Remote debugging and management method, system and device for edge intelligent power equipment and medium

Abstract

The invention discloses a remote debugging and management method, system and device for edge intelligent power equipment, and a storage medium. The method comprises the following steps: accessing the edge intelligent power equipment to a remote management platform; based on the remote management platform, performing dynamic environment judgment on the edge intelligent power equipment to obtain a judgment result; if the judgment result is a first judgment result, performing staged updating on the edge intelligent power equipment and detecting whether updating abnormity exists in the updating process or not; the first judgment result is used for representing that the edge intelligent power equipment meets a preset updating condition; and if yes, detecting an exception type corresponding to the updating exception, and generating a self-adaptive decision based on the exception type. The method can be widely applied to the technical field of power Internet of Things.

Description

Technical Field

[0001] The present application relates to the technical field of power Internet of Things, and in particular to a remote debugging and management method, system, device and storage medium for edge intelligent power equipment. Background Art

[0002] In the field of power Internet of Things, with the integration of smart grid and edge computing technologies, the demand for remote operation and maintenance of power equipment (such as smart meters, distribution terminals, and relay protection devices) has surged. Traditional remote management solutions have the following technical bottlenecks: Low update reliability: The full update mode is prone to failure when the network fluctuates or the device is highly loaded, and there is a lack of breakpoint resumption and rollback mechanisms, resulting in the risk of equipment bricking. Lack of environmental perception: Traditional solutions rely only on static thresholds (such as CPU load <80%) and cannot dynamically adapt to changes in device operating status and network quality, resulting in update interruptions or equipment downtime. Delayed exception handling: There is a strong reliance on manual intervention, a lack of self-learning capabilities for unknown errors, and a long fault recovery time. Therefore, there are still technical problems that need to be solved in related technologies. Summary of the Invention

[0003] The purpose of this application is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0004] To this end, one purpose of the embodiments of the present application is to provide a remote debugging and management method, system, device and storage medium for edge intelligent power equipment, which can reduce the risk of equipment bricking, improve update stability and improve the timeliness of update exception handling.

[0005] In order to achieve the above-mentioned technical objectives, the technical solutions adopted in the embodiments of the present application include: a remote debugging and management method for edge intelligent power equipment, comprising: connecting the edge intelligent power equipment to a remote management platform; based on the remote management platform, performing dynamic environmental judgment on the edge intelligent power equipment to obtain a judgment result; if the judgment result is a first judgment result, updating the edge intelligent power equipment in stages and detecting whether there is an update anomaly in the update process; the first judgment result is used to characterize that the edge intelligent power equipment meets the preset update conditions; if so, detecting the anomaly type corresponding to the update anomaly, and generating an adaptive decision based on the anomaly type.

[0006] In addition, the method for remote debugging and management of edge intelligent power equipment according to the above embodiment of the present invention may also have the following additional technical features:

[0007] Furthermore, in the embodiment of the present application, connecting the edge intelligent power equipment to the remote management platform specifically includes:

[0008] Connecting the edge smart power device to the remote management platform through a security protocol to ensure communication encryption;

[0009] A lightweight rule engine is pre-installed on the edge smart power device to perform preliminary filtering and anomaly marking on local data;

[0010] Based on the RBAC model, operation permissions are assigned to the edge intelligent power equipment.

[0011] Furthermore, in an embodiment of the present application, the dynamic environment judgment includes edge-side pre-judgment and cloud-side deep judgment; the edge-side pre-judgment step is specifically as follows:

[0012] Run the device-side rule engine and detect the local state parameters of the edge intelligent power device,

[0013] If the local state parameter is less than or equal to a preset first state parameter threshold, a first judgment result is generated.

[0014] Furthermore, in the embodiment of the present application, the cloud-based depth determination step is specifically as follows:

[0015] Detect the network quality of the edge intelligent power equipment and update package parameters,

[0016] If the network quality is greater than or equal to a preset second state parameter threshold, or the update package parameters are all compatible items, a first judgment result is generated.

[0017] Furthermore, in the embodiment of the present application, the edge intelligent power equipment is updated in stages, specifically:

[0018] Splitting the update package to be sent to the edge intelligent power device to obtain a driver layer update package, an application layer update package, and a configuration file update package;

[0019] The driver layer update package, the application layer update package, and the configuration file update package are sent to the edge intelligent power device according to a preset priority, so that the edge intelligent power device is updated.

[0020] Furthermore, in an embodiment of the present application, generating an adaptive decision based on the anomaly type includes:

[0021] Define an exception type library and identify the exception type of the edge smart power device during the update process through log pattern matching;

[0022] If the abnormality type is a recoverable error, controlling the edge intelligent power device to automatically re-update;

[0023] If the exception type is an unrecoverable error, a rollback mechanism is triggered, a backup image is loaded, and the edge smart power device is marked as a high-risk device;

[0024] If the exception type is an unknown error, generate a complete log and push the complete log to the artificial expert system for decision-making assistance;

[0025] If the exception type is local fault tolerance on the edge side, the edge intelligent power device is controlled to preset a minimized recovery image so that the minimized recovery image is automatically loaded when the cloud instruction cannot reach the edge intelligent power device.

[0026] Furthermore, in an embodiment of the present application, the method also includes: aggregating update logs and generating a health report of the edge smart power device.

[0027] On the other hand, an embodiment of the present application further provides a remote debugging and management system for edge intelligent power equipment, including:

[0028] A first processing unit is used to connect the edge smart power equipment to the remote management platform;

[0029] A second processing unit is configured to perform dynamic environmental judgment on the edge intelligent power device based on the remote management platform to obtain a judgment result;

[0030] A third processing unit is configured to, if the judgment result is a first judgment result, perform a phased update on the edge intelligent power device and detect whether there is an update anomaly during the update process; the first judgment result is used to indicate that the edge intelligent power device meets a preset update condition;

[0031] The fourth processing unit is configured to detect an anomaly type corresponding to the update anomaly, if any, and generate an adaptive decision based on the anomaly type.

[0032] On the other hand, the present application also provides a remote debugging and management device for edge intelligent power equipment, including:

[0033] at least one processor;

[0034] at least one memory for storing at least one program;

[0035] When the at least one program is executed by the at least one processor, the at least one processor implements a remote debugging and management method for edge intelligent power equipment as described in any one of the invention contents.

[0036] In addition, the present application also provides a computer-readable storage medium, which stores processor-executable instructions. When the processor executes the instructions, the processor-executable instructions are used to execute a remote debugging and management method for edge intelligent power equipment as described in any of the above items.

[0037] 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:

[0038] This application connects the edge intelligent power equipment to the remote management platform; based on the remote management platform, the edge intelligent power equipment is dynamically judged to obtain a judgment result; if the judgment result is the first judgment result, the edge intelligent power equipment is updated in stages and the update process is detected whether there is an update anomaly; the first judgment result is used to characterize that the edge intelligent power equipment meets the preset update conditions; if so, the anomaly type corresponding to the update anomaly is detected, and an adaptive decision is generated based on the anomaly type; this application can update the edge intelligent power equipment according to the dynamic environment judgment result and determine whether there is an update anomaly during the update. If so, an adaptive decision can be generated based on the anomaly type. This application can improve the update stability and improve the timeliness of update anomaly processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a schematic diagram of the steps of a remote debugging and management method for edge intelligent power equipment in a specific embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of the steps of a remote debugging and management method for edge intelligent power equipment in another specific embodiment of the present invention;

[0041] Figure 3 A schematic diagram of a process for dynamic environment judgment in a specific embodiment of the present invention;

[0042] Figure 4 This is a structural diagram of a remote debugging and management system for edge intelligent power equipment in a specific embodiment of the present invention;

[0043] Figure 5 This is a structural diagram of a remote debugging and management device for edge intelligent power equipment in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following describes in detail the embodiments of the present invention in conjunction with the accompanying drawings to illustrate the principles and processes of the remote debugging and management method, system, device and storage medium of the edge intelligent power equipment in the embodiments of the present invention.

[0045] Reference Figure 1The present application provides a remote debugging and management method for edge intelligent power equipment. The method may include steps S101 to S104.

[0046] S101. Connect edge smart power equipment to the remote management platform.

[0047] S102: Based on the remote management platform, perform dynamic environmental judgment on the edge intelligent power equipment to obtain a judgment result.

[0048] S103. If the judgment result is the first judgment result, the edge intelligent power equipment is updated in stages and whether there is an update anomaly in the update process; the first judgment result is used to indicate that the edge intelligent power equipment meets the preset update conditions.

[0049] S104: If so, detect the exception type corresponding to the update exception and generate an adaptive decision based on the exception type.

[0050] Furthermore, in the embodiment of the present application, connecting the edge intelligent power equipment to the remote management platform specifically includes:

[0051] Connect edge smart power devices to the remote management platform through secure protocols to ensure encrypted communications;

[0052] A lightweight rule engine is pre-installed on edge smart power equipment to perform preliminary filtering and anomaly marking on local data;

[0053] Based on the RBAC model, operation permissions are assigned to edge smart power equipment.

[0054] Furthermore, in the embodiment of the present application, dynamic environment judgment includes edge-side pre-judgment and cloud-side deep judgment; the edge-side pre-judgment step is specifically as follows:

[0055] Run the device-side rule engine and detect local status parameters of edge smart power devices,

[0056] If the local state parameter is less than or equal to a preset first state parameter threshold, a first judgment result is generated.

[0057] Furthermore, in the embodiment of the present application, the cloud depth determination step is specifically as follows:

[0058] Detect network quality of edge smart power equipment and update package parameters,

[0059] If the network quality is greater than or equal to the preset second status parameter threshold, or the update package parameters are all compatible items, a first judgment result is generated.

[0060] Furthermore, in the embodiment of the present application, the edge intelligent power equipment is updated in stages, specifically:

[0061] Split the update package to be sent to the edge smart power device into a driver layer update package, an application layer update package, and a configuration file update package;

[0062] The driver layer update package, application layer update package and configuration file update package are sent to the edge intelligent power device according to the preset priority, so that the edge intelligent power device is updated.

[0063] Furthermore, in an embodiment of the present application, generating an adaptive decision based on the anomaly type includes:

[0064] Define an exception type library and identify exception types during the update process of edge smart power devices through log pattern matching;

[0065] If the exception type is a recoverable error, the control edge intelligent power equipment will automatically re-update;

[0066] If the exception type is an unrecoverable error, the rollback mechanism is triggered, the backup image is loaded, and the edge smart power device is marked as a high-risk device;

[0067] If the exception type is an unknown error, a complete log is generated and pushed to the artificial expert system for decision-making assistance;

[0068] If the exception type is local fault tolerance on the edge side, the edge intelligent power device is controlled to preset a minimized recovery image so that the minimized recovery image is automatically loaded when the cloud instruction cannot reach the edge intelligent power device.

[0069] Furthermore, in an embodiment of the present application, the method also includes: aggregating update logs and generating a health report of the edge smart power equipment.

[0070] The specific implementation principle of this application is described below with reference to the accompanying drawings:

[0071] This embodiment solves the problems of low update reliability, lack of environmental perception and delayed exception handling of traditional remote management solutions by providing a remote debugging and management method for edge intelligent power equipment.

[0072] Deploy an LSTM time series model, input historical 72 hours of device operation data, such as memory usage and voltage fluctuations, predict status trends for the next 15 minutes, and dynamically adjust thresholds, such as lowering the CPU load threshold from 70% to 65%. Monitor bandwidth, latency, and packet loss rate, and calculate the Network Health Index (NHI) based on the QoS model. If the NHI is less than 0.7 (out of a maximum score of 1.0), pause the update, parse the version number, dependency library, and hardware adaptation list, and compare them with the device configuration. If any incompatibilities exist, such as a firmware version span greater than 3 levels, terminate the update and trigger manual review. Combine edge-side pre-judgment with cloud-side in-depth judgment to dynamically adjust the update strategy, so that the CPU load threshold and network weight parameters adapt to the device status and improve device availability.

[0073] The update package is split into driver layer, application layer, and configuration file, and pushed according to priority. The device caches the received modules and resumes the download from the breakpoint after network interruption, supporting up to 5 recovery times. Automated testing is performed after each stage is completed, and the next stage will be entered after passing the test. Combining breakpoint resumption and automated testing can reduce the update failure rate.

[0074] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0075] An embodiment of the present invention discloses a remote debugging and management method for edge intelligent power equipment.

[0076] According to the attached Figure 2-3 As shown, this embodiment includes the following steps:

[0077] Step 1: Device access and identity authentication: connect edge smart power devices to the remote management platform;

[0078] Step 2: Dynamic environment judgment, which includes edge-side pre-judgment, cloud-side deep judgment, and edge-cloud data synchronization;

[0079] Step 3: Perform the update in stages, splitting the update package into multiple modules and pushing them in order of priority. After each stage is completed, a self-check is triggered.

[0080] Step 4: Intelligent exception handling, making adaptive decisions based on the exception type;

[0081] Step 5: Update result feedback and learning.

[0082] Furthermore, the device access and identity authentication are specifically as follows:

[0083] The device accesses the remote management platform via TLS / DTLS or MQTT over TLS protocols to ensure encrypted communication;

[0084] A lightweight rule engine is pre-installed on the device to perform preliminary filtering and anomaly marking on local data;

[0085] The platform verifies the device's digital certificate or fingerprint, and the device verifies the platform's certificate to prevent man-in-the-middle attacks.

[0086] Assign operational permissions based on the RBAC model. For example, a "debugger" can only execute code pushes, and an "auditor" can only view logs.

[0087] Adopting TLS / DTLS and MQTT over TLS protocol stacks, combined with cross-validation of device digital certificates and platform certificates, a full-link encrypted channel from the physical layer to the application layer is constructed. A lightweight rule engine is pre-installed on the device side, such as a real-time filtering module based on Lua scripts, to achieve local data cleaning and anomaly marking, reducing the cloud load. Through role granularity control, such as "debuggers" can only execute code pushes, and "auditors" can only view logs, combined with operation log blockchain evidence,

[0088] Specifically disclosed, the edge-side pre-judgment step is specifically as follows:

[0089] The device-side rule engine checks local status, such as CPU load and temperature. If a threshold is triggered, such as CPU load > 80%, the update is directly rejected and the reason is reported.

[0090] Edge-side pre-judgment: The device-side rule engine integrates a hardware monitoring module and uses a sliding window algorithm to calculate the load average. When the CPU usage is greater than 80% or the temperature exceeds a threshold, such as 75°C, fuse protection is triggered.

[0091] Specifically disclosed, the cloud-based depth determination steps are as follows:

[0092] Device status judgment: Collect device operating data, such as memory usage and voltage stability, predict future status trends through the LSTM model, and dynamically adjust thresholds, such as lowering the CPU load threshold from 70% to 65%;

[0093] Status prediction: Deploy an LSTM time series model, input historical 72 hours of device operation data, such as memory usage and voltage fluctuations, predict status trends for the next 15 minutes, and dynamically adjust thresholds, such as lowering the CPU load threshold from 70% to 65%.

[0094] Network quality assessment: Monitor bandwidth, latency, and packet loss rate, and calculate the Network Health Index (NHI) based on the QoS model. If NHI is less than 0.7 (out of a maximum score of 1.0), updates will be suspended.

[0095] Network evaluation: The NHI index is calculated based on the QoS model. The formula is:

[0096] NHI=0.4×BW avail / BW max +0.3×(1-PLR)

[0097] BW avail is the available bandwidth, BW max is the maximum bandwidth, PLR is the packet loss rate. When NHI < 0.7, the update is suspended.

[0098] Update package compatibility check: Analyze the version number, dependency library, and hardware adapter list, and compare them with the device configuration. If there are incompatibilities, such as a firmware version span > 3 levels, the update is terminated and a manual review is triggered.

[0099] Compatibility verification: Analyze the update package's version number, dependency library, and hardware adapter list, and use hash comparison to prevent firmware rollback attacks.

[0100] Specifically disclosed, the edge-cloud data synchronization steps are as follows:

[0101] The pre-judgment results on the device are uploaded to the cloud through an encrypted channel, and the cloud makes a comprehensive decision on whether to execute the update.

[0102] Specifically disclosed, the phased update execution steps are as follows:

[0103] Update package splitting and priority definition: The update package is split into three stages: driver layer, application layer, and configuration file, and pushed according to high, medium, and low priorities;

[0104] Edge-side caching and breakpoint resumption: The device caches received modules and resumes transmission from the breakpoint after a network interruption, reducing repeated transmissions.

[0105] Stage self-inspection and testing: After each stage is completed, automated testing is performed, such as communication protocol testing and IO port response testing. Only after passing the test can the next stage be entered. The test case library includes three types of test cases: functional testing, performance testing, and security testing.

[0106] The update package is split into driver layer, application layer, and configuration file. The priorities of the driver layer, application layer, and configuration file are P0, P1, and P2 respectively. A priority scheduling algorithm is used to ensure that key modules are transmitted first.

[0107] Resume download: The device caches the module to NAND flash memory and resumes downloading via HTTP Range request after network interruption, supporting up to five breakpoint recovery attempts.

[0108] Stage self-test: After each stage is completed, an automated test suite is executed, including:

[0109] Functional test: Modbus protocol consistency check;

[0110] Performance test: IO throughput benchmark test, such as SPI interface rate > 10Mbps;

[0111] Security testing: fuzz testing verifies input validation;

[0112] Specifically disclosed, the intelligent exception handling steps are as follows:

[0113] Abnormal type identification: Define an abnormal type library, such as network interruption, file corruption, and hardware conflict, and identify abnormalities through log pattern matching;

[0114] Adaptive processing strategy:

[0115] Recoverable errors, such as temporary network fluctuations: Automatic retry, with a maximum of 3 retries, and an exponentially increasing retry interval, such as 1s → 2s → 4s;

[0116] Unrecoverable errors, such as hardware incompatibility, trigger a rollback mechanism, load a backup image, and mark the device as high-risk.

[0117] Unknown error: Generates a complete log including call stack and register status, and pushes it to the artificial expert system for decision-making assistance;

[0118] Local fault tolerance at the edge:

[0119] The device side presets a minimized recovery image, which is automatically loaded when cloud commands cannot be reached.

[0120] Furthermore, the update result feedback and learning steps are specifically as follows:

[0121] Log summary and analysis: Summarize update logs, including success rate, time consumption, and exception types;

[0122] Machine learning threshold optimization: Based on historical data training models, thresholds are dynamically adjusted, such as CPU load thresholds and network quality weights.

[0123] Device health report generation: Generate a report containing device status scores and update suggestions, and push it to the operation and maintenance terminal.

[0124] Deploy an LSTM time series model, input historical 72 hours of device operation data, such as memory usage and voltage fluctuations, predict status trends for the next 15 minutes, and dynamically adjust thresholds, such as lowering the CPU load threshold from 70% to 65%. Monitor bandwidth, latency, and packet loss rate, and calculate the Network Health Index (NHI) based on the QoS model. If the NHI is less than 0.7 (out of a maximum score of 1.0), pause the update, parse the version number, dependency library, and hardware adaptation list, and compare them with the device configuration. If any incompatibilities exist, such as a firmware version span greater than 3 levels, terminate the update and trigger manual review. Combine edge-side pre-judgment with cloud-side in-depth judgment to dynamically adjust the update strategy, so that the CPU load threshold and network weight parameters adapt to the device status and improve device availability.

[0125] The update package is split into driver layer, application layer, and configuration file, and pushed according to priority. The device caches the received modules and resumes the download from the breakpoint after network interruption, supporting up to 5 recovery times. Automated testing is performed after each stage is completed, and the next stage will be entered after passing the test. Combining breakpoint resumption and automated testing can reduce the update failure rate.

[0126] In addition, refer to Figure 4 ,and Figure 1 Corresponding to the method, an embodiment of the present application also provides a remote debugging and management system for edge intelligent power equipment. The system may include: a first processing unit 1001, a second processing unit 1002, a third processing unit 1003 and a fourth processing unit 1004. The first processing unit 1001 can be used to connect the edge intelligent power equipment to the remote management platform. The second processing unit 1002 can be used to perform dynamic environmental judgment on the edge intelligent power equipment based on the remote management platform to obtain a judgment result. The third processing unit 1003 can be used to update the edge intelligent power equipment in stages and detect whether there is an update anomaly in the update process if the judgment result is the first judgment result; the first judgment result is used to characterize that the edge intelligent power equipment meets the preset update conditions. The fourth processing unit 1004 can be used to detect the anomaly type corresponding to the update anomaly, if any, and generate an adaptive decision based on the anomaly type.

[0127] It should be noted that the contents of the above-mentioned embodiments of the remote debugging and management method for edge intelligent power equipment are all applicable to the embodiments of the remote debugging and management system for this edge intelligent power equipment. The functions specifically implemented by the embodiments of the remote debugging and management system for this edge intelligent power equipment are the same as those of the above-mentioned embodiments of the remote debugging and management method for edge intelligent power equipment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned embodiments of the remote debugging and management method for edge intelligent power equipment.

[0128] and Figure 1Corresponding to the method, the embodiment of the present application also provides a remote debugging and management device for edge intelligent power equipment, the specific structure of which can be referred to Figure 5 ,include:

[0129] at least one processor 1011;

[0130] at least one memory 1012, configured to store at least one program;

[0131] When the at least one program is executed by the at least one processor, the at least one processor implements the remote debugging and management method of the edge intelligent power equipment.

[0132] The contents of the above method embodiments are all 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.

[0133] and Figure 1 Corresponding to the method, an embodiment of the present application also provides a computer-readable storage medium, which stores processor-executable instructions, and the processor-executable instructions are used to execute the remote debugging and management method of the edge intelligent power equipment when executed by the processor.

[0134] The contents of the above-mentioned remote debugging and management method embodiments of edge intelligent power equipment are all applicable to the present storage medium embodiments. The functions specifically implemented by the present storage medium embodiments are the same as those of the above-mentioned remote debugging and management method embodiments of edge intelligent power equipment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned remote debugging and management method embodiments of edge intelligent power equipment.

[0135] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can 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 logical 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.

[0136] 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.

[0137] If the functions are implemented in the form of software functional units and sold or used as independent products, they 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 programs for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment 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.

[0138] The logic and / or steps represented in a flowchart or otherwise described herein, for example, may be considered as an ordered list of executable programs for implementing the logical functions, and may be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can retrieve and execute a program from a program execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, a program execution system, apparatus, or device.

[0139] 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.

[0140] 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 program 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.

[0141] 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.

[0142] 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.

[0143] 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. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A remote debugging and management method for edge intelligent power equipment, characterized in that: The following steps are involved: Connect edge smart power equipment to the remote management platform; Based on the remote management platform, dynamic environmental judgment is performed on the edge intelligent power equipment to obtain a judgment result; If the judgment result is the first judgment result, the edge intelligent power device is updated in stages and whether there is an update anomaly during the update process; The first judgment result is used to indicate that the edge intelligent power device meets the preset update condition; If so, detect the exception type corresponding to the update exception, and generate an adaptive decision based on the exception type.

2. A remote debugging and management method for edge intelligent power equipment according to claim 1, characterized in that: Connecting edge smart power equipment to the remote management platform specifically includes: Connecting the edge smart power device to the remote management platform through a security protocol to ensure communication encryption; A lightweight rule engine is pre-installed on the edge smart power device to perform preliminary filtering and anomaly marking on local data; Based on the RBAC model, operation permissions are assigned to the edge intelligent power equipment.

3. The remote debugging and management method of edge intelligent power equipment according to claim 1, characterized in that: The dynamic environment judgment includes edge-side pre-judgment and cloud-side deep judgment; The edge-side pre-judgment includes: Run the device-side rule engine and detect the local state parameters of the edge intelligent power device, If the local state parameter is less than or equal to a preset first state parameter threshold, a first judgment result is generated.

4. A remote debugging and management method for edge intelligent power equipment according to claim 3, characterized in that: The cloud-based depth judgment includes: Detect the network quality of the edge intelligent power equipment and update package parameters, If the network quality is greater than or equal to a preset second state parameter threshold, or the update package parameters are all compatible items, a first judgment result is generated.

5. The remote debugging and management method of edge intelligent power equipment according to claim 1, characterized in that: The phased updating of the edge intelligent power equipment includes: Splitting the update package to be sent to the edge intelligent power device to obtain a driver layer update package, an application layer update package, and a configuration file update package; The driver layer update package, the application layer update package, and the configuration file update package are sent to the edge intelligent power device according to a preset priority, so that the edge intelligent power device is updated.

6. A remote debugging and management method for edge intelligent power equipment according to claim 1, characterized in that: Generating an adaptive decision based on the exception type includes: Define an exception type library and identify the exception type of the edge smart power device during the update process through log pattern matching; If the abnormality type is a recoverable error, controlling the edge intelligent power device to automatically re-update; If the exception type is an unrecoverable error, a rollback mechanism is triggered, a backup image is loaded, and the edge smart power device is marked as a high-risk device; If the exception type is an unknown error, generate a complete log and push the complete log to the artificial expert system for decision-making assistance; If the exception type is local fault tolerance on the edge side, the edge intelligent power device is controlled to preset a minimized recovery image so that the minimized recovery image is automatically loaded when the cloud instruction cannot reach the edge intelligent power device.

7. A remote debugging and management method for edge intelligent power equipment according to claim 1, characterized in that: The method further comprises: Aggregate update logs and generate health reports for the edge smart power devices.

8. A remote debugging and management system for edge intelligent power equipment, characterized in that: include: A first processing unit is used to connect the edge smart power equipment to the remote management platform; A second processing unit is configured to perform dynamic environmental judgment on the edge intelligent power device based on the remote management platform to obtain a judgment result; A third processing unit is configured to, if the judgment result is the first judgment result, update the edge intelligent power device in stages and detect whether there is an update anomaly during the update process; The first judgment result is used to indicate that the edge intelligent power device meets the preset update condition; The fourth processing unit is configured to detect an anomaly type corresponding to the update anomaly, if any, and generate an adaptive decision based on the anomaly type.

9. A remote debugging and management device for edge intelligent power equipment, 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 remote debugging and management method of edge intelligent power equipment as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions executable by a processor, characterized in that: The processor-executable instructions, when executed by the processor, are used to execute a remote debugging and management method for edge intelligent power equipment as described in any one of claims 1-7.

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