electronic device by matching the first monitoring data and the second monitoring data with an intelligent scenario model;

[0025] An adjustment module, configured to adjust the target parameters of the target processing unit based on the usage scenario.

[0026] A third aspect of this application provides a processing system, including: a first processing unit, a first sensing unit, and an intelligent engine;

[0027] The first sensing unit is configured to monitor target parameters provided by a monitoring power supply to a target processing unit, and the target parameters are used to indicate the performance of the target processing unit in an operating state;

[0028] The intelligent engine is used to manage the target program currently running when in the running state;

[0029] The first processing unit is used to execute the processing method described in any one of the above.

[0030] The processing system further includes: a cloud server and a first server;

[0031] The intelligent engine is further used to:

[0032] Obtain first information of the electronic device, where the first information represents parameter information of the usage status of the electronic device in the general usage scenario;

[0033] Send the first information to the cloud server, so that the first server obtains the first information from the cloud server, updates the intelligent neural network model based on the first information, and sends the updated intelligent neural network model to the cloud server for storage.

[0034] The intelligent engine is further used to:

[0035] Send a first request to the cloud server and obtain the neural network model version information and the neural network model download address returned by the cloud server in response to the first request;

[0036] If the neural network model version information indicates that the current neural network model in the cloud server is newer than the neural network model of the electronic device, then based on the neural network model download address, obtain the current neural network model in the cloud server, and use the current neural network model as the intelligent neural network model.

[0037] In this application, the first processing unit determines the usage scenario of the electronic device by obtaining the first monitoring data of the first sensing unit and the second monitoring data of the intelligent engine, and matching the first monitoring data and the second monitoring data with the intelligent scenario model, so as to realize that the first processing unit determines the usage scenario of the electronic device based on the data related to the target processing unit and the data related to the target program currently running of the electronic device, ensuring the accuracy of the determination of the usage scenario of the electronic device. On this basis, the first processing unit adjusts the target parameters of the target processing unit based on the usage scenario, improving the accuracy and flexibility of the adjustment of the target parameters. Description of the Drawings

[0038] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0039] Figure 1 It is a schematic flowchart of a processing method provided in Embodiment 1 of the present application;

[0040] Figure 2 It is a schematic diagram of an interaction scenario between a first sensing unit and a first processing unit provided by the present application;

[0041] Figure 3 It is another schematic diagram of an interaction scenario between a first sensing unit and a first processing unit provided by the present application;

[0042] Figure 4 It is a schematic structural diagram of an intelligent neural network model provided by the present application;

[0043] Figure 5 It is a schematic diagram of a scenario for obtaining an intelligent neural network model provided by the present application;

[0044] Figure 6 It is a schematic diagram of a scenario for adjusting the target parameters of a target processing unit provided by the present application;

[0045] Figure 7 It is a schematic flowchart of a processing method provided in Embodiment 2 of the present application;

[0046] Figure 8 It is a schematic flowchart of a processing method provided in Embodiment 3 of the present application;

[0047] Figure 9 It is a schematic flowchart of a processing method provided in Embodiment 4 of the present application;

[0048] Figure 10 It is a schematic diagram of an implementation scenario of a processing method provided in Embodiment 4 of the present application;

[0049] Figure 11 It is a schematic flowchart of a processing method provided in Embodiment 5 of the present application;

[0050] Figure 12 It is a schematic diagram of an implementation scenario of a processing method provided in Embodiment 5 of the present application;

[0051] Figure 13 It is a schematic logical structure diagram of a processing device provided by the present application. Detailed implementation manners

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0053] In the prior art, although users can manually switch the performance mode, by collecting data on the actual usage habits of users, it is found that the proportion of the number of times users manually perform performance switching is very small, and more users still stay in the default performance mode set at the factory of the electronic device.

[0054] In the case where the proportion of the number of times users manually perform performance switching is very small, the electronic device can rarely be used in a situation where its performance mode matches the usage scenario, that is, the hardware of the electronic device can rarely operate in a situation where its parameters match the usage scenario, resulting in poor usage effects of the electronic device and a poor user experience.

[0055] To solve the above problems, the present application proposes a brand-new processing method. Next, the processing method provided by the present application will be introduced.

[0056] Referring to Figure 1 , which is a schematic flowchart of a processing method provided in Embodiment 1 of the present application. The processing method provided by the present application can be applied to an electronic device. The present application does not limit the product type of the electronic device. As Figure 1 shown, the method may include but is not limited to the following steps:

[0057] Step S101: The first processing unit obtains the first monitoring data of the first sensing unit. The first sensing unit monitors the target parameters provided by the power supply to the target processing unit, and the target parameters are used to indicate the performance of the target processing unit in the operating state.

[0058] In this embodiment, as Figure 2 shown, the first sensing unit can monitor the target parameters provided by the power supply to the target processing unit and use the target parameters as the first monitoring data. For example, the first sensing unit can monitor at least one or more of the voltage, current, and power provided by the power supply to the target processing unit, and use at least one or more of the voltage, current, and power provided by the power supply to the target processing unit as the first monitoring data. Correspondingly, the first processing unit obtains the target parameters provided by the power supply to the target processing unit monitored by the first sensing unit.

[0059] As Figure 3As shown, the first sensing unit can also monitor the target parameters provided by the power supply to the target processing unit, determine the data to be used based on the target parameters, and use the target parameters and the data to be used as the first monitoring data. For example, the target parameters can include voltage and current. Based on the voltage and current, power is calculated, and the voltage, current, and power are used as the first monitoring data. Accordingly, the first processing unit obtains the target parameters provided by the power supply to the target processing unit monitored by the first sensing unit and the data to be used determined based on the target parameters.

[0060] In this embodiment, the target processing unit can include, but is not limited to, at least one or more of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and an SSD (Solid State Disk).

[0061] The first processing unit can be, but is not limited to, a chip with computing capabilities. The type of the chip with computing capabilities is not limited in this application.

[0062] The first sensing unit can include, but is not limited to, a power sensor.

[0063] Step S102: The first processing unit obtains the second monitoring data of the intelligent engine, and the intelligent engine in the running state manages the currently running target program.

[0064] The intelligent engine in the running state manages the currently running target program, which can include, but is not limited to:

[0065] The intelligent engine in the running state monitors the data related to the currently running target program and manages the currently running target program based on the data related to the currently running target program.

[0066] The intelligent engine in the running state can, but is not limited to, use the data related to the currently running target program as the second monitoring data.

[0067] The currently running target program can include, but is not limited to, at least one or more of an application program, a service process in an operating system, and a management software for controlling the target processing unit.

[0068] For the embodiment where the target processing unit is a CPU or a GPU, the management software for controlling the target processing unit can, but is not limited to, be used to control the CPU or GPU to switch from the first frequency to the second frequency, and the second frequency is higher than the first frequency.

[0069] The data related to the application program can include, but is not limited to, the user interaction information and network traffic information corresponding to the application program.

[0070] The service processes in the operating system may include: operating system processes and / or user processes. An operating system process can be understood as a process for completing the services provided by the operating system. A user process may include: a process corresponding to an application program, and / or, a process corresponding to the management software for controlling the target processing unit.

[0071] The data related to the service processes in the operating system may at least include: the utilization rate of the target processing unit corresponding to the operating system process, and / or, the utilization rate of the target processing unit corresponding to the user process.

[0072] The data related to the management software for controlling the target processing unit may include: the network traffic data corresponding to the management software for controlling the target processing unit in the running state, and / or, the running data of the management software for controlling the target processing unit in the running state.

[0073] The manner in which the intelligent engine in the running state manages the currently running target program is not limited in this application. For example, based on the data related to the currently running target program, it can be determined that the application program is in the background running state, and the duration of the background running state exceeds the set duration threshold, and the application program in the background running state is terminated.

[0074] Step S103: The first processing unit matches the first monitoring data and the second monitoring data with the intelligent scenario model to determine the usage scenario of the electronic device.

[0075] This step may include, but is not limited to:

[0076] S1031: Find the usage scenario of the electronic device corresponding to the first monitoring data and the second monitoring data in the intelligent scenario model.

[0077] Among them, the intelligent scenario model may contain the correspondence between the monitoring data and the usage scenario. Correspondingly, finding the usage scenario of the electronic device corresponding to the first monitoring data and the second monitoring data in the intelligent scenario model may include: finding the monitoring data corresponding to the first monitoring data and the second monitoring data in the intelligent scenario model, and determining the usage scenario corresponding to the monitoring data in the intelligent scenario model as the usage scenario of the electronic device.

[0078] Certainly, the intelligent scenario model may also be an intelligent neural network model. Correspondingly, this step may also include, but is not limited to:

[0079] S1032: Input the first monitoring data and the second monitoring data into the intelligent neural network model to obtain the usage scenario of the electronic device determined by the intelligent neural network model.

[0080] Such as Figure 4As shown, the intelligent neural network model may include, but is not limited to: an input layer, a Relu layer, a Hidden layer, a Softmax layer, and an output layer. The first monitoring data and the second monitoring data are input into the input layer, and through the calculations of the Relu layer, the Hidden layer, and the Softmax layer, the usage scenario of the electronic device is obtained, and the usage scenario of the electronic device is output to the output layer.

[0081] It should be noted that Figure 4 in the output layer Y1, Y2,... Y n indicates that the intelligent neural network model has the ability to determine multiple usage scenarios. However, at a certain moment, the intelligent neural network model only determines one usage scenario and outputs it.

[0082] It should be noted that Figure 4 is only an example of the intelligent neural network model and does not serve as a limitation to the intelligent neural network model.

[0083] In this embodiment, the intelligent neural network model can be obtained by the electronic device from the cloud server, and the intelligent neural network model in the cloud server is obtained from the first server.

[0084] Among them, as Figure 5 shown, the electronic device obtaining the intelligent neural network model from the cloud server may include:

[0085] S10321. The intelligent engine sends a first request to the cloud server and obtains the neural network model version information and the neural network model download address returned by the cloud server in response to the first request;

[0086] S10322. If the neural network model version information indicates that the current neural network model in the cloud server is newer than the intelligent neural network model of the electronic device, the intelligent engine obtains the current neural network model in the cloud server based on the neural network model download address, and replaces the intelligent neural network model of the electronic device with the current neural network model in the cloud server.

[0087] The intelligent engine stores the current neural network model in the cloud server in the first processing unit.

[0088] Of course, if the neural network model version information indicates that the current neural network model in the cloud service is the same as the intelligent neural network model of the electronic device, the electronic device continues to use the intelligent neural network model of the electronic device.

[0089] The first server can determine the intelligent neural network model in the following manner:

[0090] S10323. Obtain the monitoring data of the electronic device and the first usage scenario label annotated for the monitoring data from the cloud server. The monitoring data includes the first historical monitoring data of the first sensing unit and the second historical monitoring data of the intelligent engine.

[0091] The first historical monitoring data is determined from the target parameters provided by the monitoring power supply to the target processing unit by the first sensing unit at a moment before the current moment. The target parameters are used to indicate the performance of the target processing unit in the operating state; the second historical monitoring data is obtained by the intelligent engine in the operating state through managing and running the target program at a moment before the current moment.

[0092] S10324. The first server uses the monitoring data and the first usage scenario label annotated for the monitoring data as training data to train a neural network model and obtain an intelligent neural network model.

[0093] The usage scenarios of the electronic device may include but are not limited to: idle scenario, office scenario, online meeting scenario, web browsing scenario, creation scenario or game scenario.

[0094] The idle scenario can be understood as: the operating system enters the desktop, but the user does not perform any operations; the office scenario can be understood as: the scenario of using the electronic device for office work; the online meeting scenario can be understood as: the scenario of using the electronic device for online meetings; the web browsing scenario can be understood as: the scenario of using the electronic device for web browsing; the creation scenario can be understood as: the scenario of using the electronic device for digital content creation, image creation or video creation, etc.; the game scenario can be understood as: the scenario of using the electronic device to run games.

[0095] Step S104. The first processing unit adjusts the target parameters of the target processing unit based on the usage scenario.

[0096] The first processing unit adjusting the target parameters of the target processing unit based on the usage scenario may include but are not limited to:

[0097] The first processing unit sends the usage scenario to the BIOS (Basic Input Output System) so that the BIOS determines the first parameter to be used corresponding to the usage scenario of the target processing unit and adjusts the target parameters of the target processing unit to the first parameter to be used.

[0098] It can be understood that the target parameters of the target processing unit are adjusted based on the usage scenario, so that the target processing unit in the running state can meet the usage requirements of the application corresponding to the usage scenario. Moreover, a usage scenario can correspond to one or more applications, and the performance requirements of the applications corresponding to different usage scenarios for the target processing unit are different. Accordingly, the adjustment of the target parameters of the target processing unit based on the usage scenario is also different. For example, as Figure 6 shown in part (a) of Figure 6 , the usage scenario of the electronic device determined by the first processing unit at the first moment is the creation scenario. Based on the creation scenario, the power of the target processing unit can be adjusted to the first power to be used, so that the target processing unit can meet the usage requirements of the digital content creation program, the image creation program, and the audio-visual creation program corresponding to the creation scenario; as Figure 6 shown in part (b) of Figure 6 , the usage scenario of the electronic device determined by the first processing unit at the second moment is the office scenario. Based on the office scenario, the power of the target processing unit can be adjusted to the second power to be used, so that the target processing unit in the running state can meet the usage requirements of office software (such as Excel, Word, and PPT, etc.) corresponding to the office scenario.

[0099] In this embodiment, the first processing unit determines the usage scenario of the electronic device by obtaining the first monitoring data of the first sensing unit and the second monitoring data of the intelligent engine, and matching the first monitoring data and the second monitoring data with the intelligent scenario model. The first processing unit determines the usage scenario of the electronic device based on the data related to the target processing unit and the data related to the target program currently running on the electronic device, ensuring the accuracy of determining the usage scenario of the electronic device. On this basis, the first processing unit adjusts the target parameters of the target processing unit based on the usage scenario, improving the accuracy and flexibility of the target parameter adjustment and enhancing the user experience.

[0100] As another optional embodiment of the present application, referring to Figure 7 , which is a schematic flowchart of Embodiment 2 of a processing method provided by the present application. This embodiment is mainly an extended solution to the processing method described in the above Embodiment 1. The method may include but is not limited to the following steps:

[0101] Step S201: The first processing unit obtains the first monitoring data of the first sensing unit. The first sensing unit monitors the target parameters provided by the power supply to the target processing unit, and the target parameters are used to indicate the performance of the target processing unit in the running state.

[0102] Step S202: The first processing unit obtains the second monitoring data of the intelligent engine, and the intelligent engine in the running state manages the target program currently running.

[0103] Step S203: The first processing unit matches the first monitoring data and the second monitoring data with the intelligent scenario model to determine the usage scenario of the electronic device.

[0104] Step S204: The first processing unit adjusts the target parameters of the target processing unit based on the usage scenario.

[0105] For the detailed processes of Steps S201 - S204, reference can be made to the relevant descriptions of Steps S101 - S104 in Embodiment 1, which will not be elaborated here.

[0106] Step S205: The first processing unit sets the management software for controlling the target processing unit through the intelligent engine.

[0107] This step may include but is not limited to:

[0108] S2051: The first processing unit obtains the operation status data of the management software for controlling the target processing unit;

[0109] S2052: The first processing unit adjusts the operation status of the management software for controlling the target processing unit through the intelligent engine based on the usage scenario and the operation status data of the management software for controlling the target processing unit.

[0110] For example, if the target processing unit is the CPU and the usage scenario is the office scenario, and if the operation status data of the management software for controlling the CPU indicates that the operation status of the management software for controlling the CPU is to control the CPU to switch from the first frequency to the second frequency, and the second frequency is higher than the first frequency. The first processing unit determines based on the office scenario that the CPU does not need to run at a high frequency, and then adjusts the operation status of the management software for controlling the CPU to control the CPU to switch from the second frequency to the first frequency through the intelligent engine.

[0111] In this embodiment, the first processing unit obtains the first monitoring data of the first sensing unit and the second monitoring data of the intelligent engine, and matches the first monitoring data and the second monitoring data with the intelligent scenario model to determine the usage scenario of the electronic device, realizing that the first processing unit determines the usage scenario of the electronic device based on the data related to the target processing unit and the data related to the target program currently running on the electronic device, ensuring the accuracy of determining the usage scenario of the electronic device. On this basis, the first processing unit adjusts the target parameters of the target processing unit based on the usage scenario, improving the accuracy and flexibility of target parameter adjustment and enhancing the user experience.

[0112] Moreover, on the basis of adjusting the target parameters of the target processing unit, the first processing unit can also set the management software for controlling the target processing unit through the intelligent engine, further improving the usage effect of the electronic device and further enhancing the user experience.

[0113] As another alternative embodiment of the present application, refer to Figure 8 , which is a schematic flowchart of Embodiment 3 of a processing method provided by the present application. This embodiment is mainly an extended solution to the processing method described in Embodiment 1 above. The method may include but is not limited to the following steps:

[0114] Step S301: The first processing unit obtains the first monitoring data of the first sensing unit. The first sensing unit monitors the target parameters provided by the monitoring power supply to the target processing unit, and the target parameters are used to indicate the performance of the target processing unit in the running state.

[0115] Step S302: The first processing unit obtains the second monitoring data of the intelligent engine. The intelligent engine in the running state manages the currently running target program.

[0116] For the detailed processes of Steps S301 - S302, reference can be made to the relevant introductions of Steps S101 - S102 in Embodiment 1, which will not be elaborated here.

[0117] Step S303: The first processing unit inputs the first monitoring data and the second monitoring data into the intelligent neural network model to obtain the usage scenario of the electronic device determined by the intelligent neural network model.

[0118] Step S303 is a specific implementation manner of Step S103 in Embodiment 1.

[0119] Step S304: If the accuracy of the usage scenario of the electronic device determined by the intelligent neural network model does not meet the accuracy threshold, control the usage scenario of the electronic device to be a general usage scenario.

[0120] The situation where the accuracy of the usage scenario of the electronic device determined by the intelligent neural network model does not meet the accuracy threshold may include but is not limited to:

[0121] S3041: Obtain the reference usage scenario of the electronic device that the intelligent neural network model can determine.

[0122] Specifically, the training data of the intelligent neural network model can be obtained, and the usage scenario of the electronic device corresponding to the monitoring data in the training data is determined as the reference usage scenario of the electronic device.

[0123] S3042: If it is determined that the usage scenario of the electronic device determined by the intelligent neural network model does not belong to the reference usage scenario of the electronic device, it is determined that the accuracy of the usage scenario of the electronic device determined by the intelligent neural network model does not meet the accuracy threshold.

[0124] It can be understood that during the use of an electronic device, certain events in the electronic device (for example, the event of adding a new application program in the electronic device, or multiple application programs running simultaneously in the electronic device and the data of the multiple simultaneously running application programs not being monitored) may cause the first monitoring data and the second monitoring data obtained to be unprocessed by the intelligent neural network model, and further cause the usage scenario of the electronic device determined by the intelligent neural network model not to belong to the reference usage scenario of the electronic device.

[0125] Step S305: The first processing unit adjusts the target parameters of the target processing unit based on the general usage scenario.

[0126] In this embodiment, the first processing unit may send the general usage scenario to the BIOS so that the BIOS determines the second parameter to be used corresponding to the general usage scenario of the target processing unit and adjusts the target parameters of the target processing unit to the second parameter to be used.

[0127] It should be noted that the second parameter to be used corresponding to the general usage scenario can at least ensure that the target processing unit in the running state can meet the usage requirements of at least some application programs in the electronic device.

[0128] Step S305 is a specific implementation manner of step S104 in Embodiment 1.

[0129] In this embodiment, the first processing unit obtains the first monitoring data of the first sensing unit and the second monitoring data of the intelligent engine, inputs the first monitoring data and the second monitoring data into the intelligent neural network model to obtain the usage scenario of the electronic device determined by the intelligent neural network model. If the accuracy of the usage scenario of the electronic device determined by the intelligent neural network model does not meet the accuracy threshold, it controls the usage scenario of the electronic device to be the general usage scenario and adjusts the target parameters of the target processing unit based on the general usage scenario, reducing the error of the target parameter adjustment and ensuring the user experience.

[0130] As another alternative embodiment of the present application, refer to Figure 9 , which is a schematic flowchart of Embodiment 4 of a processing method provided by the present application. This embodiment is mainly an extended solution to the processing method described in the above Embodiment 3. The method may include but is not limited to the following steps:

[0131] Step S401: The first processing unit obtains the first monitoring data of the first sensing unit. The first sensing unit monitors the target parameters provided by the power supply to the target processing unit, and the target parameters are used to indicate the performance of the target processing unit in the running state.

[0132] Step S402: The first processing unit obtains the second monitoring data of the intelligent engine, and the intelligent engine in the running state manages the target program currently running.

[0133] Step S403: The first processing unit inputs the first monitoring data and the second monitoring data into the intelligent neural network model to obtain the usage scenario of the electronic device determined by the intelligent neural network model.

[0134] Step S404: If the accuracy of the usage scenario of the electronic device determined by the intelligent neural network model does not meet the accuracy threshold, then control the usage scenario of the electronic device to be the general usage scenario.

[0135] Step S405: The first processing unit adjusts the target parameters of the target processing unit based on the general usage scenario.

[0136] For the detailed processes of Steps S401 - S405, reference can be made to the relevant descriptions of Steps S301 - S305 in Embodiment 3, which will not be elaborated here.

[0137] Step S406: The intelligent engine obtains the first information of the electronic device, and the first information represents the parameter information of the usage status of the electronic device in the general usage scenario.

[0138] The intelligent engine obtaining the first information of the electronic device may include but is not limited to:

[0139] S4061: The intelligent engine obtains the third monitoring data obtained by the first sensing unit by monitoring the target parameters of the target processing unit of the electronic device supplied by the power supply in the general usage scenario.

[0140] For the specific implementation of obtaining the third monitoring data by monitoring the target parameters of the target processing unit of the electronic device supplied by the power supply in the general usage scenario, reference can be made to the relevant descriptions of the first sensing unit obtaining the first monitoring data in Embodiment 1, which will not be elaborated here.

[0141] S4062: The intelligent engine determines the fourth monitoring data by managing the target program running in the electronic device in the general usage scenario.

[0142] For the specific implementation of the intelligent engine determining the fourth monitoring data, reference can be made to the relevant descriptions of the intelligent engine determining the second monitoring data in Step S102 of Embodiment 1, which will not be elaborated here.

[0143] The first information includes the third monitoring data and the fourth monitoring data.

[0144] Step S407: The intelligent engine sends the first information to the cloud server, so that the first server can obtain the first information from the cloud server, update the intelligent neural network model based on the first information, and send the updated intelligent neural network model to the cloud server for storage.

[0145] Updating the intelligent neural network model based on the first information may include:

[0146] S4071: The first server obtains the second usage scenario label annotated for the first information;

[0147] The second usage scenario label represents the usage scenario of the electronic device corresponding to the first information.

[0148] S4072: Update the intelligent neural network model based on the first information and the second usage scenario label annotated for the first information to obtain the updated intelligent neural network model.

[0149] It can be understood that, on the basis of having the same capabilities as the intelligent neural network model, the updated intelligent neural network model also has the ability to accurately determine the usage scenario represented by the second usage scenario label.

[0150] In this embodiment, as Figure 10 shown, the first processing unit inputs the first monitoring data obtained from the first sensing unit and the second monitoring data obtained from the intelligent engine into the intelligent neural network model to obtain the usage scenario of the electronic device determined by the intelligent neural network model. If the accuracy of the usage scenario of the electronic device determined by the intelligent neural network model does not meet the accuracy threshold, the usage scenario of the electronic device is controlled to be the general usage scenario, and the target parameters of the target processing unit are adjusted based on the general usage scenario. The intelligent engine obtains the third monitoring data of the first sensing unit and manages the target program running in the electronic device under the general usage scenario to obtain the fourth monitoring data, and sends the first information including the third monitoring data and the fourth monitoring data to the cloud server, so that the first server can obtain the first information from the cloud server, update the intelligent neural network model based on the first information to improve the accuracy of the intelligent neural network model in determining the usage scenario, and send the updated intelligent neural network model to the cloud server for storage, so that the intelligent engine can obtain it from the cloud server and use it for the first processing unit, further improving the accuracy of the target parameter adjustment and improving the user experience.

[0151] As another optional embodiment of the present application, referring to Figure 11 , it is a schematic flowchart of Embodiment 5 of a processing method provided by the present application. This embodiment is mainly an extended solution to the processing method described in the above Embodiment 3. The method may include but is not limited to the following steps:

[0152] Step S501: The first processing unit obtains the first monitoring data of the first sensing unit. The first sensing unit monitors the target parameters provided by the power supply to the target processing unit, and the target parameters are used to indicate the performance of the target processing unit in the operating state.

[0153] Step S502: The first processing unit obtains the second monitoring data of the intelligent engine. The intelligent engine in the operating state manages the currently running target program.

[0154] Step S503: The first processing unit inputs the first monitoring data and the second monitoring data into the intelligent neural network model to obtain the usage scenario of the electronic device determined by the intelligent neural network model.

[0155] Step S504: If the accuracy of the usage scenario of the electronic device determined by the intelligent neural network model does not meet the accuracy threshold, then control the usage scenario of the electronic device to be a general usage scenario.

[0156] Step S505: The first processing unit adjusts the target parameters of the target processing unit based on the general usage scenario.

[0157] For the detailed processes of Steps S401 - S405, reference can be made to the relevant descriptions of Steps S301 - S305 in Embodiment 3, which will not be elaborated here.

[0158] Step S506: The intelligent engine obtains the second information of the electronic device and sends the second information of the electronic device to the cloud server.

[0159] The intelligent engine obtains the second information of the electronic device, which can be understood as: the intelligent engine obtains the second information of the electronic device by managing the target program running in the electronic device under the general usage scenario.

[0160] Step S507: The first sensing unit obtains the third information of the electronic device and sends the third information of the electronic device to the cloud server, so that the first server obtains the second information and the third information from the cloud server, updates the intelligent neural network model based on the second information and the third information, and sends the updated intelligent neural network model to the cloud server for storage.

[0161] The first sensing unit obtains the third information of the electronic device, which can be understood as: the first sensing unit obtains the third information of the electronic device by monitoring the target parameters provided by the power supply to the target processing unit of the electronic device under the general usage scenario.

[0162] In this embodiment, as Figure 12As shown, the first processing unit inputs the first monitoring data of the first sensing unit and the second monitoring data of the intelligent engine into the intelligent neural network model by obtaining the first monitoring data of the first sensing unit and the second monitoring data of the intelligent engine, and obtains the usage scenario of the electronic device determined by the intelligent neural network model. If the accuracy of the usage scenario of the electronic device determined by the intelligent neural network model does not meet the accuracy threshold, it controls the usage scenario of the electronic device to be a general usage scenario, and adjusts the target parameters of the target processing unit based on the general usage scenario. The intelligent engine obtains the second information of the electronic device, and the first sensing unit obtains the third information of the electronic device. The intelligent engine sends the second information of the electronic device to the cloud server, and the first sensing unit sends the third information of the electronic device to the cloud server, so that the first server can obtain the second information and the third information from the cloud server, update the intelligent neural network model based on the second information and the third information to improve the accuracy of determining the usage scenario by the intelligent neural network model, and send the updated intelligent neural network model to the cloud server for storage, so that the electronic device can obtain and use the updated intelligent neural network model from the cloud server, further improving the accuracy of target parameter adjustment and improving the user experience.

[0163] Corresponding to the above-described method embodiment for processing provided by the present application, the present application also provides an embodiment of a processing device.

[0164] Please refer to Figure 13 , the processing device includes: a first obtaining module 100, a second obtaining module 200, a determining module 300, and an adjusting module 400.

[0165] The first obtaining module 100 is configured to obtain the first monitoring data of the first sensing unit. The first sensing unit monitors the target parameters provided by the power supply to the target processing unit, and the target parameters are used to indicate the performance of the target processing unit in the running state.

[0166] The second obtaining module 200 is configured to obtain the second monitoring data of the intelligent engine. The intelligent engine in the running state manages the currently running target program.

[0167] The determining module 300 is configured to determine the usage scenario of the electronic device based on the matching of the first monitoring data and the second monitoring data with the intelligent scenario model.

[0168] The adjusting module 400 is configured to adjust the target parameters of the target processing unit based on the usage scenario.

[0169] In this embodiment, the processing device may further include:

[0170] A setting module, configured to set the management software for controlling the target processing unit through the intelligent engine.

[0171] In this embodiment, the intelligent scenario model may be an intelligent neural network model, and the first processing unit may be a chip with computing capabilities.

[0172] In this embodiment, the processing device may further include:

[0173] A control module, configured to control the usage scenario of the electronic device to be a general usage scenario if the accuracy of the usage scenario of the electronic device determined by the intelligent neural network model does not meet the accuracy threshold.

[0174] Corresponding to the method embodiment of a processing method provided in the present application, the present application also provides an embodiment of a processing system.

[0175] The processing system includes:

[0176] A first sensing unit, configured to monitor a target parameter provided by a power supply to a target processing unit, where the target parameter is used to indicate the performance of the target processing unit in an operating state;

[0177] An intelligent engine, configured to manage a currently running target program when in an operating state;

[0178] A first processing unit, configured to execute the processing method described in any one of Embodiments 1-3 of the method embodiment.

[0179] In this embodiment, the processing system may further include: a cloud server and a first server;

[0180] The intelligent engine may further be configured to:

[0181] Obtain first information of the electronic device, where the first information represents parameter information of the usage status of the electronic device in a general usage scenario;

[0182] Send the first information to the cloud server, so that the first server obtains the first information from the cloud server, updates the intelligent neural network model based on the first information, and sends the updated intelligent neural network model to the cloud server for storage.

[0183] In this embodiment, the intelligent engine may further be configured to:

[0184] Send a first request to the cloud server, and obtain neural network model version information and a neural network model download address returned by the cloud server in response to the first request;

[0185] If the neural network model version information indicates that the current neural network model in the cloud server is newer than the neural network model of the electronic device, obtain the current neural network model in the cloud server based on the neural network model download address, and use the current neural network model as the intelligent neural network model.

[0186] It should be noted that each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For device embodiments, since they are basically similar to method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the corresponding descriptions in the method embodiments.

[0187] Finally, it should also be noted that in this text, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the said element.

[0188] For the convenience of description, when describing the above device, it is divided into various units according to functions for separate description. Of course, when implementing the present application, the functions of each unit can be implemented in one or more software and / or hardware.

[0189] From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present application.

[0190] The above has introduced in detail a processing method, device and system provided by the present application. Specific examples are used in this text to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A processing method, the method comprising: A first processing unit obtains first monitoring data of a first sensing unit, where the first sensing unit monitors a target parameter provided by a monitoring power supply to a target processing unit, and the target parameter is used to indicate the performance of the target processing unit in an operating state; The first processing unit obtains second monitoring data of an intelligent engine, where the intelligent engine in an operating state manages a currently running target program; The first processing unit matches the first monitoring data and the second monitoring data with an intelligent neural network model to determine a usage scenario of the electronic device; The first processing unit adjusts the target parameter of the target processing unit based on the usage scenario; If the accuracy of the usage scenario of the electronic device determined by the intelligent neural network model does not meet an accuracy threshold, then control the usage scenario of the electronic device to be a general usage scenario.

2. The method according to claim 1, the method further comprising: The first processing unit sets, through the intelligent engine, management software for controlling the target processing unit.

3. The method according to claim 1, the first processing unit being a chip with computing capabilities.

4. The method according to claim 1, the method further comprising: The intelligent engine obtains first information of the electronic device, where the first information represents parameter information of the usage status of the electronic device in the general usage scenario; The intelligent engine sends the first information to a cloud server, so that a first server obtains the first information from the cloud server, updates the intelligent neural network model based on the first information, and sends the updated intelligent neural network model to the cloud server for storage.

5. The method according to claim 3, the intelligent neural network model being obtained in the following manner, including: The intelligent engine sends a first request to the cloud server and obtains neural network model version information and a neural network model download address returned by the cloud server in response to the first request; If the neural network model version information indicates that the current neural network model in the cloud server is newer than the neural network model of the electronic device, then the intelligent engine obtains the current neural network model in the cloud server based on the neural network model download address and uses the current neural network model as the intelligent neural network model.

6. A processing device, comprising: A first obtaining module, configured to obtain first monitoring data of a first sensing unit, where the first sensing unit monitors a target parameter provided by a monitoring power supply to a target processing unit, and the target parameter is used to indicate the performance of the target processing unit in an operating state; A second obtaining module, configured to obtain second monitoring data of an intelligent engine, where the intelligent engine in an operating state manages a currently running target program; A determining module, configured to match the first monitoring data and the second monitoring data with an intelligent neural network model to determine a usage scenario of the electronic device; An adjusting module, configured to adjust the target parameter of the target processing unit based on the usage scenario; The processing device is further configured to: If the accuracy of the usage scenario of the electronic device determined by the intelligent neural network model does not meet the accuracy threshold, control the usage scenario of the electronic device as a general usage scenario.

7. A processing system, comprising: A first processing unit, a first sensing unit, and an intelligent engine; The first sensing unit is configured to monitor target parameters provided by a power supply to a target processing unit, where the target parameters are used to indicate the performance of the target processing unit in an operating state; The intelligent engine is configured to manage a target program currently running when in an operating state; The first processing unit is configured to match the target parameters and data related to the target program currently running with an intelligent neural network model to determine the usage scenario of the electronic device, adjust the target parameters of the target processing unit based on the usage scenario, and if the accuracy of the usage scenario of the electronic device determined by the intelligent neural network model does not meet the accuracy threshold, control the usage scenario of the electronic device as a general usage scenario.

8. The processing system according to claim 7, wherein the processing system further comprises: A cloud server and a first server; The intelligent engine is further configured to: Obtain first information of the electronic device, where the first information represents parameter information of the usage status of the electronic device in the general usage scenario; Send the first information to the cloud server, so that the first server obtains the first information from the cloud server, updates the intelligent neural network model based on the first information, and sends the updated intelligent neural network model to the cloud server for storage.

9. The processing system according to claim 7, wherein the intelligent engine is further configured to: Send a first request to the cloud server and obtain neural network model version information and a neural network model download address returned by the cloud server in response to the first request; If the neural network model version information indicates that the current neural network model in the cloud server is newer than the neural network model of the electronic device, obtain the current neural network model in the cloud server based on the neural network model download address and use the current neural network model as the intelligent neural network model.

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