Firmware upgrading parameter determination method and device, equipment, medium and program product

Abstract

The invention relates to a firmware upgrading parameter determination method and device, equipment, a medium and a program product. The method comprises the steps of firstly, obtaining a first configuration parameter identifier and a second configuration parameter identifier corresponding to firmware, then determining corresponding first configuration data according to the first configuration parameter identifier, and performing data prediction processing according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier and a preset prediction model, second configuration data corresponding to a second configuration parameter identifier is determined, the first configuration parameter identifier is a configuration parameter identifier before firmware upgrading, and the second configuration parameter identifier is a configuration parameter identifier after firmware upgrading. By adopting the method, the parameters after firmware upgrading can be automatically determined, so that option setting can be carried out after firmware upgrading according to the second configuration data, manual setting by a user is not needed, and the firmware upgrading efficiency is higher.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, equipment, medium and program product for determining firmware upgrade parameters. Background Art

[0002] BIOS (Basic Input Output System) is the first program software loaded when the server starts. BMC (Baseboard Management Controller) is an important means of server management and configuration. Users can set BIOS options according to the application business running on the server to make the server hardware run in the best state and give full play to the hardware's optimal performance. At the same time, by setting BMC options, you can monitor the working status of each hardware component of the server.

[0003] Therefore, in the process of infrastructure operation and maintenance of the data center computer room, it is essential to adjust the appropriate BIOS options and BMC option settings for servers running different businesses.

[0004] Currently, after the BIOS or BMC upgrade is completed, the current BIOS option settings or BMC option settings may not be saved, so that the user needs to re-set the options after the BIOS or BMC upgrade is completed, resulting in low firmware upgrade efficiency. Summary of the invention

[0005] Based on this, it is necessary to provide a firmware upgrade parameter determination method, device, equipment, medium and program product that can automatically determine the parameters after the firmware upgrade in order to solve the above technical problems.

[0006] In a first aspect, the present application provides a method for determining firmware upgrade parameters, the method comprising:

[0007] Obtain a first configuration parameter identifier and a second configuration parameter identifier corresponding to the firmware; the first configuration parameter identifier is a configuration parameter identifier before the firmware is upgraded, and the second configuration parameter identifier is a configuration parameter identifier after the firmware is upgraded;

[0008] Determine corresponding first configuration data according to the first configuration parameter identifier;

[0009] Data prediction processing is performed according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier and a preset prediction model to determine the second configuration data corresponding to the second configuration parameter identifier.

[0010] In one embodiment, the data prediction processing is performed according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier and a preset prediction model to determine the second configuration data corresponding to the second configuration parameter identifier, including:

[0011] Inputting the first configuration parameter identifier, the first configuration data, and the second configuration parameter identifier into the prediction model, and determining target attribute information corresponding to the first configuration data according to the first configuration data;

[0012] Determine, according to the first configuration parameter identifier, a target function corresponding to the first configuration parameter identifier;

[0013] The second configuration data is generated according to the target attribute information, the target function, the first configuration data and the second configuration parameter identifier.

[0014] In this embodiment, after determining the target attribute information and the target function of the firmware option, the second configuration data after the firmware upgrade is predicted by the prediction model, thereby improving the accuracy of the second configuration data prediction.

[0015] In one embodiment, the prediction model includes a first corresponding relationship, and the step of determining target attribute information corresponding to the first configuration data according to the first configuration data includes:

[0016] The first configuration data is input into the first corresponding relationship for matching, and the target attribute information corresponding to the first configuration data is determined; wherein the first corresponding relationship includes the corresponding relationship between different attribute information and configuration data.

[0017] In this embodiment, the target attribute information corresponding to the first configuration data is determined through the first corresponding relationship, thereby improving the accuracy of subsequent prediction of the second configuration data.

[0018] In one embodiment, the prediction model further includes a second corresponding relationship, and the step of determining the target function corresponding to the first configuration parameter identifier according to the first configuration parameter identifier includes:

[0019] The first configuration parameter identifier is input into the second corresponding relationship for matching, and the target function corresponding to the first configuration parameter identifier is determined; wherein the second corresponding relationship includes the corresponding relationship between different functions and configuration parameter identifiers.

[0020] In this embodiment, the target function corresponding to the first configuration parameter identifier is determined through the second corresponding relationship, thereby improving the accuracy of subsequent second configuration data prediction.

[0021] In one embodiment, the training method of the above prediction model includes:

[0022] Acquire multiple training data sets; each training data set includes training configuration parameters before the firmware upgrade and training configuration parameters after the firmware upgrade, and the training configuration parameters include training configuration parameter identifiers and training configuration data;

[0023] The training configuration parameters before the firmware upgrade and the training configuration parameter identifiers in the training configuration parameters after the firmware upgrade are used as inputs of the initial prediction model, and the training configuration data in the training configuration parameters after the firmware upgrade are used as reference outputs of the initial prediction model. The initial prediction model is trained to obtain a prediction model.

[0024] In this embodiment, a prediction model is obtained by training according to a training data set, and the prediction model can be used to predict the option configuration data after the firmware upgrade according to the option configuration data before the firmware upgrade, thereby improving the efficiency of determining firmware parameters during the firmware upgrade process.

[0025] In one of the embodiments, the training configuration parameters before the firmware upgrade and the training configuration parameter identifiers in the training configuration parameters after the firmware upgrade are used as inputs of the initial prediction model, the training configuration data in the training configuration parameters after the firmware upgrade are used as reference outputs of the initial prediction model, and the initial prediction model is trained to obtain the prediction model, including:

[0026] Inputting the training configuration parameter identifiers in the training configuration parameters before the firmware upgrade and the training configuration parameters after the firmware upgrade into the initial prediction model, performing supervised classification processing on each training data set, and determining the prediction attribute information corresponding to each training configuration data;

[0027] Perform unsupervised classification processing on each training data set to determine the prediction function corresponding to each training configuration parameter identifier;

[0028] According to each prediction attribute information, each prediction function and each training data set, the initial prediction model is trained to obtain a prediction model.

[0029] In this embodiment, a prediction model is trained based on a training data set, and classification is performed through supervised learning and unsupervised learning during the training process. The classification results are then used as input to train an initial prediction model to obtain a prediction model, which can improve the efficiency and accuracy of the model training process.

[0030] In a second aspect, the present application also provides a firmware upgrade parameter determination device, comprising:

[0031] An acquisition module, used to acquire a first configuration parameter identifier and a second configuration parameter identifier corresponding to the firmware; the first configuration parameter identifier is a configuration parameter identifier before the firmware is upgraded, and the second configuration parameter identifier is a configuration parameter identifier after the firmware is upgraded;

[0032] A determination module, configured to determine corresponding first configuration data according to a first configuration parameter identifier;

[0033] The prediction module is used to perform data prediction processing according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier and a preset prediction model to determine the second configuration data corresponding to the second configuration parameter identifier.

[0034] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method for determining firmware upgrade parameters described in any one of the first aspects above is implemented.

[0035] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for determining firmware upgrade parameters described in any one of the first aspects above is implemented.

[0036] In a fifth aspect, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the firmware upgrade parameter determination method described in any one of the first aspects above.

[0037] The above-mentioned method, device, equipment, medium and program product for determining firmware upgrade parameters first obtain the first configuration parameter identifier and the second configuration parameter identifier corresponding to the firmware, then determine the corresponding first configuration data according to the first configuration parameter identifier, and finally perform data prediction processing according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier and the preset prediction model to determine the second configuration data corresponding to the second configuration parameter identifier, wherein the first configuration parameter identifier is the configuration parameter identifier before the firmware upgrade, and the second configuration parameter identifier is the configuration parameter identifier after the firmware upgrade. In this way, the second configuration data after the firmware upgrade is obtained by predicting the first configuration data before the firmware upgrade, the first configuration parameter identifier and the second configuration parameter identifier through the preset prediction model, and the parameters after the firmware upgrade can be automatically determined, so that the options after the firmware upgrade can be set according to the second configuration data without the need for the user to set them manually, so the efficiency of the firmware upgrade is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 is an internal structure diagram of a computer device in one embodiment;

[0040] Figure 2 A schematic diagram of a method for determining firmware upgrade parameters in one embodiment;

[0041] Figure 3 It is a flowchart of a method for determining firmware upgrade parameters in another embodiment;

[0042] Figure 4 A flowchart of a prediction model training method in one embodiment;

[0043] Figure 5 A schematic diagram of a flow chart of a prediction model training method in another embodiment;

[0044] Figure 6 A flowchart of firmware upgrade parameter configuration in another embodiment;

[0045] Figure 7 It is a structural block diagram of a device for determining firmware upgrade parameters in an embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] The firmware upgrade parameter determination method provided in this embodiment can be applied to a computer device, which can be a terminal or a server. Taking a terminal as an example, its internal structure diagram can be as follows: Figure 1As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for determining firmware upgrade parameters is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0048] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0049] In one embodiment, Figure 2 As shown, a method for determining firmware upgrade parameters is provided, and the method is described by taking the application of the method to a computer device as an example. The method may include the following steps:

[0050] S202: Obtain a first configuration parameter identifier and a second configuration parameter identifier corresponding to the firmware.

[0051] The first configuration parameter identifier is a configuration parameter identifier before the firmware upgrade, and the second configuration parameter identifier is a configuration parameter identifier after the firmware upgrade. The firmware can be the BIOS running on the server, or it can be the BMC, or other software to be upgraded. The configuration parameter identifier of the firmware can be an identifier of the firmware option configuration. By setting the firmware options, the server can better realize its respective functions. The firmware upgrade can be an upgrade of adjacent versions of firmware, or it can be an upgrade of non-adjacent versions of firmware.

[0052] S204: Determine corresponding first configuration data according to the first configuration parameter identifier.

[0053] Among them, the first configuration data is the configuration data of the options before the firmware upgrade. The option configuration data before the firmware upgrade can be obtained according to the first configuration parameter identifier. Optionally, the method for obtaining the first configuration data may include determining it according to the output content of the manufacturer's tools of different firmware, or determining it by performing image recognition after taking a screenshot of the firmware interface, or determining it by performing a web crawler on the firmware interface, etc.

[0054] S206: Perform data prediction processing according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier, and a preset prediction model to determine the second configuration data corresponding to the second configuration parameter identifier.

[0055] Among them, the preset prediction model is trained based on historical configuration parameter data. Optionally, after the firmware is upgraded, the configuration parameter identifier of the option and the configuration parameter identifier of the option before the upgrade may change, especially when the firmware version span before and after the upgrade is large, the configuration parameter identifier before and after the firmware upgrade changes greatly. Therefore, the first configuration parameter identifier, first configuration data, and second configuration parameter identifier obtained as mentioned above are all input into the preset prediction model for data prediction processing to obtain the second configuration data corresponding to the second configuration parameter identifier after the firmware upgrade. The second configuration data is also the configuration data of the option after the firmware upgrade, which realizes the automatic determination of the option after the firmware upgrade, and the user does not need to consider the problem of different versions when upgrading the firmware.

[0056] In the above-mentioned method for determining firmware upgrade parameters, firstly, the first configuration parameter identifier and the second configuration parameter identifier corresponding to the firmware are obtained, then the corresponding first configuration data is determined according to the first configuration parameter identifier, and finally, data prediction processing is performed according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier and the preset prediction model to determine the second configuration data corresponding to the second configuration parameter identifier, wherein the first configuration parameter identifier is the configuration parameter identifier before the firmware upgrade, and the second configuration parameter identifier is the configuration parameter identifier after the firmware upgrade. In this way, the second configuration data after the firmware upgrade is obtained by predicting the first configuration data before the firmware upgrade, the first configuration parameter identifier and the second configuration parameter identifier through the preset prediction model, and the parameters after the firmware upgrade can be automatically determined, so that the options after the firmware upgrade can be set according to the second configuration data without the need for the user to set them manually, so the efficiency of the firmware upgrade is higher.

[0057] In another embodiment, another method for determining firmware upgrade parameters is provided. Based on the above embodiment, Figure 3 As shown, the above S206 may include the following steps:

[0058] S302: Input the first configuration parameter identifier, the first configuration data, and the second configuration parameter identifier into a prediction model, and determine target attribute information corresponding to the first configuration data according to the first configuration data.

[0059] The target attribute information may be the manufacturer information corresponding to the firmware, and the descriptions corresponding to the options of the same function of the firmware of different manufacturers may be different. According to the specific description habits and description methods of the first configuration data before the firmware upgrade, the firmware can be preliminarily classified to determine the manufacturer information corresponding to the firmware.

[0060] Optionally, the prediction model includes a first corresponding relationship, and determining target attribute information corresponding to the first configuration data according to the first configuration data includes: inputting the first configuration data into the first corresponding relationship for matching, and determining the target attribute information corresponding to the first configuration data.

[0061] The first corresponding relationship includes the corresponding relationship between different attribute information and configuration data. Optionally, the first corresponding relationship is determined by the prediction model during the model training process. The first configuration data is matched with the configuration data in the first corresponding relationship, and the attribute information corresponding to the successfully matched configuration data is determined as the target attribute information corresponding to the first configuration data.

[0062] S304: Determine a target function corresponding to the first configuration parameter identifier according to the first configuration parameter identifier.

[0063] The target function is the function of the option corresponding to the first configuration parameter identifier. Optionally, the options of the BIOS firmware can be divided into chip settings, power settings, security option settings, boot device settings, and boot option settings, etc., according to specific functions. The options of the BMC firmware can be divided into system management settings, power management settings, heat dissipation management settings, user settings, and security settings, etc., according to functions. Further classification is performed according to the first configuration parameter identifier to determine the function of the option corresponding to the first configuration parameter identifier.

[0064] Optionally, the prediction model also includes a second corresponding relationship, and determining the target function corresponding to the first configuration parameter identifier based on the first configuration parameter identifier, including: inputting the first configuration parameter identifier into the second corresponding relationship for matching, and determining the target function corresponding to the first configuration parameter identifier.

[0065] The second correspondence includes the correspondence between different functions and configuration parameter identifiers. Optionally, the second correspondence is determined by the prediction model during the model training process. The first configuration parameter identifier is matched with the configuration parameter identifier in the second correspondence, and the function corresponding to the successfully matched configuration parameter identifier is determined as the target function corresponding to the first configuration parameter identifier.

[0066] S306: Generate second configuration data according to the target attribute information, the target function, the first configuration data and the second configuration parameter identifier.

[0067] After determining the target attribute information and the target function, the prediction model generates the second configuration data corresponding to the second configuration parameter identifier according to the first configuration data and the second configuration parameter identifier. After determining the second configuration data, the second configuration data can be automatically updated to the upgraded firmware options according to the manufacturer tool or WEB automation technology corresponding to the target attribute information, so as to realize the automatic configuration of the options after the firmware upgrade.

[0068] In this embodiment, the target attribute information corresponding to the first configuration data is determined by the first corresponding relationship, and the target function corresponding to the first configuration parameter identifier is determined by the second corresponding relationship, thereby improving the accuracy of the subsequent prediction of the second configuration data. Furthermore, after determining the target attribute information and target function of the firmware option, the second configuration data after the firmware upgrade is predicted by the prediction model, thereby improving the accuracy of the second configuration data prediction.

[0069] In another embodiment, the preset prediction model needs to be trained through historical option configuration data, and the training method of the prediction model is as follows: Figure 4 As shown, including:

[0070] S402, obtaining multiple training data sets.

[0071] Each training data set includes training configuration parameters before the firmware upgrade and training configuration parameters after the firmware upgrade, and the training configuration parameters include training configuration parameter identifiers and training configuration data. The training data set may include training configuration parameters for multiple firmware versions. Accordingly, the training data set has a rich firmware version, and the accuracy of the prediction model obtained by training is higher.

[0072] S404, using the training configuration parameters before the firmware upgrade and the training configuration parameter identifiers in the training configuration parameters after the firmware upgrade as inputs to the initial prediction model, using the training configuration data in the training configuration parameters after the firmware upgrade as reference outputs of the initial prediction model, training the initial prediction model, and obtaining a prediction model.

[0073] The initial prediction model may be a model based on machine learning, such as a neural network model or a random forest algorithm, and the initial prediction model is trained according to a training data set with labeled inputs and outputs until the accuracy of the initial prediction model meets preset requirements to obtain a prediction model.

[0074] In this embodiment, a prediction model is obtained by training according to a training data set, and the prediction model can be used to predict the option configuration data after the firmware upgrade according to the option configuration data before the firmware upgrade, thereby improving the efficiency of determining firmware parameters during the firmware upgrade process.

[0075] In another embodiment, based on the above embodiment, Figure 5 As shown, the above S404 may include the following steps:

[0076] S502, input the training configuration parameter identifiers in the training configuration parameters before the firmware upgrade and the training configuration parameters after the firmware upgrade into the initial prediction model, perform supervised classification processing on each training data set, and determine the prediction attribute information corresponding to each training configuration data.

[0077] Among them, supervised learning is a method in machine learning, which can learn or establish a pattern from a labeled training data set, and infer new data based on this pattern. Optionally, the training configuration parameters before the firmware upgrade and the training configuration parameter identifiers in the training configuration parameters after the firmware upgrade are marked, and then input into the initial prediction model for supervised classification processing to determine the predicted attribute information corresponding to the training configuration data. The predicted attribute information may include the manufacturer information of the firmware. According to the predicted attribute information, the first corresponding relationship can be determined, that is, the corresponding relationship between different attribute information and configuration data is obtained.

[0078] S504, performing unsupervised classification processing on each training data set to determine a prediction function corresponding to each training configuration parameter identifier.

[0079] Among them, unsupervised classification does not require labeling of the training data set. Optionally, a cluster analysis algorithm can be used to discover hidden grouping patterns in the training data set through exploratory data analysis. By performing unsupervised classification on each training data set, the prediction function corresponding to each training configuration parameter identifier is determined, thereby determining the second corresponding relationship, that is, obtaining the corresponding relationship between different functions and configuration data.

[0080] S506: Train the initial prediction model according to each prediction attribute information, each prediction function and each training data set to obtain a prediction model.

[0081] According to the prediction attribute information and the first corresponding relationship, the prediction functions and the second corresponding relationship, and the training data sets obtained after the above classification, the initial prediction model is trained to obtain the prediction model. Optionally, multiple test data sets can also be obtained, each of which includes test configuration parameters before the firmware upgrade and test configuration parameters after the firmware upgrade, and the test configuration parameters include test configuration parameter identifiers and test configuration data. The trained prediction model is tested according to the test data set, and the prediction model is obtained when the test result meets the requirements.

[0082] In this embodiment, a prediction model is trained based on a training data set, and classification is performed through supervised learning and unsupervised learning during the training process. The classification results are then used as input to train an initial prediction model to obtain a prediction model, which can improve the efficiency and accuracy of the model training process.

[0083] In the examples of this application, please refer to Figure 6 , which shows a flowchart of a method for determining firmware upgrade parameters provided in an embodiment of the present application. First, a first configuration parameter before the firmware upgrade on the server is obtained, the first configuration parameter including a first configuration parameter identifier and first configuration parameter data. Then, the server is firmware upgraded. After the firmware upgrade, the first configuration parameter before the firmware upgrade and the second configuration parameter identifier obtained after the firmware upgrade are used as inputs, and a prediction model is used for prediction to obtain configuration parameter data of the second configuration parameter after the firmware upgrade, that is, to obtain option configuration data after the firmware upgrade. Finally, the configuration parameter data of the second configuration parameter can be imported into the server through the manufacturer tool corresponding to the firmware to complete the option configuration after the firmware upgrade.

[0084] In this embodiment, when the user is performing a firmware upgrade, the configuration parameter data after the firmware upgrade can be predicted by a prediction model based on the configuration parameter data before the upgrade, thereby achieving the purpose of retaining the original option settings after the firmware version is upgraded, and the user does not need to set the options after the firmware upgrade.

[0085] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0086] Based on the same inventive concept, the embodiment of the present application also provides a firmware upgrade parameter determination device for implementing the firmware upgrade parameter determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more firmware upgrade parameter determination device embodiments provided below can refer to the limitations of the firmware upgrade parameter determination method above, and will not be repeated here.

[0087] In an exemplary embodiment, Figure 7 As shown, a firmware upgrade parameter determination device is provided, comprising: an acquisition module, a determination module and a prediction module, wherein:

[0088] The acquisition module is used to acquire a first configuration parameter identifier and a second configuration parameter identifier corresponding to the firmware; the first configuration parameter identifier is a configuration parameter identifier before the firmware is upgraded, and the second configuration parameter identifier is a configuration parameter identifier after the firmware is upgraded;

[0089] The determination module is used to determine the corresponding first configuration data according to the first configuration parameter identifier;

[0090] The prediction module is used to perform data prediction processing according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier and a preset prediction model to determine the second configuration data corresponding to the second configuration parameter identifier.

[0091] In one embodiment, the prediction module includes:

[0092] A first determining unit, configured to input the first configuration parameter identifier, the first configuration data, and the second configuration parameter identifier into the prediction model, and determine target attribute information corresponding to the first configuration data according to the first configuration data;

[0093] A second determining unit, configured to determine a target function corresponding to the first configuration parameter identifier according to the first configuration parameter identifier;

[0094] The generating unit is used to generate the second configuration data according to the target attribute information, the target function, the first configuration data and the second configuration parameter identifier.

[0095] In one of the embodiments, the prediction model includes a first corresponding relationship, and the prediction module is specifically used to input the first configuration data into the first corresponding relationship for matching, and determine the target attribute information corresponding to the first configuration data; wherein the first corresponding relationship includes the correspondence between different attribute information and configuration data.

[0096] In one of the embodiments, the prediction model also includes a second correspondence relationship, and the prediction module is specifically used to input the first configuration parameter identifier into the second correspondence relationship for matching, and determine the target function corresponding to the first configuration parameter identifier; wherein the second correspondence relationship includes the correspondence between different functions and configuration parameter identifiers.

[0097] In one embodiment, a training module is also included, including:

[0098] An acquisition unit, used to acquire multiple training data sets; each training data set includes training configuration parameters before the firmware upgrade and training configuration parameters after the firmware upgrade, and the training configuration parameters include training configuration parameter identifiers and training configuration data;

[0099] The training unit is used to use the training configuration parameters before the firmware upgrade and the training configuration parameter identifiers in the training configuration parameters after the firmware upgrade as inputs of the initial prediction model, and use the training configuration data in the training configuration parameters after the firmware upgrade as reference outputs of the initial prediction model to train the initial prediction model and obtain a prediction model.

[0100] In one embodiment, the training module further includes:

[0101] A third determination unit is used to input the training configuration parameter identifiers in the training configuration parameters before the firmware upgrade and the training configuration parameters after the firmware upgrade into the initial prediction model, perform supervised classification processing on each training data set, and determine the prediction attribute information corresponding to each training configuration data;

[0102] A fourth determination unit, configured to perform unsupervised classification processing on each training data set to determine a prediction function corresponding to each training configuration parameter identifier;

[0103] The training unit is specifically used to train the initial prediction model according to each prediction attribute information, each prediction function and each training data set to obtain the prediction model.

[0104] Each module in the above-mentioned firmware upgrade parameter determination device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0105] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: obtaining a first configuration parameter identifier and a second configuration parameter identifier corresponding to a firmware; the first configuration parameter identifier is a configuration parameter identifier before the firmware is upgraded, and the second configuration parameter identifier is a configuration parameter identifier after the firmware is upgraded; determining the corresponding first configuration data according to the first configuration parameter identifier; performing data prediction processing according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier and a preset prediction model to determine the second configuration data corresponding to the second configuration parameter identifier.

[0106] In one embodiment, when the processor executes the computer program, the following steps are also implemented: inputting the first configuration parameter identifier, the first configuration data, and the second configuration parameter identifier into the prediction model, determining the target attribute information corresponding to the first configuration data based on the first configuration data; determining the target function corresponding to the first configuration parameter identifier based on the first configuration parameter identifier; and generating the second configuration data based on the target attribute information, the target function, the first configuration data, and the second configuration parameter identifier.

[0107] In one embodiment, the prediction model includes a first correspondence relationship, and the processor further implements the following steps when executing the computer program: inputting the first configuration data into the first correspondence relationship for matching, and determining the target attribute information corresponding to the first configuration data; wherein the first correspondence relationship includes the correspondence between different attribute information and configuration data.

[0108] In one embodiment, the prediction model also includes a second correspondence relationship, and the processor also implements the following steps when executing the computer program: inputting the first configuration parameter identifier into the second correspondence relationship for matching, and determining the target function corresponding to the first configuration parameter identifier; wherein the second correspondence relationship includes the correspondence between different functions and configuration parameter identifiers.

[0109] In one embodiment, when the processor executes the computer program, the following steps are also implemented: obtaining multiple training data sets; each training data set includes training configuration parameters before the firmware upgrade and training configuration parameters after the firmware upgrade, and the training configuration parameters include training configuration parameter identifiers and training configuration data; using the training configuration parameters before the firmware upgrade and the training configuration parameter identifiers in the training configuration parameters after the firmware upgrade as inputs to the initial prediction model, using the training configuration data in the training configuration parameters after the firmware upgrade as reference outputs of the initial prediction model, training the initial prediction model, and obtaining a prediction model.

[0110] In one embodiment, when the processor executes the computer program, the following steps are also implemented: the training configuration parameters before the firmware upgrade and the training configuration parameter identifiers in the training configuration parameters after the firmware upgrade are input into the initial prediction model, and supervised classification processing is performed on each training data set to determine the prediction attribute information corresponding to each training configuration data; unsupervised classification processing is performed on each training data set to determine the prediction function corresponding to each training configuration parameter identifier; and the initial prediction model is trained according to each prediction attribute information, each prediction function and each training data set to obtain a prediction model.

[0111] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: obtaining a first configuration parameter identifier and a second configuration parameter identifier corresponding to a firmware; the first configuration parameter identifier is a configuration parameter identifier before the firmware is upgraded, and the second configuration parameter identifier is a configuration parameter identifier after the firmware is upgraded; determining the corresponding first configuration data according to the first configuration parameter identifier; performing data prediction processing according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier, and a preset prediction model to determine the second configuration data corresponding to the second configuration parameter identifier.

[0112] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: inputting the first configuration parameter identifier, the first configuration data, and the second configuration parameter identifier into the prediction model, determining the target attribute information corresponding to the first configuration data based on the first configuration data; determining the target function corresponding to the first configuration parameter identifier based on the first configuration parameter identifier; and generating the second configuration data based on the target attribute information, the target function, the first configuration data, and the second configuration parameter identifier.

[0113] In one embodiment, the prediction model includes a first correspondence relationship, and the computer program, when executed by the processor, also implements the following steps: inputting the first configuration data into the first correspondence relationship for matching, and determining the target attribute information corresponding to the first configuration data; wherein the first correspondence relationship includes the correspondence between different attribute information and configuration data.

[0114] In one embodiment, the prediction model also includes a second correspondence relationship, and the computer program, when executed by the processor, also implements the following steps: inputting the first configuration parameter identifier into the second correspondence relationship for matching, and determining the target function corresponding to the first configuration parameter identifier; wherein the second correspondence relationship includes the correspondence between different functions and configuration parameter identifiers.

[0115] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining multiple training data sets; each training data set includes training configuration parameters before the firmware upgrade and training configuration parameters after the firmware upgrade, and the training configuration parameters include training configuration parameter identifiers and training configuration data; using the training configuration parameters before the firmware upgrade and the training configuration parameter identifiers in the training configuration parameters after the firmware upgrade as inputs to the initial prediction model, using the training configuration data in the training configuration parameters after the firmware upgrade as reference outputs of the initial prediction model, training the initial prediction model, and obtaining a prediction model.

[0116] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: the training configuration parameters before the firmware upgrade and the training configuration parameter identifiers in the training configuration parameters after the firmware upgrade are input into the initial prediction model, and supervised classification processing is performed on each training data set to determine the prediction attribute information corresponding to each training configuration data; unsupervised classification processing is performed on each training data set to determine the prediction function corresponding to each training configuration parameter identifier; and the initial prediction model is trained according to each prediction attribute information, each prediction function and each training data set to obtain a prediction model.

[0117] In one embodiment, a computer program product is provided, including a computer program, which implements the following steps when executed by a processor: obtaining a first configuration parameter identifier and a second configuration parameter identifier corresponding to a firmware; the first configuration parameter identifier is a configuration parameter identifier before the firmware is upgraded, and the second configuration parameter identifier is a configuration parameter identifier after the firmware is upgraded; determining the corresponding first configuration data according to the first configuration parameter identifier; performing data prediction processing according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier and a preset prediction model to determine the second configuration data corresponding to the second configuration parameter identifier.

[0118] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: inputting the first configuration parameter identifier, the first configuration data, and the second configuration parameter identifier into the prediction model, determining the target attribute information corresponding to the first configuration data based on the first configuration data; determining the target function corresponding to the first configuration parameter identifier based on the first configuration parameter identifier; and generating the second configuration data based on the target attribute information, the target function, the first configuration data, and the second configuration parameter identifier.

[0119] In one embodiment, the prediction model includes a first correspondence relationship, and the computer program, when executed by the processor, also implements the following steps: inputting the first configuration data into the first correspondence relationship for matching, and determining the target attribute information corresponding to the first configuration data; wherein the first correspondence relationship includes the correspondence between different attribute information and configuration data.

[0120] In one embodiment, the prediction model also includes a second correspondence relationship, and the computer program, when executed by the processor, also implements the following steps: inputting the first configuration parameter identifier into the second correspondence relationship for matching, and determining the target function corresponding to the first configuration parameter identifier; wherein the second correspondence relationship includes the correspondence between different functions and configuration parameter identifiers.

[0121] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining multiple training data sets; each training data set includes training configuration parameters before the firmware upgrade and training configuration parameters after the firmware upgrade, and the training configuration parameters include training configuration parameter identifiers and training configuration data; using the training configuration parameters before the firmware upgrade and the training configuration parameter identifiers in the training configuration parameters after the firmware upgrade as inputs to the initial prediction model, using the training configuration data in the training configuration parameters after the firmware upgrade as reference outputs of the initial prediction model, training the initial prediction model, and obtaining a prediction model.

[0122] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: the training configuration parameters before the firmware upgrade and the training configuration parameter identifiers in the training configuration parameters after the firmware upgrade are input into the initial prediction model, and supervised classification processing is performed on each training data set to determine the prediction attribute information corresponding to each training configuration data; unsupervised classification processing is performed on each training data set to determine the prediction function corresponding to each training configuration parameter identifier; and the initial prediction model is trained according to each prediction attribute information, each prediction function and each training data set to obtain a prediction model.

[0123] It should be noted that the data involved in this application (including but not limited to data used for analysis, data stored, data displayed, etc.) are all data fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0124] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0125] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining firmware upgrade parameters, characterized in that: The method comprises: Obtain a first configuration parameter identifier and a second configuration parameter identifier corresponding to the firmware; the first configuration parameter identifier is a configuration parameter identifier before the firmware is upgraded, and the second configuration parameter identifier is a configuration parameter identifier after the firmware is upgraded; Determine corresponding first configuration data according to the first configuration parameter identifier; Data prediction processing is performed according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier, and a preset prediction model to determine the second configuration data corresponding to the second configuration parameter identifier.

2. The method according to claim 1, characterized in that The performing data prediction processing according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier, and a preset prediction model to determine the second configuration data corresponding to the second configuration parameter identifier includes: Inputting the first configuration parameter identifier, the first configuration data, and the second configuration parameter identifier into the prediction model, and determining target attribute information corresponding to the first configuration data according to the first configuration data; Determine, according to the first configuration parameter identifier, a target function corresponding to the first configuration parameter identifier; The second configuration data is generated according to the target attribute information, the target function, the first configuration data and the second configuration parameter identifier.

3. The method according to claim 2, characterized in that The prediction model includes a first corresponding relationship, and determining target attribute information corresponding to the first configuration data according to the first configuration data includes: Inputting the first configuration data into the first corresponding relationship for matching, and determining target attribute information corresponding to the first configuration data; The first corresponding relationship includes the corresponding relationship between different attribute information and configuration data.

4. The method according to claim 2, characterized in that: The prediction model also includes a second corresponding relationship, and the determining, according to the first configuration parameter identifier, a target function corresponding to the first configuration parameter identifier includes: Inputting the first configuration parameter identifier into the second corresponding relationship for matching, and determining a target function corresponding to the first configuration parameter identifier; The second corresponding relationship includes the corresponding relationship between different functions and configuration parameter identifiers.

5. The method according to any one of claims 1 to 4, characterized in that: The training method of the prediction model includes: Acquire multiple training data sets; each of the training data sets includes training configuration parameters before the firmware upgrade and training configuration parameters after the firmware upgrade, and the training configuration parameters include training configuration parameter identifiers and training configuration data; The training configuration parameters before the firmware upgrade and the training configuration parameter identifiers in the training configuration parameters after the firmware upgrade are used as inputs of an initial prediction model, and the training configuration data in the training configuration parameters after the firmware upgrade are used as reference outputs of the initial prediction model. The initial prediction model is trained to obtain the prediction model.

6. The method according to claim 5, characterized in that The method uses the training configuration parameters before the firmware upgrade and the training configuration parameter identifiers in the training configuration parameters after the firmware upgrade as inputs of the initial prediction model, uses the training configuration data in the training configuration parameters after the firmware upgrade as reference outputs of the initial prediction model, trains the initial prediction model, and obtains the prediction model, including: Inputting the training configuration parameter identifiers of the training configuration parameters before the firmware upgrade and the training configuration parameters after the firmware upgrade into the initial prediction model, performing supervised classification processing on each of the training data sets, and determining the prediction attribute information corresponding to each of the training configuration data; Performing unsupervised classification processing on each of the training data sets to determine the prediction function corresponding to each training configuration parameter identifier; The initial prediction model is trained according to each of the prediction attribute information, each of the prediction functions and each of the training data sets to obtain the prediction model.

7. A device for determining firmware upgrade parameters, characterized in that: The device comprises: An acquisition module, used to acquire a first configuration parameter identifier and a second configuration parameter identifier corresponding to the firmware; the first configuration parameter identifier is a configuration parameter identifier before the firmware is upgraded, and the second configuration parameter identifier is a configuration parameter identifier after the firmware is upgraded; A determination module, configured to determine corresponding first configuration data according to the first configuration parameter identifier; A prediction module is used to perform data prediction processing according to the first configuration parameter identifier, the first configuration data, the second configuration parameter identifier and a preset prediction model to determine the second configuration data corresponding to the second configuration parameter identifier.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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