Firmware upgrading method, system, equipment and medium

Abstract

The invention discloses a firmware upgrading method, system and device and a medium, and the method comprises the steps: obtaining the current operation state data and a first upgrading strategy of a power grid device through an Internet of Things platform; the Internet of Things platform inputs the current running state data into the updated upgrading risk analysis model for risk analysis, and a risk level classification result output by the updated upgrading risk analysis model and a plurality of risk factor weights of the risk level classification result are obtained; the Internet of Things platform updates the first upgrading strategy according to the risk level classification result to obtain a second upgrading strategy of the power grid equipment; and performing firmware upgrading on the power grid equipment through the edge node according to the second upgrading strategy and all the risk factor weights to obtain the power grid equipment after firmware upgrading. According to the method, the adaptive capacity of the Internet of Things equipment during firmware upgrading can be effectively improved. The invention relates to the technical field of power Internet of Things.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power Internet of Things, and in particular to a firmware upgrade method, system, device and medium. Background Art

[0002] During the use of power Internet of Things devices, due to system upgrades and other reasons, it is necessary to upgrade the firmware of the power Internet of Things devices so that the power Internet of Things devices can operate continuously and stably.

[0003] At present, relevant technologies usually adopt static strategies and complete version updates of power Internet of Things devices through full upgrade packages. This method has a certain risk of firmware upgrade failure when facing complex network environments and diversified device configurations, and its adaptability is limited when facing complex network environments and diversified device configurations.

[0004] Therefore, the problems existing in related technologies still need to be solved and optimized urgently. Summary of the Invention

[0005] The purpose of the present invention is to solve one of the technical problems existing in the related art to at least a certain extent.

[0006] To this end, an object of an embodiment of the present invention is to provide a firmware upgrade method, system, device and medium, wherein the method can effectively improve the adaptability of IoT devices when performing firmware upgrades.

[0007] In order to achieve the above technical objectives, the technical solutions adopted in the embodiments of the present application include:

[0008] In a first aspect, an embodiment of the present application provides a firmware upgrade method, which is applied to a firmware upgrade system, wherein the firmware upgrade system includes an Internet of Things platform and an edge node, and the method includes:

[0009] The Internet of Things platform obtains current operating status data and a first upgrade strategy of the power grid device;

[0010] The Internet of Things platform inputs the current operating status data into the updated upgrade risk analysis model for risk analysis, and obtains a risk level classification result output by the updated upgrade risk analysis model, as well as several risk factor weights of the risk level classification result;

[0011] The Internet of Things platform updates the first upgrade strategy according to the risk level classification result to obtain a second upgrade strategy for the power grid device;

[0012] The edge node upgrades the firmware of the power grid device according to the second upgrade strategy and all the risk factor weights to obtain the power grid device after the firmware upgrade.

[0013] In addition, the method according to the above embodiment of the present application may also have the following additional technical features:

[0014] Furthermore, in one embodiment of the present application, the updated upgrade risk analysis model is obtained by the following steps:

[0015] Acquire historical operating status data of the power grid device and behavior alarm data corresponding to the historical operating status data, wherein a data value of the behavior alarm data is greater than or equal to a dynamic threshold;

[0016] The historical operating status data and the behavior alarm data are input into the current upgrade risk analysis model for supervised training to obtain the updated upgrade risk analysis model.

[0017] Furthermore, in one embodiment of the present application, obtaining behavior alarm data includes:

[0018] Obtaining original behavior resource data of the power grid device;

[0019] Comparing the behavior resource data with the dynamic threshold to obtain a threshold comparison result;

[0020] If the threshold comparison result is that the behavior resource data is less than the dynamic threshold, return to the step of obtaining the original behavior resource data of the power grid device; or, if the threshold comparison result is that the behavior resource data is greater than or equal to the dynamic threshold, determine the original behavior resource data as the behavior alarm data.

[0021] Furthermore, in one embodiment of the present application, the method further includes:

[0022] Obtaining a trained dynamic threshold analysis model, as well as intermediate state data and intermediate alarm data, wherein the intermediate state data is the previous running state data of the current running state data, and the intermediate alarm data is the behavior alarm data corresponding to the intermediate state data;

[0023] The intermediate state data and the intermediate alarm data are input into the trained dynamic threshold analysis model to perform threshold prediction, and obtain a dynamic threshold corresponding to the current operating state data.

[0024] Furthermore, in one embodiment of the present application, updating the first upgrade strategy according to the risk level classification result to obtain the second upgrade strategy for the power grid device includes:

[0025] Obtaining a preset risk mapping table, wherein the risk mapping table is used to indicate a mapping relationship between risk levels and escalation strategies;

[0026] According to the risk level classification result, the risk mapping table is looked up and mapped to obtain a target upgrade strategy corresponding to the risk level classification result;

[0027] The first upgrade strategy is updated according to the target upgrade strategy to obtain a second upgrade strategy for the power grid device.

[0028] Furthermore, in one embodiment of the present application, performing a firmware upgrade on the power grid device according to the second upgrade strategy and all the risk factor weights to obtain the power grid device after the firmware upgrade includes:

[0029] If the second upgrade strategy is a high-risk upgrade strategy, obtaining original device version information of the power grid device, and performing a firmware rollback on the power grid device according to the original device version information;

[0030] Alternatively, if the upgrade strategy is a medium-risk upgrade strategy, obtaining risk upgrade instructions corresponding to all the risk factor weights, original device version information of the power grid device, and target device version information; performing a firmware upgrade on the power grid device according to the risk upgrade instruction, the original device version information, and the target device version information to obtain the power grid device after the firmware upgrade;

[0031] Alternatively, if the upgrade strategy is a low-risk upgrade strategy, the original device version information and the target device version information of the power grid device are obtained, and the firmware of the power grid device is upgraded according to the original device version information and the target device version information to obtain the power grid device after the firmware upgrade.

[0032] Furthermore, in one embodiment of the present application, performing a firmware upgrade on the power grid device according to the original device version information and the target device version information to obtain the power grid device after the firmware upgrade includes:

[0033] Acquire an original version file corresponding to the original device version information and a target version file corresponding to the target device version information;

[0034] Comparing the original version file and the target version file to obtain file difference information;

[0035] Constructing an incremental upgrade package, and a unique identifier and a timestamp of the incremental upgrade package according to the file difference information;

[0036] Encrypting the incremental upgrade package according to the unique identifier and the timestamp to obtain an incremental encrypted package;

[0037] The firmware of the power grid device is upgraded according to the incremental encrypted package to obtain the power grid device after the firmware upgrade.

[0038] In a second aspect, an embodiment of the present application provides a firmware upgrade system, including:

[0039] The Internet of Things platform is configured to obtain current operating status data and a first upgrade strategy for a power grid device; input the current operating status data into an updated upgrade risk analysis model for risk analysis, thereby obtaining a risk level classification result output by the updated upgrade risk analysis model and weights of several risk factors of the risk level classification result; and update the first upgrade strategy based on the risk level classification result to obtain a second upgrade strategy for the power grid device;

[0040] The edge node is configured to perform a firmware upgrade on the power grid device according to the second upgrade strategy and all the risk factor weights to obtain a power grid device with upgraded firmware.

[0041] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0042] at least one processor;

[0043] at least one memory for storing at least one program;

[0044] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0045] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores a program executable by a processor, and the program executable by the processor is used to implement the above method when executed by the processor.

[0046] The advantages and benefits of this application will be partially given in the following description, and partially become apparent from the following description, or learned through practice of this application:

[0047] The embodiments of the present application disclose a firmware upgrade method, system, device, and medium, wherein the method obtains the current operating status data and a first upgrade strategy of a power grid device through an Internet of Things platform; the Internet of Things platform inputs the current operating status data into an updated upgrade risk analysis model for risk analysis, obtains a risk level classification result output by the updated upgrade risk analysis model, and several risk factor weights of the risk level classification result; the Internet of Things platform updates the first upgrade strategy according to the risk level classification result to obtain a second upgrade strategy of the power grid device; and performs a firmware upgrade on the power grid device according to the second upgrade strategy and all the risk factor weights through an edge node to obtain a power grid device after the firmware upgrade. The method updates the current upgrade strategy (i.e., the second upgrade strategy) of the power grid device based on the risk level classification result obtained by real-time risk analysis, and performs a firmware upgrade on the power grid device based on the second upgrade strategy. This method can effectively improve the adaptability of the power grid device in the face of complex network environments and diversified device configurations, and is conducive to reducing the failure risk of the power grid device during the firmware upgrade process. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present application or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly expressing some embodiments of the technical solutions of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0049] Figure 1 A flowchart of a firmware upgrade method provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of the structural framework of a firmware upgrade system provided in an embodiment of the present application;

[0051] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application. For the step numbers in the following embodiments, they are provided only for the convenience of explanation and are not intended to limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0054] At present, relevant technologies usually adopt static strategies and complete version updates of power Internet of Things devices through full upgrade packages. This method has a certain risk of firmware upgrade failure when facing complex network environments and diversified device configurations, and its adaptability is limited when facing complex network environments and diversified device configurations.

[0055] In addition, there are some related technologies that are based on a centralized architecture, which push the firmware upgrade package to the target power grid equipment to be upgraded through a centralized server, so that the target power grid equipment completes the firmware upgrade. This method has a large resource overhead when facing high-concurrency upgrade scenarios, and the firmware upgrade is more complex and costly.

[0056] In view of this, an embodiment of the present invention provides a firmware upgrade method, system, device and medium, wherein the method updates the current upgrade strategy (i.e., the second upgrade strategy) of the power grid equipment through the risk level classification results obtained through real-time risk analysis, and performs firmware upgrades on the power grid equipment based on the second upgrade strategy. This can effectively improve the adaptability of the power grid equipment in the face of complex network environments and diversified equipment configurations, and is conducive to reducing the failure risk of the power grid equipment during the firmware upgrade process.

[0057] In addition, this method realizes the firmware upgrade of power grid equipment based on edge nodes and IoT platforms in different architectural positions. Specifically, the current upgrade strategy of the power grid equipment (i.e., the second upgrade strategy) is determined by the IoT platform, and the firmware of the power grid equipment is upgraded based on the second upgrade strategy through the edge node. This method effectively reduces the required resource overhead and the complexity of firmware upgrades when facing high-concurrency upgrade scenarios, thereby reducing costs.

[0058] Reference Figure 1In an embodiment of the present application, a firmware upgrade method is applied to a firmware upgrade system, wherein the firmware upgrade system includes an Internet of Things platform and an edge node. The method includes:

[0059] Step 110: The IoT platform obtains current operating status data and a first upgrade strategy of the power grid device;

[0060] In the embodiments of this application, the IoT platform in the firmware upgrade system can be connected to multiple edge nodes based on a distributed architecture. The IoT platform's architectural location is different from the architectural location of each edge node. In actual applications, each edge node is connected to multiple power grid devices, which can be of various types, such as electric meters, and this application will not further elaborate on this.

[0061] It is understood that the current operating status data obtained by the IoT platform may be obtained by edge nodes after real-time acquisition of the operating status of power grid equipment and transmitted to the IoT platform by edge nodes. This current operating status data is used to indicate the operating status of the corresponding power grid equipment at the current point in time. It specifically includes the current firmware version number of the power grid equipment, hardware configuration parameters (such as processor model and memory capacity), network connection status (such as signal strength and network latency), historical vulnerability records, and environmental variables (such as temperature and humidity). The first upgrade strategy is the second upgrade strategy at the time point before the current time point.

[0062] Step 120: The IoT platform inputs the current operating status data into the updated upgrade risk analysis model for risk analysis, and obtains a risk level classification result output by the updated upgrade risk analysis model, as well as weights of several risk factors of the risk level classification result.

[0063] In an embodiment of the present application, for any power grid device, after obtaining the current operating status data of the power grid device, the Internet of Things platform can input the current operating status data of the power grid device into an updated upgrade risk analysis model, and use the updated upgrade risk analysis model to predict and analyze the risk level of the power grid device, so as to obtain a risk level classification result and a number of risk factor weights corresponding to the risk level classification result. The risk level classification result can specifically be high risk, medium risk, low risk, etc. The example of this application is for illustration only. For example, if there are multiple attack records in the historical vulnerability records of a certain power grid device, and the current network delay exceeds 200ms, the determined risk level classification result is high risk, and several risk factor weights are output (such as vulnerability accounts for 60%, network status accounts for 30%, and environmental variables account for 10%).

[0064] In some embodiments, the updated upgrade risk analysis model is obtained by the following steps:

[0065] Acquire historical operating status data of the power grid device and behavior alarm data corresponding to the historical operating status data, wherein a data value of the behavior alarm data is greater than or equal to a dynamic threshold;

[0066] The historical operating status data and the behavior alarm data are input into the current upgrade risk analysis model for supervised training to obtain the updated upgrade risk analysis model.

[0067] In an embodiment of the present application, the current upgrade risk analysis model can be an upgrade risk analysis model preset in the Internet of Things platform, or it can be an updated upgrade risk analysis model at a previous time point; the behavior alarm data can be obtained by embedding an abnormality monitoring module in the power grid equipment, and using the abnormality monitoring module to collect performance indicators such as the CPU occupancy rate, memory usage rate and network resource occupancy rate of the power grid equipment in real time, as well as monitoring behavioral indicators such as middleman behavior, data tampering and abnormal retransmission behavior through traffic analysis, and then generating corresponding behavior alarm data when the data value of any performance indicator or behavior indicator is greater than or equal to the dynamic threshold, and transmitting the behavior alarm data to the Internet of Things platform via the edge node through the abnormality monitoring module.

[0068] It can be understood that if the current upgrade risk analysis model is an updated upgrade risk analysis model at a previous time point, the historical operating status data and behavior alarm data at the previous time point can be input into the current upgrade risk analysis model, and the current upgrade risk analysis model can be trained through a supervised learning algorithm to obtain an updated upgrade risk analysis model. The upgrade risk analysis model can specifically be any one of a support vector machine (SVM), a random forest model, a Bayesian network model, etc., which will not be repeated in this application.

[0069] Furthermore, behavioral alert data is obtained, including:

[0070] Obtaining original behavior resource data of the power grid device;

[0071] Comparing the behavior resource data with the dynamic threshold to obtain a threshold comparison result;

[0072] If the threshold comparison result is that the behavior resource data is less than the dynamic threshold, return to the step of obtaining the original behavior resource data of the power grid device; or, if the threshold comparison result is that the behavior resource data is greater than or equal to the dynamic threshold, determine the original behavior resource data as the behavior alarm data.

[0073] In an embodiment of the present application, if the time point of the current operating status data of the power grid equipment is the current time point, the original behavior resource data may be the behavior resource data at the current time point, or the behavior resource data at the time point before the current time point; the behavior resource data includes performance indicators such as CPU occupancy, memory usage, and network resource occupancy, as well as behavior indicators such as middleman behavior, data tampering, and abnormal retransmission behavior.

[0074] It is understandable that for any power grid device, after collecting the original behavior resource data of the power grid device, the abnormality monitoring module can compare the size relationship between each indicator in the behavior resource data and the corresponding dynamic threshold to obtain the threshold comparison result.

[0075] Specifically, if each indicator in the behavior resource data is less than the corresponding dynamic threshold, a threshold comparison result can be generated to indicate that the behavior resource data is less than the dynamic threshold. This indicates that the power grid equipment is operating normally, and the step of obtaining the original behavior resource data of the power grid equipment can be returned.

[0076] Alternatively, if there is at least one indicator in the behavior resource data that is greater than or equal to the corresponding dynamic threshold, it means that the operation of the power grid equipment is partially or completely abnormal. At this time, the original behavior resource data can be directly determined as behavior alarm data, or at least one indicator in the original behavior resource data that is greater than or equal to the corresponding dynamic threshold can be determined as behavior alarm data.

[0077] Furthermore, the method further comprises:

[0078] Obtaining a trained dynamic threshold analysis model, as well as intermediate state data and intermediate alarm data, wherein the intermediate state data is the previous running state data of the current running state data, and the intermediate alarm data is the behavior alarm data corresponding to the intermediate state data;

[0079] The intermediate state data and the intermediate alarm data are input into the trained dynamic threshold analysis model to perform threshold prediction, and obtain a dynamic threshold corresponding to the current operating state data.

[0080] In an embodiment of the present application, for the abnormality monitoring module embedded in the power grid equipment, the dynamic threshold used at each time point can be dynamically changed. Specifically, for the current time point, the operating status data of the time point before the current time point can be obtained, recorded as intermediate state data, and the behavioral alarm data of the time point before the current time point can be obtained, recorded as intermediate alarm data. Then, the intermediate state data and the intermediate alarm data are input into a trained dynamic threshold analysis model, which can be a linear regression model, etc. The dynamic threshold of the current time point is predicted by the trained dynamic threshold analysis model, so as to obtain the dynamic threshold corresponding to the current operating status data.

[0081] Step 130: The IoT platform updates the first upgrade strategy according to the risk level classification result to obtain a second upgrade strategy for the power grid device;

[0082] In an embodiment of the present application, the Internet of Things platform can determine the upgrade strategy corresponding to the risk level based on the risk level indicated by the risk level classification result, and replace the first upgrade strategy based on the upgrade strategy corresponding to the risk level, thereby obtaining the upgrade strategy of the power grid equipment at the current time point, which is recorded as the second upgrade strategy.

[0083] In some embodiments, updating the first upgrade strategy according to the risk level classification result to obtain a second upgrade strategy for the power grid device includes:

[0084] Obtaining a preset risk mapping table, wherein the risk mapping table is used to indicate a mapping relationship between risk levels and escalation strategies;

[0085] According to the risk level classification result, the risk mapping table is looked up and mapped to obtain a target upgrade strategy corresponding to the risk level classification result;

[0086] The first upgrade strategy is updated according to the target upgrade strategy to obtain a second upgrade strategy for the power grid device.

[0087] In an embodiment of the present application, a risk mapping table records mapping relationships between several risk levels and several upgrade strategies, with each risk level corresponding to an upgrade strategy. Specifically, the table lookup mapping may be based on the risk level indicated by the risk level classification result, determining a mapping relationship group between the risk level and the corresponding upgrade strategy from the risk mapping table, and determining a target upgrade strategy based on the upgrade strategy in the mapping relationship group. The target upgrade strategy may specifically be a low-risk upgrade strategy, a medium-risk upgrade strategy, or a high-risk strategy; then, the target upgrade strategy is used to replace the first upgrade strategy, and the replaced first upgrade strategy is determined as the second upgrade strategy for the power grid device at the current time point.

[0088] Step 140: The edge node upgrades the firmware of the power grid device according to the second upgrade strategy and all the risk factor weights to obtain the power grid device after the firmware upgrade.

[0089] In an embodiment of the present application, after the Internet of Things platform obtains the second upgrade strategy and all risk factor weights, the Internet of Things platform can transmit the second upgrade strategy and all risk factor weights to the corresponding edge node, and execute the second upgrade strategy through the edge node, and use the risk factor weights to upgrade the firmware of the power grid equipment, thereby obtaining the power grid equipment after the firmware upgrade.

[0090] In some embodiments, performing a firmware upgrade on the power grid device according to the second upgrade strategy and all the risk factor weights to obtain a power grid device with upgraded firmware includes:

[0091] If the second upgrade strategy is a high-risk upgrade strategy, obtaining original device version information of the power grid device, and performing a firmware rollback on the power grid device according to the original device version information;

[0092] Alternatively, if the upgrade strategy is a medium-risk upgrade strategy, obtaining risk upgrade instructions corresponding to all the risk factor weights, original device version information of the power grid device, and target device version information; performing a firmware upgrade on the power grid device according to the risk upgrade instruction, the original device version information, and the target device version information to obtain the power grid device after the firmware upgrade;

[0093] Alternatively, if the upgrade strategy is a low-risk upgrade strategy, the original device version information and the target device version information of the power grid device are obtained, and the firmware of the power grid device is upgraded according to the original device version information and the target device version information to obtain the power grid device after the firmware upgrade.

[0094] In an embodiment of the present application, if the second upgrade strategy of the power grid device at the current time point is a high-risk strategy, the edge node can automatically suspend the firmware upgrade process, obtain the hash value and digital signature of the previous stable version of the power grid device based on blockchain traceability technology, record it as the original device version information, and restore the firmware version of the power grid device to the stable version indicated by the original device version information through the differential upgrade algorithm and the original device version information.

[0095] It is understandable that if the second upgrade strategy of the power grid device at the current point in time is a medium-risk strategy, the edge node can input the weights of all risk factors into the pre-built AI agent for decision-making, thereby obtaining the risk upgrade instruction provided by the AI ​​agent. Specifically, if the risk upgrade instruction is to stop the upgrade, the firmware of the power grid device can be rolled back based on the original device version information of the power grid device; alternatively, if the risk upgrade instruction is to continue the upgrade, the firmware of the power grid device can be upgraded based on the original device version information and the target device version information of the power grid device, thereby obtaining the power grid device after the firmware upgrade, and the target device version information is used to indicate the target firmware version of the power grid device.

[0096] It should be noted that if the second upgrade strategy of the power grid device at the current time point is a low-risk strategy, the edge node can directly upgrade the firmware of the power grid device based on the original device version information and the target device version information of the power grid device, thereby obtaining the power grid device with upgraded firmware.

[0097] Furthermore, performing a firmware upgrade on the power grid device according to the original device version information and the target device version information to obtain the power grid device after the firmware upgrade includes:

[0098] Acquire an original version file corresponding to the original device version information and a target version file corresponding to the target device version information;

[0099] Comparing the original version file and the target version file to obtain file difference information;

[0100] Constructing an incremental upgrade package, and a unique identifier and a timestamp of the incremental upgrade package according to the file difference information;

[0101] Encrypting the incremental upgrade package according to the unique identifier and the timestamp to obtain an incremental encrypted package;

[0102] The firmware of the power grid device is upgraded according to the incremental encrypted package to obtain the power grid device after the firmware upgrade.

[0103] In an embodiment of the present application, the edge node can obtain the original version file based on the stable version indicated in the original device version information, and obtain the target version file based on the version to be upgraded indicated in the target device version information; then, based on the differential upgrade algorithm, the difference between the original version file and the target version file is determined through binary comparison, recorded as file difference information, and an incremental upgrade package is generated based on the file difference information. The incremental upgrade package contains the version number of the target version file, the version number, unique identifier and timestamp of the original version file, etc.

[0104] It is understandable that after the incremental upgrade package is constructed, the incremental upgrade package can be encrypted using compression encryption technology, and the encrypted incremental upgrade package (i.e., incremental encrypted package) can be transmitted to the corresponding power grid device, so that the power grid device completes the firmware upgrade and obtains the power grid device after the firmware upgrade.

[0105] It should be noted that after receiving the incremental encrypted package, the edge node can also write the hash value, digital signature, timestamp, and unique identifier of the incremental encrypted package into the distributed ledger of the blockchain. Based on this distributed ledger and smart contract technology, the source legitimacy and version consistency verification of the firmware upgrade system upgrade package can be implemented. For the firmware upgrade process of any power grid device, secure communication protocols such as TLS / DTLS can be applied to the communication link between the edge node and the power grid device, and encryption algorithms such as SM2 and SM4 can be used to achieve two-way recognition and data encryption between the edge node and the power grid device. This application will not go into details here.

[0106] A firmware upgrade system proposed according to an embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0107] Reference Figure 2 , a firmware upgrade system proposed in an embodiment of the present application includes:

[0108] The Internet of Things platform 101 is configured to obtain current operating status data and a first upgrade strategy for a power grid device; input the current operating status data into an updated upgrade risk analysis model for risk analysis, thereby obtaining a risk level classification result output by the updated upgrade risk analysis model, as well as weights of several risk factors of the risk level classification result; and update the first upgrade strategy based on the risk level classification result to obtain a second upgrade strategy for the power grid device.

[0109] The edge node 102 is configured to upgrade the firmware of the power grid device according to the second upgrade strategy and all the risk factor weights to obtain the power grid device after the firmware upgrade.

[0110] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0111] Reference Figure 3 , an embodiment of the present application further provides an electronic device, including:

[0112] at least one processor 201;

[0113] At least one memory 202, configured to store at least one program;

[0114] When the at least one program is executed by the at least one processor 201 , the at least one processor 201 implements the above method embodiment.

[0115] Similarly, it can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0116] An embodiment of the present application further provides a computer-readable storage medium, in which a program executable by the processor 201 is stored. The program executable by the processor 201 is used to implement the above-mentioned method embodiment when executed by the processor 201.

[0117] Similarly, the contents of the above method embodiments are applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0118] In some optional embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, the two boxes shown in succession may actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flow chart of the present application are provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logic flows presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0119] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present application. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present application as set forth in the claims using ordinary techniques without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the appended claims and their equivalents.

[0120] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the embodiment method of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0121] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0122] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0123] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0124] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples.

[0125] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and intent of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0126] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present application, and these equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A firmware upgrade method, characterized in that: Applied to a firmware upgrade system, the firmware upgrade system includes an Internet of Things platform and an edge node, and the method includes: The Internet of Things platform obtains current operating status data and a first upgrade strategy of the power grid device; The Internet of Things platform inputs the current operating status data into the updated upgrade risk analysis model for risk analysis, and obtains a risk level classification result output by the updated upgrade risk analysis model, as well as several risk factor weights of the risk level classification result; The Internet of Things platform updates the first upgrade strategy according to the risk level classification result to obtain a second upgrade strategy for the power grid device; The edge node upgrades the firmware of the power grid device according to the second upgrade strategy and all the risk factor weights to obtain the power grid device after the firmware upgrade.

2. The method according to claim 1, characterized in that The updated upgrade risk analysis model is obtained by the following steps: Acquire historical operating status data of the power grid device and behavior alarm data corresponding to the historical operating status data, wherein a data value of the behavior alarm data is greater than or equal to a dynamic threshold; The historical operating status data and the behavior alarm data are input into the current upgrade risk analysis model for supervised training to obtain the updated upgrade risk analysis model.

3. The method according to claim 2, characterized in that Obtain behavioral alert data, including: Obtaining original behavior resource data of the power grid device; Comparing the behavior resource data with the dynamic threshold to obtain a threshold comparison result; If the threshold comparison result is that the behavior resource data is less than the dynamic threshold, return to the step of obtaining the original behavior resource data of the power grid device; or, if the threshold comparison result is that the behavior resource data is greater than or equal to the dynamic threshold, determine the original behavior resource data as the behavior alarm data.

4. The method according to claim 3, characterized in that The method further comprises: Obtaining a trained dynamic threshold analysis model, as well as intermediate state data and intermediate alarm data, wherein the intermediate state data is the previous running state data of the current running state data, and the intermediate alarm data is the behavior alarm data corresponding to the intermediate state data; The intermediate state data and the intermediate alarm data are input into the trained dynamic threshold analysis model to perform threshold prediction, and obtain a dynamic threshold corresponding to the current operating state data.

5. The method according to claim 1, wherein The updating of the first upgrade strategy according to the risk level classification result to obtain a second upgrade strategy for the power grid device includes: Obtaining a preset risk mapping table, wherein the risk mapping table is used to indicate a mapping relationship between risk levels and escalation strategies; According to the risk level classification result, the risk mapping table is looked up and mapped to obtain a target upgrade strategy corresponding to the risk level classification result; The first upgrade strategy is updated according to the target upgrade strategy to obtain a second upgrade strategy for the power grid device.

6. The method according to claim 1, characterized in that The step of performing firmware upgrade on the power grid device according to the second upgrade strategy and all the risk factor weights to obtain the power grid device after firmware upgrade includes: If the second upgrade strategy is a high-risk upgrade strategy, obtaining original device version information of the power grid device, and performing a firmware rollback on the power grid device according to the original device version information; Alternatively, if the upgrade strategy is a medium-risk upgrade strategy, obtaining risk upgrade instructions corresponding to all the risk factor weights, original device version information of the power grid device, and target device version information; performing a firmware upgrade on the power grid device according to the risk upgrade instruction, the original device version information, and the target device version information to obtain the power grid device after the firmware upgrade; Alternatively, if the upgrade strategy is a low-risk upgrade strategy, the original device version information and the target device version information of the power grid device are obtained, and the firmware of the power grid device is upgraded according to the original device version information and the target device version information to obtain the power grid device after the firmware upgrade.

7. The method according to claim 6, characterized in that The step of performing a firmware upgrade on the power grid device according to the original device version information and the target device version information to obtain the power grid device after the firmware upgrade includes: Acquire an original version file corresponding to the original device version information and a target version file corresponding to the target device version information; Comparing the original version file and the target version file to obtain file difference information; Constructing an incremental upgrade package, and a unique identifier and a timestamp of the incremental upgrade package according to the file difference information; Encrypting the incremental upgrade package according to the unique identifier and the timestamp to obtain an incremental encrypted package; The firmware of the power grid device is upgraded according to the incremental encrypted package to obtain the power grid device after the firmware upgrade.

8. A firmware upgrade system, characterized in that: include: IoT platform, used to obtain current operating status data and first upgrade strategy of power grid equipment; Inputting the current operating status data into an updated upgrade risk analysis model for risk analysis, obtaining a risk level classification result output by the updated upgrade risk analysis model, and a plurality of risk factor weights of the risk level classification result; updating the first upgrade strategy according to the risk level classification result to obtain a second upgrade strategy for the power grid device; The edge node is configured to perform a firmware upgrade on the power grid device according to the second upgrade strategy and all the risk factor weights to obtain a power grid device with upgraded firmware.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 7 when executed by the processor.

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