Problem handling method and apparatus

Abstract

The application is suitable for the field of artificial intelligence technology, and provides a problem processing method and device. In the problem processing method, in response to a user inputted problem to be diagnosed, diagnosis is performed and a diagnosis result is obtained, a target mode for solving the problem is determined based on the diagnosis result, and a control instruction is sent to a target object to execute the mode. In the above manner, the user can complete the whole-link closed loop from problem diagnosis to mode execution in a single interaction, solving the problem that the existing operation and maintenance processing link is dispersed in multiple pages and cannot be comprehensively diagnosed, and significantly improving the accuracy of problem positioning and processing efficiency.

Description

Technical Field

[0001] This application belongs to the field of artificial intelligence technology, and in particular relates to problem-solving methods and apparatus. Background Technology

[0002] Currently, in equipment operation and maintenance scenarios, when users need to locate and handle equipment problems, they usually need to switch repeatedly between multiple independent function pages and then manually make a comprehensive judgment on the root cause of the problem.

[0003] This mode of dispersing the operation and maintenance process across multiple pages makes it impossible for users to obtain complete diagnostic information on a single interface, and it is also difficult to achieve comprehensive analysis and accurate location of problems, resulting in low diagnostic efficiency and inaccurate problem location. Summary of the Invention

[0004] This application provides a problem-solving method and apparatus that can solve the problems of low diagnostic efficiency and inaccurate problem location during operation and maintenance.

[0005] In a first aspect, embodiments of this application provide a problem-solving method, including: In response to receiving user input about a problem to be diagnosed, perform a diagnosis based on the problem and obtain the diagnosis result; Identify the target pattern for resolving the problem to be diagnosed; The control instructions corresponding to the target mode are written into the target object associated with the diagnostic results. The control instructions are used to control the target object to execute the target mode.

[0006] In one possible implementation of the first aspect, the diagnosis based on the problem to be diagnosed and the acquisition of diagnostic results include: Obtain the runtime data of the target object; Based on operational data, obtain diagnostic reference data; Diagnostic results are obtained by analyzing diagnostic reference data.

[0007] One possible implementation of the first aspect also includes: outputting diagnostic results to the user.

[0008] In one possible implementation of the first aspect, the diagnostic reference data includes at least one of the following: peak-shaving data of the target object and energy loss data of the target object; The diagnostic results include peak shaving estimation information of the target object and at least one item from the peak shaving line graph of the target object; wherein, the peak shaving estimation information includes at least one item from the peak shaving time information, power consumption information, and peak shaving power information of the target object; the peak shaving line graph includes at least one item from the power output curve, available power curve, difference information between the power output curve and the available power curve, and cause of the problem.

[0009] In one possible implementation of the first aspect, the target pattern for resolving the problem to be diagnosed is determined, including: Output recommendation information to the user, which is used to recommend at least one pattern to the user; The target pattern is determined based on the user's triggering action on the recommended information; wherein the recommended information includes at least one of the following: At least one title corresponding to each mode in the pattern; Reasons for the recommendation pattern; Prompt information used to guide users in selecting the execution mode; At least one selection control for each mode in the pattern, the selection control is used by the user to select the target mode; Confirmation control: The confirmation control is used by the user to confirm the user's current selection.

[0010] In one possible implementation of the first aspect, at least one of the title, recommendation reason, prompt information, selection control, and confirmation control is displayed in the recommendation information in a first preset order.

[0011] In one possible implementation of the first aspect, the target pattern is determined based on the user's triggering action on the recommendation information, including: In response to the user's trigger operation on the selection control and / or confirmation control, the mode corresponding to the trigger operation is determined to be the target mode; the problem handling method also includes: performing simulation based on the target mode and obtaining simulation results.

[0012] In one possible implementation of the first aspect, the method further includes: outputting simulation results to the user, wherein the simulation results include at least one of the following: Description of the target pattern; Comparison of simulation results between the current operating mode and the target mode; Simulation operation information is used to indicate the changes in key parameters corresponding to the target mode.

[0013] In one possible implementation of the first aspect, the explanatory information, simulation comparison information, and simulation operation information are displayed sequentially in the simulation results according to a second preset order.

[0014] In one possible implementation of the first aspect, problem handling also includes: Based on the write status of the target mode, output the write status information of the target mode to the user. The write status information indicates at least one of the following statuses: The target pattern is in a pending confirmation state; The target mode is in write mode; The target mode is in the write complete state.

[0015] In one possible implementation of the first aspect, when the target mode is in a pending confirmation state, the status information includes the target mode's pending application settings and a confirmation control for the user to confirm whether to execute the target mode. When the target pattern is in a write state, the status information includes the execution progress of the target pattern; When the target mode is in the write complete state, the status information includes a success flag indicating that the write to the target mode has been completed or a failure flag indicating that the execution failed.

[0016] In one possible implementation of the first aspect, the status information also includes a jump control when the target pattern has been written. In response to the user's triggering of the jump control, the native settings page of the target object is output to the user. The native settings interface includes the parameters of the target object after executing the target mode.

[0017] Secondly, embodiments of this application provide a problem-solving apparatus, including: The acquisition module is used to respond to the user's input of a problem to be diagnosed, perform a diagnosis based on the problem, and obtain the diagnosis result; The determination module is used to determine the target pattern for resolving the problem to be diagnosed. The processing module is used to write the control instructions corresponding to the target mode into the target object associated with the diagnostic results. The control instructions are used to control the target object to execute the target mode.

[0018] Thirdly, embodiments of this application provide a client, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the problem-handling method as described in any of the first aspects.

[0019] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements a problem-solving method as described in any of the first aspects.

[0020] Fifthly, embodiments of this application provide a computer program product that, when run on a client, causes the client to execute any of the problem-solving methods described in the first aspect above.

[0021] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0022] In this embodiment, in response to a user-inputted problem to be diagnosed, the system automatically performs a diagnosis and obtains the diagnosis results. Based on the diagnosis results, it determines the target mode for solving the problem and sends a control command to the target object to execute the mode. Through this method, the system can complete the entire closed-loop process from problem diagnosis to mode execution in a single interaction, solving the problem of existing operation and maintenance processing links being scattered across multiple pages and unable to provide unified and comprehensive diagnosis, significantly improving the accuracy of problem location and processing efficiency. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application; Figure 2 This is a flowchart illustrating a problem-solving method provided in an embodiment of this application; Figure 3 This is a flowchart illustrating a problem-solving method provided in another embodiment of this application; Figure 4 This is a schematic diagram of the system architecture provided in one embodiment of this application; Figure 5 This is an interactive schematic diagram of a problem-solving method provided in an embodiment of this application; Figure 6 This is a structural block diagram of a problem-solving apparatus provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0026] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0027] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0028] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0029] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0030] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0031] Figure 1 This is a schematic diagram illustrating an application scenario provided by an embodiment of this application. (Reference) Figure 1 This scenario includes a user, a client, and at least one target object.

[0032] In one possible implementation, at least one object in the target object refers to a physical device or virtual object that is being maintained or monitored. For example, in a new energy storage or photovoltaic power plant management system, the object may be a physical device such as a photovoltaic module, inverter, combiner box, battery management system (BMS), power conversion system (PCS), transformer, switch cabinet, cable, etc., or a virtual object such as a logical unit, device group, or virtual machine group.

[0033] In one possible implementation, the user can be a person who performs operations on a client (e.g., an AI assistant) through a terminal device or a person with management authority over the target object, including but not limited to owners (e.g., owners or investors of the target object), installers (e.g., technicians responsible for equipment installation, debugging, and configuration), and operation and maintenance management personnel (e.g., operation and maintenance engineers or system administrators).

[0034] In one possible implementation, the client is hosted on a terminal device. This terminal device can be a computing device such as a desktop computer, laptop, PDA, or cloud server.

[0035] In this embodiment, users can ask questions or issue commands to the AI ​​server through a client. The AI ​​server will then perform corresponding operations based on the received questions or commands and respond to the user via the client in a conversational format. For example, a user (an operations engineer) might input the question "Can the battery's minimum state of charge (SOC) be modified?" into the client. The client will then send the question to the AI ​​server, which will analyze and make a decision based on the question, and execute subsequent control operations. For instance, the AI ​​server can configure the device mode if preset conditions are met (such as the current device status allowing it, or the user having the appropriate permissions); alternatively, the server can refuse the operation if the conditions are not met and can provide the user with the reason for the refusal.

[0036] In the above scenario, the AI ​​server can complete the entire closed loop from problem diagnosis to pattern execution in a single interaction, solving the problem that the existing operation and maintenance processing links are scattered across multiple pages and cannot be uniformly and comprehensively diagnosed, thus significantly improving the accuracy of problem location and processing efficiency.

[0037] Figure 2 This is a flowchart illustrating a problem-solving method provided in one embodiment of this application. It should be understood that the execution entity of the problem-solving method in this embodiment is an AI server. Figure 2 As shown, the problem-solving method includes the following steps: S101, in response to receiving the user input of the problem to be diagnosed, perform diagnosis based on the problem to be diagnosed and obtain the diagnosis result.

[0038] In some embodiments, users can input the problem to be diagnosed through the AI ​​dialogue interface in the client. Taking the user as an installer as an example, when the installer discovers that a power station has a photovoltaic peak shaving problem, he can ask in the dialogue: "I noticed that the sunlight is very strong during the day, but it seems that some solar energy is not being utilized. How can we reduce waste?" In response to the problem to be diagnosed, the AI ​​server starts the diagnostic engine and determines the diagnostic result based on the question entered by the user.

[0039] In some possible implementations, the AI ​​server can perform diagnosis through pre-defined rules or AI model analysis to obtain diagnostic results.

[0040] Optionally, the AI ​​server can pre-build a rule base, where each rule defines the mapping relationship between questions, diagnostic data, and diagnostic methods. When a received question to be diagnosed matches the trigger condition of a rule, the AI ​​server determines the diagnostic data and diagnostic method corresponding to that rule, analyzes the diagnostic data according to the determined diagnostic method, and then obtains the diagnostic result. Alternatively, the AI ​​server can use a trained machine learning model or large language model, taking the user's question as input, and having the model perform semantic understanding, intent recognition, and reasoning decision-making to output the corresponding diagnostic result.

[0041] For example, a user asks, "I notice that the sunlight is very strong during the day, but it seems that some solar energy is not being utilized. How can I reduce waste?" In response to this question, the AI ​​server retrieves system data for the target object, performs photovoltaic (PV) peak shaving checks and energy loss analysis, and ultimately obtains diagnostic results. In this example, the diagnostic results may include peak shaving data, energy loss data, etc.

[0042] S102, Determine the target pattern for resolving the problem to be diagnosed.

[0043] In some embodiments, after obtaining the diagnostic results, the AI ​​server further determines a target mode for resolving the problem to be diagnosed based on the diagnostic results. The target mode may be a device operating mode, parameter configuration strategy, or charge / discharge scheduling strategy, etc.

[0044] Optionally, the AI ​​server can pre-build a pattern mapping rule base, where each rule defines the correspondence between diagnostic result features and target patterns. When a diagnostic result meets the triggering condition of a rule, the AI ​​server determines that the pattern corresponding to that rule is the target pattern. For example, when the diagnostic result indicates "PV peak reduction and SOC full during midday," the target pattern corresponding to the matching rule is "manual discharge mode." Alternatively, the AI ​​server can use a trained machine learning model or large language model, taking the diagnostic result as input, allowing the model to analyze and reason, and outputting a target pattern to solve the problem. This approach is suitable for scenarios with complex diagnostic results and multiple potential solution paths.

[0045] For example, following the example in S101, the diagnostic result obtained by the AI ​​server is "PV peak reduction caused by the battery SOC being full during the midday period, resulting in a loss of approximately 2.84 kWh of electricity." Based on this diagnostic result, the AI ​​server determines the target mode as "manual discharge mode," which can release battery capacity in advance during the midday peak period in order to absorb the photovoltaic power that would otherwise be wasted.

[0046] S103, write the control command corresponding to the target mode into the target object associated with the diagnostic results.

[0047] Among them, control instructions are used to control the target object to execute the target mode.

[0048] In some embodiments, after determining a target mode, the AI ​​server generates a control command corresponding to that target mode and sends the control command to the target object associated with the target mode to control the target object to execute the target mode. The target object can be a physical device such as a photovoltaic inverter, a power storage converter (PCS), or a battery management system (BMS), or it can be a group of devices or a logical unit. The specific format and protocol of the control command depend on the type of target object and the communication method; for example, it can be writing register values ​​or issuing remote adjustment commands.

[0049] In some possible implementations, the AI ​​server may perform permission and condition checks before sending control commands. Permission checks confirm whether the current user has the authority to perform the operation; condition checks confirm whether the current state of the target object allows the execution of the mode (e.g., whether the device is online, whether the signal is writable, etc.). Control commands are sent only if the checks pass; otherwise, execution is rejected and a reason for rejection is output.

[0050] In some possible implementations, the AI ​​server can also output confirmation information to the user, allowing the user to confirm whether to execute the target mode. The confirmation information can be in text form within the dialogue context or presented as an operable object (such as a control card or confirmation button). After user confirmation, the AI ​​server then sends a control command. For example, continuing from the example in S102, the AI ​​server determines the target mode to be "manual discharge mode." The AI ​​server first generates the corresponding control command (such as changing the operating mode register value from "maximum self-generation and self-use" to "manual discharge") and sends this command to the target object (such as the energy storage converter) to control it to execute the manual discharge mode. After writing, the AI ​​server can also provide feedback to the user on the execution result, such as "Switched to manual discharge mode, current discharge power 3kW, SOC 78%."

[0051] In this embodiment, by integrating diagnosis, pattern determination, and control execution into a single AI dialogue interface, users can complete the entire process from problem diagnosis to processing execution without repeatedly switching between multiple independent pages, significantly improving operational efficiency and ease of use. Furthermore, it can automatically diagnose based on user-inputted problems and determine the target pattern for resolving the issue based on the diagnostic results, thereby achieving precise problem localization and targeted processing, improving diagnostic accuracy and processing efficiency. After determining the target pattern, it sends control commands to the target object to execute that pattern, solving the problem of existing operational processing links being scattered across multiple pages and unable to provide unified and comprehensive diagnosis.

[0052] Figure 3 This is a flowchart illustrating a problem-solving method provided in another embodiment of this application. It should be understood that the execution entity of the problem-solving method in this embodiment is a server. Figure 3 As shown, the problem-solving method includes the following steps: S201, in response to receiving a user input of a problem to be diagnosed, perform a diagnosis based on the problem to be diagnosed and obtain a diagnosis result.

[0053] In some embodiments, diagnosing a problem and obtaining diagnostic results includes the following steps S1 to S3: S1, in response to receiving user input about the problem to be diagnosed, obtains the runtime data of the target object.

[0054] The operational data includes, but is not limited to: actual photovoltaic output power, theoretical available photovoltaic power (calculated based on irradiance and module nominal capacity), battery SOC, state of charge / discharge, and grid-connected power. This data can be sourced from cloud data interfaces or local acquisition devices.

[0055] In some embodiments, different problems correspond to different operational data, and the AI ​​server can dynamically determine the required data dimensions based on the characteristics of the problem. Specifically, in response to the user's input of a problem to be diagnosed, the AI ​​server can obtain operational data of the target object associated with the problem, based on the type and intent of the problem.

[0056] For example, when a user's question concerns photovoltaic peak shaving or energy waste, such as "Some solar energy is not being utilized; how can we reduce waste?", the AI ​​server will obtain data such as actual photovoltaic output power, theoretically available photovoltaic power, battery state of charge, and peak shaving periods. When a user's question concerns electricity costs or revenue, such as "Why is my electricity bill so high this month?", the AI ​​server will obtain data such as photovoltaic power generation, grid-purchased electricity, load electricity consumption, time-of-use pricing, and battery state of charge. When a user's question concerns equipment temperature or heat dissipation, such as "Is the inverter temperature too high?", the AI ​​server will obtain data such as equipment temperature, ambient temperature, cooling fan status, and load power. When a user's question concerns alarms or faults, such as "Why is PCS #1 constantly alarming?", the AI ​​server will obtain data such as alarm code, alarm level, equipment status, and fault occurrence time. When a user's question concerns battery health, such as "How long can the battery last?", the AI ​​server will obtain data such as battery health status, cycle count, individual cell voltage range, and historical charge / discharge records. When user questions involve communication or online status, such as "Why is my device offline?", the AI ​​server will obtain data such as the device's online status, last communication time, signal strength, and network status.

[0057] The determination of the aforementioned data dimensions can be achieved through a pre-set rule base, i.e., by pre-building a mapping relationship between question types and data dimensions. When a user's question matches a certain type of question, the system automatically determines the data to be acquired. Alternatively, it can be achieved through an AI model, i.e., using a large language model to perform intent recognition and entity extraction on the user's question, dynamically inferring the data dimensions required for the question and generating a corresponding data acquisition strategy. Through these methods, the system can acquire data on demand for different questions, avoiding unnecessary data retrieval and improving diagnostic efficiency and data utilization.

[0058] S2, based on operational data, obtains diagnostic reference data.

[0059] In some embodiments, the AI ​​server further extracts or calculates diagnostic reference data based on the acquired operational data. For example, when a user's question involves photovoltaic peak shaving or energy waste, the diagnostic reference data includes, but is not limited to, at least one of the target object's peak shaving data and energy loss data. Peak shaving data describes the phenomenon where the actual output power of a photovoltaic power generation system cannot reach the theoretically available power, and may specifically include peak shaving periods, peak shaving duration, peak shaving power, etc. Energy loss data describes the energy loss caused by peak shaving or other anomalies, and may specifically include lost electricity, loss percentage, and economic loss estimates, etc.

[0060] It should be noted that different types of problems may require different diagnostic reference data. For example, for peak shaving-related problems, the diagnostic reference data mainly includes peak shaving data and energy loss data; for temperature anomaly problems, the diagnostic reference data may include temperature change trends, temperature rise rates, etc.; for alarm problems, the diagnostic reference data may include alarm frequency, related events, etc. The system can dynamically determine the dimensions of the diagnostic reference data to be acquired based on the problem type.

[0061] S3, based on the diagnostic reference data, analyzes to obtain the diagnostic results.

[0062] In some embodiments, the AI ​​server performs comprehensive analysis based on diagnostic reference data to identify the root cause of the problem and generate diagnostic results. The diagnostic results include the specific manifestations of the problem, quantitative data, and root causes. For example, taking the photovoltaic peak shaving problem as an example, the peak shaving data obtained by the AI ​​server includes the peak shaving period being from 12:15 to 13:30, the peak shaving power being 2.46kW, and the energy loss data including the lost electricity being 2.84kWh. Based on the above diagnostic reference data, the AI ​​server analyzes and concludes the diagnostic result: the photovoltaic peak shaving during the midday period is due to the battery being fully charged, resulting in a total loss of approximately 2.84kWh of electricity.

[0063] S202 outputs diagnostic results to the user.

[0064] In the above example, the diagnostic results include peak shaving estimation information for the target object and at least one item from the peak shaving line graph of the target object. Specifically, the peak shaving estimation information includes at least one of the following: peak shaving time information, power consumption information, and peak shaving power information for the target object; the peak shaving line graph includes at least one of the following: power output curve, available power curve, difference information between the power output curve and the available power curve, and cause of the problem.

[0065] In some embodiments, diagnostic results can be output to the user in the form of diagnostic cards. For example, diagnostic results can be displayed in a progressively revealing manner through a peak shaving estimation card, showing peak shaving periods, wasted electricity, and peak peak shaving power; or displayed through a peak shaving line graph card, showing a comparison between the actual photovoltaic output curve and the theoretical available power curve, and marking the peak shaving sections, with a streaming text explanation of the analysis process (i.e., the cause of the problem) below the graph.

[0066] In some embodiments, diagnostic results can be output to the user in text form. For example, the diagnostic results can be directly output as natural language paragraphs, such as "Diagnosis shows that the photovoltaic peak shaving occurred during the midday period due to the battery's SOC being full, resulting in a total power loss of approximately 2.84 kWh," thereby improving efficiency. Alternatively, the diagnostic results can be output dynamically in a word-by-word or sentence-by-sentence manner, thereby enhancing user attention and ensuring that the information is quickly understood by the user. Or, the diagnostic results can be extracted into a key point summary, such as "Peak shaving period: 12:15-13:30 | Power loss: 2.84 kWh | Reason: SOC was full," thereby helping users quickly obtain key information.

[0067] In some embodiments, diagnostic results may be output to the user in the form of tables, voice broadcasts, videos / animations, etc.

[0068] In some embodiments, in addition to line charts, the diagnostic results may also be presented in the form of bar charts, pie charts / donut charts, radar charts, etc.

[0069] It should be noted that the above output methods can be combined arbitrarily.

[0070] S203, output recommendation information to the user, the recommendation information is used to recommend at least one pattern to the user.

[0071] The recommended information includes at least one of the following: At least one title corresponding to each mode in the pattern; Reasons for the recommendation pattern; Prompt information used to guide users in selecting the execution mode; At least one selection control for each mode in the pattern, the selection control is used by the user to select the target mode; Confirmation control: The confirmation control is used by the user to confirm the user's current selection.

[0072] In some embodiments, the recommendation information may be presented to the user in the form of recommendation cards.

[0073] In some embodiments, at least one of the title, recommendation reason, prompt message, selection control, and confirmation control is displayed sequentially in the recommendation information according to a first preset order. This application embodiment does not limit the first preset order. For example, the first preset order is: First, a card title appears, such as "Recommended xx AI Mode," to inform the user that the current recommendation stage is underway and the recommendation engine being used.

[0074] Secondly, the introductory text is displayed dynamically in a word-by-word appending manner, explaining why this mode is recommended and helping users understand the basis for the recommendation.

[0075] Secondly, the question titles appear word-for-word, such as "What would you like the system to prioritize?", to guide users in making choices and clarify the core issue that needs to be decided.

[0076] Then, the options appear sequentially, with the title and description of each option dynamically rendered word by word. For example, after the title of the first option, "Manual Discharge Mode," is displayed word by word, its description, "Allows users to manually control the discharge power and time," is added word by word; then the title of the second option, "Maximize Self-Consumption Mode," is displayed word by word, and its description, "Prioritize the use of photovoltaic power generation and reduce grid power purchases," is added word by word, and so on.

[0077] Finally, once all content has finished streaming, the confirmation control (e.g., the Confirm button) appears on the screen. During the streaming animation, the confirmation control is hidden or disabled, forcing users to read the recommended content completely before making a decision, thus avoiding accidental touches during dynamic rendering.

[0078] In this embodiment of the application, through the aforementioned progressive disclosure of recommendation information output method, users can gradually understand the basis for the recommendation, read the content of each option, and ultimately make an informed choice, thereby improving the interactive experience and decision-making accuracy during the recommendation stage.

[0079] S204, Based on the user's trigger operation on the confirmation control and / or selection control, determine the mode corresponding to the trigger operation as the target mode.

[0080] In some embodiments, users interact with recommendation cards through a dialog interface. The user first interacts with a selection control to choose a mode from at least one set of modes as their preferred option; then, they trigger a confirmation action (such as clicking a "Confirm" button). In response to the user's trigger action, the AI ​​server determines the mode selected by the user as the target mode.

[0081] In some embodiments, if the recommended information contains only one mode (i.e., only one option), the user can directly trigger the confirmation control, and the system will determine the unique mode as the target mode without any additional selection operation.

[0082] In some embodiments, if the recommendation information allows users to select multiple modes simultaneously, the user can trigger operations on multiple selection controls. After selecting multiple modes, the user can trigger a confirmation control, and the system will determine the multiple modes selected by the user as the target mode set.

[0083] In some embodiments, after a user selects an option (e.g., "xx mode") and clicks the confirmation control, the client automatically writes a preset question (e.g., "OK, please apply xx Mode based on my selection") as a new user message into the dialog stream, triggering the execution of the next stage (e.g., the simulation stage).

[0084] In this way, the system can accurately identify the user's intent and determine the target mode based on the user's triggering operation on the confirmation control and / or selection control in the recommended card, thus providing a clear operation object for subsequent control execution.

[0085] S205, simulates based on the target mode corresponding to the trigger operation, and obtains simulation results.

[0086] The simulation results include at least one of the following: Description of the target pattern; Comparison of simulation results between the current operating mode and the target mode; Simulation operation information is used to indicate the changes in key parameters corresponding to the target mode.

[0087] In this embodiment, after the user selects the target mode, the simulation engine is automatically triggered to simulate and calculate the expected effect after the target mode is executed, thereby improving the accuracy of the decision.

[0088] S206 outputs simulation results to the user.

[0089] In some possible implementations, explanatory information, simulation comparison information, and simulation operation information are displayed sequentially in the simulation results according to a second preset order.

[0090] In some embodiments, after receiving a preset question generated by the user triggering the confirmation control, the client starts the simulation engine and executes a five-step process: user goal analysis, system capability assessment, simulation calculation, pattern comparison analysis, and user confirmation guidance. After the simulation is complete, the simulation results are output sequentially according to a second preset order, where the simulation results are output to the user in the form of simulation cards, images, text, or voice.

[0091] Taking the simulation result as an example, the explanatory information can be included in the introduction card (narrative view). Specifically, after the title image appears, the introductory text is displayed in a word-by-word streaming manner, explaining the specific workings of the new model. Subsequently, four numbered items appear sequentially and are filled in word by word, explaining the typical behavior of the new model. For example, the first numbered item is "Reserving battery capacity before PV peak", the second is "Prioritizing the absorption of wasted solar energy at noon", the third is "Selling excess electricity during high-price periods", and the fourth is "Converting wasted solar energy into usable energy or export revenue". Through this progressive display, word by word, users can gradually understand the operating logic and revenue sources of the new model.

[0092] Simulation result comparison information can be included in the simulation result comparison table (quantitative view) card. After the table header appears, each row is constructed in a flowing manner following the sequence of "row skeleton appears → indicator name displayed word by word → three columns of values ​​displayed word by word." The comparison table is used to display the quantitative differences between the current mode and the recommended mode in key indicators. For example, in the "Today's PV Peak Shaving" indicator row, the current mode value is 2.84kWh, and the recommended mode value is 0kWh, a difference of -2.84kWh; similarly, in the "Estimated Additional Revenue" indicator row, the current mode value is 0, and the recommended mode value is 0.23kWh, a difference of +0.23kWh. Through this dynamic construction, row by row and word by word, users can clearly see the changes in each indicator under the two modes.

[0093] Simulation operation information can be included in the simulation operation curve card (time series view). The upper half of this card is a multi-line graph of power, displaying the trends of four curves—solar power, grid power, load power, and battery power—on the same 24-hour time axis. The lower half is a dual-axis graph of price and state of charge (SOC), with the left axis showing the buying price and selling price, and the right axis showing the battery SOC, all sharing the same 24-hour time axis.

[0094] In this embodiment, three simulation cards—an introduction card, a quantitative comparison table, and a time-series curve card—are used to display simulation results from multiple dimensions, including behavioral description, quantitative differences, and time-series trends. This helps users fully evaluate the effects and benefits before actual execution, improving the accuracy and reliability of decision-making.

[0095] S207, based on the write status of the target mode, outputs the write status information of the target mode to the user.

[0096] The written status information is used to indicate at least one of the following statuses: The target pattern is in a pending confirmation state; The target mode is in write mode; The target mode is in the write complete state.

[0097] In some embodiments, after the simulation is complete, write status information can be output to the user in the form of a card. This card, through state machine transitions, displays different write states of the target mode to the user.

[0098] In some embodiments, when the target mode is in a pending confirmation state, the status information includes the settings to be applied for the target mode and a confirmation control for the user to confirm whether to execute the target mode. For example, the AI ​​server can output a "Settings to Apply" card to the user, which lists all the settings to be written to the device, such as "Operating mode → xx Mode", "Modepreference → Profit Max", "PV strategy → Prioritize the use of wasted solar energy", "Battery strategy → Allow more aggressive charge and discharge optimization", etc.

[0099] Optionally, when the target mode is in a pending state, the card displays a list of settings and two buttons: Cancel and Confirm, for the user to review. When the user clicks the Confirm button, the card enters the executing state, and the Cancel button is grayed out.

[0100] In this embodiment of the application, a three-state card flow mechanism of pending confirmation, in execution, and completed execution is used to provide a list of settings for the user to review and confirm before writing, thereby avoiding accidental operation.

[0101] Further, perform the following step S208.

[0102] S208, write the control instructions corresponding to the target mode into the target object associated with the diagnostic results. The control instructions are used to control the target object to execute the target mode.

[0103] In some embodiments, after sending control commands to the target object (i.e., the target mode is in the write state), the status information includes the execution progress of the target mode. At this time, the confirmation control is replaced with the "Executing" text with a rotation indicator, and the write executor calls the device control interface.

[0104] In some embodiments, when the write is complete (i.e., the target mode is in the write-completed state), the card enters the write-completed state and switches to a success view. The card includes a success indicator indicating that the write to the target mode has been completed or a failure indicator indicating that the execution failed. For example, the card displays a completion icon and the text "Updated successfully" or "Update failed".

[0105] In this embodiment, the cancellation control is disabled and the execution progress is displayed during the write process to prevent duplicate submissions.

[0106] S209, in response to the user's triggering operation on the jump control, outputs the native settings page of the target object to the user.

[0107] The native settings interface includes parameters for the target object after executing the target mode. Specifically, once the target mode is written, the status information also includes a jump control. Specifically, a jump control is attached to the card; after the user triggers the jump control, they can view the target object's native settings page to verify the actual effective status. For example, a jump button can be displayed at the bottom of the card, such as "View xx AI Profit MaxSettings". After the user triggers this jump button, the system executes a page jump via the in-application routing dispatcher.

[0108] In some embodiments, the route dispatcher first determines the redirection path based on the write target, such as "general-setting / energy-profile-settings". Subsequently, the route dispatcher performs a strong type conversion on the redirection parameters and executes a plant context consistency check to ensure that the redirection target matches the currently operating plant and device. After successful verification, the route dispatcher calls a page navigation method (such as named navigation (Get.toNamed)) to complete the page redirection.

[0109] After the installer is redirected to the native settings page, they can check the actual status of the device on this page. For example, they can confirm that the "Operating Mode" has been switched to "xx AI Mode", the "Mode Preference" has been set to "Profit Max", the "PV Strategy" has been applied to "Prioritize the use of wasted solar energy", and the "Battery Strategy" has been enabled to "Allow more aggressive charge and discharge optimization".

[0110] In this embodiment, a success indicator is displayed after the writing is completed, along with a jump control. Users can jump to the native settings page with one click to verify the actual effective status. Through direct verification on the native page, installers can confirm that the parameters written by AI are consistent with the actual device status, thereby enhancing the verifiability and trustworthiness of AI operation and maintenance.

[0111] The system architecture and functions of the AI ​​server in this application embodiment are shown in the table below:

[0112] Figure 4 This is a schematic diagram of the system architecture provided in one embodiment of this application. Please refer to it. Figure 4 After a user raises a question, the diagnostic engine receives real-time data, identifies the problem, quantifies its impact, and outputs a diagnostic card event. The five-segment state coordinator confirms the completion of segment 1, triggering segment 2. The recommendation engine generates alternative solutions based on the diagnostic conclusions and power plant capabilities, outputting a recommendation card event. The five-segment state coordinator confirms the completion of segment 2, triggering segment 3. The simulation engine performs simulation calculations based on historical data and recommended solutions, outputting simulation three-state card events (introduction card, quantitative comparison table, and operating curve card). The five-segment state coordinator confirms the completion of segment 3, triggering segment 4. The write executor calls the device control interface to batch write settings items after user confirmation, completing segment 4. The routing dispatcher generates jump parameters based on the write target and executes page navigation, outputting jump instructions to the native settings page, completing segment 5. The streaming event parsing layer routes SSE events to the corresponding card renderers on the client side according to event type and reports the status to the five-segment state coordinator. The ordered content block storage stores all card blocks within the dialogue and supports historical playback.

[0113] Figure 5 This is an interactive schematic diagram of a problem-solving method provided in an embodiment of this application. For example... Figure 5 As shown, this application's embodiment defines a protocol constraint that the five stages—diagnosis, recommendation, simulation, writing, and jump verification—must occur in sequence, with the triggering condition of each subsequent stage strictly dependent on the completion status of the preceding stage. Specifically, after a user raises a question, the entire process—stage 1 diagnosis, stage 2 recommendation, stage 3 simulation, stage 4 writing, and stage 5 jump verification—is completed through sequential interactions between the client, AI server, and target object. Optionally, subsequent stages will not occur if any stage is not completed, thus creating a mandatory requirement. The following is combined with... Figure 5 Explanation of each paragraph: Segment 1 (Diagnosis): The user inputs the problem to be diagnosed through the client, which then sends the problem to the AI ​​server. The AI ​​server starts the diagnostic engine, obtains the target object's operational data, performs peak shaving checks, energy loss analysis, etc., and outputs the diagnostic results. After this segment is completed, Segment 2 is triggered.

[0114] Segment 2 (Recommendation): Based on the diagnostic results, the AI ​​server determines the target pattern for solving the problem and outputs recommendation information to the user in the form of recommendation cards. The recommendation information includes a pattern title, a reason for recommendation, a prompt message, a selection control, and a confirmation control. Segment 3 is triggered after the user selects a pattern and confirms.

[0115] Segment 3 (Simulation): The AI ​​server starts the simulation engine based on the target mode selected by the user, simulates the expected effect after executing the mode, and outputs three simulation cards: an introduction card, a quantitative comparison table, and a running curve card, displaying the simulation results from three dimensions: behavioral description, quantitative difference, and time series trend. After the simulation is completed, Segment 4 is triggered.

[0116] Segment 4 (Write): The AI ​​server outputs a settings card to be applied to the user, listing all settings to be written to the device. The card transitions through a state machine: In the pending confirmation state, the list of settings and a cancel / confirm button are displayed; in the execution state, the cancel button is disabled and the execution progress is displayed; in the completed state, a success indicator and a jump button are displayed. Segment 5 is triggered after writing is complete.

[0117] Section 5 (Redirect Verification): After the user triggers the redirect button, the system executes a page redirection through the in-application route dispatcher. Based on the target data, the redirection path is determined and parameters are validated before redirecting to the target object's native settings page. The installer verifies the actual active status on the native page to confirm consistency with the AI ​​data.

[0118] In this embodiment, the five stages of diagnosis, recommendation, simulation, writing, and status feedback are sequentially connected and dependent on each other within a single AI dialogue interface, realizing a closed-loop process from problem identification to control execution. This solves the problem that existing operation and maintenance processes are scattered across multiple pages and cannot be diagnosed in a unified and comprehensive manner, significantly improving operation and maintenance efficiency, response speed, and automation.

[0119] In some embodiments, the solutions of this disclosure also include the following alternatives: Alternative Option A: A1: Change the five sections to four sections, and merge the diagnosis and recommendation into one section. This is suitable for scenarios with a single problem type that do not require separate diagnosis display.

[0120] A2: Change the five segments to three segments, in the order of diagnosis stage, simulation and write merging stage, and jump stage. Remove the recommendation stage. This is suitable for scenarios where there is only one recommended solution and the user does not need to make a choice.

[0121] A3: Allows the third segment (simulation) and the fourth segment (write) to run in parallel, meaning that the simulation card and the application settings card can appear at the same time. This is suitable for scenarios where users are already familiar with the recommended solution and do not need to confirm step by step.

[0122] Alternative Option B (Alternative Recommendation): B1: The recommendation card has been changed to a multi-select mode instead of a single selection mode, which is suitable for scenarios where multiple strategies need to be enabled at the same time and users want to combine multiple modes.

[0123] B2: Remove the gradual disclosure mechanism and display all options at once. This is suitable for scenarios with a small number of options (two or less) where users do not need to read them step by step.

[0124] B3: When the user clicks the confirmation control, no preset question is injected. Instead, the server automatically triggers the simulation phase, which can reduce one round of dialogue and improve interaction efficiency.

[0125] Alternative Solution C (Alternative Simulation Information): C1: The three-view diagram in the simulation information is changed to a two-view diagram. The introduction card is removed, and only the quantitative comparison table and the running curve card are retained. This is suitable for scenarios where the installer already understands the meaning of the mode and does not need behavioral explanation.

[0126] C2: The quantitative comparison table is changed to a radar chart or bar chart, which is suitable for scenarios with more than four comparison dimensions and where a more intuitive display of multi-dimensional comparison results is needed.

[0127] C3: The operating curve card is changed to a single-axis display, which only displays the power curve and does not display the price curve and state of charge curve. It is suitable for markets without time-of-use pricing mechanisms or scenarios where it is not necessary to display price correlations.

[0128] Alternative Solution D (Alternative to writing status information): D1: The three-state system is changed to a two-state system. The execution state is removed, and only the pending confirmation state and the execution completed state are retained. This is suitable for scenarios with extremely low write latency and no need to display intermediate progress.

[0129] D2: The completed state does not redirect to the native settings page, but directly displays a snapshot of the device status after writing within the card. This is suitable for scenarios where users do not need in-depth verification but only need quick confirmation.

[0130] D3: Add a fourth state, namely the failure state. The state transitions to the pending confirmation state → in progress state → failure state. When a failure occurs, the reason for the failure is displayed and a retry button is provided. This is suitable for scenarios where the network is unstable or there is a high risk of failure in write operations.

[0131] Alternative Option E: E1: The five-stage closed loop is changed from a dialogue flow mode to a wizard-style multi-step form mode. Users complete the diagnostic results viewing, recommendation selection, simulation confirmation, write execution, and jump operation step by step. This solution loses the continuity of the dialogue context, but it is simpler to implement and the process is more linear.

[0132] E2: The five-stage process is changed to a notification-based execution mode. The AI ​​backend automatically completes the three stages of diagnosis, recommendation, and simulation. The user is unaware of the intermediate processes, and only pushes an application settings card for user confirmation in the final stage. After confirmation, the settings are written and redirected. This solution is suitable for scenarios with high automation, high user trust, and no need for human intervention in intermediate decision-making.

[0133] Corresponding to the problem-solving method in the above embodiments, Figure 6 This is a structural block diagram of a problem-solving apparatus provided in one embodiment of this application. For ease of explanation, only the parts related to the embodiment of this application are shown. (Refer to...) Figure 6 The device includes: The acquisition module 61 is used to respond to the user input of the problem to be diagnosed, perform diagnosis based on the problem to be diagnosed, and obtain the diagnosis result; Module 62 is used to determine the target pattern for resolving the problem to be diagnosed. The processing module 63 is used to write the control instructions corresponding to the target mode into the target object associated with the diagnostic results. The control instructions are used to control the target object to execute the target mode.

[0134] In one possible implementation, module 61 is used for: Obtain the runtime data of the target object; Based on operational data, obtain diagnostic reference data; Diagnostic results are obtained by analyzing diagnostic reference data.

[0135] In one possible implementation, the processing module 63 is also used to: output diagnostic results to the user.

[0136] In one possible implementation, the diagnostic reference data includes at least one of the following: peak-shaving data of the target object and energy loss data of the target object; The diagnostic results include peak shaving estimation information of the target object and at least one item from the peak shaving line graph of the target object; wherein, the peak shaving estimation information includes at least one item from the peak shaving time information, power consumption information, and peak shaving power information of the target object; the peak shaving line graph includes at least one item from the power output curve, available power curve, difference information between the power output curve and the available power curve, and cause of the problem.

[0137] In one possible implementation, the processing module 63 is further configured to: output recommendation information to the user, the recommendation information being used to recommend at least one pattern to the user; The determining module 62 is used to: determine the target pattern based on the user's triggering operation on the recommendation information; wherein the recommendation information includes at least one of the following: At least one title corresponding to each mode in the pattern; Reasons for the recommendation pattern; Prompt information used to guide users in selecting the execution mode; At least one selection control for each mode in the pattern, the selection control is used by the user to select the target mode; Confirmation control: The confirmation control is used by the user to confirm the user's current selection.

[0138] In one possible implementation, at least one of the following elements—title, reason for recommendation, prompt message, selection control, and confirmation control—is displayed sequentially in the recommendation information according to a first preset order.

[0139] In one possible implementation, the processing module 63 is used to: in response to a user's triggering operation on the selection control and / or confirmation control, determine that the mode corresponding to the triggering operation is the target mode; The processing module 63 is also used to: perform simulation based on the target pattern and obtain simulation results.

[0140] In one possible implementation, the processing module 63 is further configured to: output simulation results to the user, the simulation results including at least one of the following: Description of the target pattern; Comparison of simulation results between the current operating mode and the target mode; Simulation operation information is used to indicate the changes in key parameters corresponding to the target mode.

[0141] In one possible implementation, explanatory information, simulation comparison information, and simulation operation information are displayed sequentially in the simulation results according to a second preset order.

[0142] In one possible implementation, the processing module 63 is further configured to: output write status information of the target mode to the user based on the write status of the target mode, wherein the write status information indicates at least one of the following states: The target pattern is in a pending confirmation state; The target mode is in write mode; The target mode is in the write complete state.

[0143] In one possible implementation, when the target mode is in a pending confirmation state, the status information includes the target mode's pending application settings and a confirmation control for the user to confirm whether to execute the target mode. When the target pattern is in a write state, the status information includes the execution progress of the target pattern; When the target mode is in the write complete state, the status information includes a success flag indicating that the write to the target mode has been completed or a failure flag indicating that the execution failed.

[0144] In one possible implementation, the status information also includes a jump control when the target pattern has been written. The processing module 63 is also used to: respond to the user's trigger operation on the jump control, output the native settings page of the target object to the user, the native settings interface including the parameters of the target object after executing the target mode.

[0145] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 7 As shown, the electronic device 7 in this embodiment is, for example, an AI server. The electronic device includes: at least one processor 70 ( Figure 7 (Only one is shown in the image) a processor, a memory 71, and a computer program 72 stored in the memory 71 and executable on at least one processor 70, wherein the processor 70 executes the computer program 72 to implement the steps in any of the above-described problem-solving method embodiments.

[0146] Electronic device 7 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This electronic device may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will understand that... Figure 7 This is merely an example of electronic device 7 and does not constitute a limitation on electronic device 7. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0147] The processor 70 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0148] In some embodiments, memory 71 may be an internal storage unit of electronic device 7, such as a hard disk or memory of electronic device 7. In other embodiments, memory 71 may be an external storage device of electronic device 7, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on electronic device 7. Furthermore, memory 71 may include both internal and external storage units of electronic device 7. Memory 71 is used to store operating system, electronic device, bootloader, data, and other programs, such as program code of computer programs. Memory 71 may also be used to temporarily store data that has been output or will be output.

[0149] If the integrated unit is implemented as 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, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can at least include: any entity or device capable of carrying computer program code to a device / control device, a recording medium, a computer memory, a read-only memory (ROM), etc. Only memory, random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical discs.

[0150] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0151] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0152] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0153] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps described in the various method embodiments above.

[0154] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0155] If the integrated unit is implemented as 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, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographic device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, a computer-readable medium cannot be an electrical carrier signal or a telecommunication signal.

[0156] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0157] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0158] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0159] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0160] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A problem-solving method, characterized in that, The method includes: In response to receiving a question to be diagnosed from user input, a diagnosis is performed based on the question to be diagnosed and a diagnosis result is obtained; Determine the target pattern for resolving the problem to be diagnosed; The control instructions corresponding to the target mode are written into the target object associated with the diagnostic result. The control instructions are used to control the target object to execute the target mode.

2. The method according to claim 1, characterized in that, The process of diagnosing the problem to be diagnosed and obtaining the diagnostic result includes: Obtain the runtime data of the target object; Based on the aforementioned operational data, diagnostic reference data is obtained; The diagnostic results are obtained by analyzing the diagnostic reference data.

3. The method according to claim 1, characterized in that, Also includes: The diagnostic results are output to the user.

4. The method according to claim 2, characterized in that, The diagnostic reference data includes at least one of the following: peak shaving data of the target object, and energy loss data of the target object; The diagnostic results include peak reduction estimation information of the target object and at least one of the peak reduction line graphs of the target object; The peak shaving estimation information includes at least one of the following: peak shaving time information, power consumption information, and peak shaving power information of the target object; the peak shaving line graph includes at least one of the following: power output curve, available power curve, difference information between the power output curve and the available power curve, and cause of the problem.

5. The method according to claim 1, characterized in that, The determination of the target pattern for resolving the problem to be diagnosed includes: The system outputs recommendation information to the user, the recommendation information being used to recommend at least one pattern to the user; The target pattern is determined based on the user's triggering action on the recommendation information; wherein the recommendation information includes at least one of the following: The title corresponding to each mode in at least one mode; Reasons for recommending each mode; Prompt information used to prompt the user to select the execution mode; Each mode has a corresponding selection control, which is used by the user to select the target mode from the at least one mode; A confirmation control is used by the user to confirm the user's current selection.

6. The method according to claim 5, characterized in that, At least one of the title, the reason for recommendation, the prompt information, the selection control, and the confirmation control is displayed in the recommendation information in a first preset order.

7. The method according to claim 5, characterized in that, The step of determining the target pattern based on the user's triggering operation on the recommendation information includes: In response to the user's triggering operation on the selection control and / or confirmation control in the recommendation information, the mode corresponding to the triggering operation is determined to be the target mode; The method further includes: performing simulation based on the target mode and obtaining simulation results.

8. The method according to claim 7, characterized in that, Also includes: The simulation results are output to the user, and the simulation results include at least one of the following: Description of the target pattern; Comparison of simulation results between the current operating mode and the target mode; Simulation operation information, which is used to indicate the changes in key parameters corresponding to the target mode.

9. The method according to claim 8, characterized in that, The explanatory information, the simulation comparison information, and the simulation operation information are displayed sequentially in the simulation results according to a second preset order.

10. The method according to any one of claims 1 to 9, characterized in that, Also includes: Based on the write status of the target mode, the write status information of the target mode is output to the user, the write status information indicating at least one of the following states: The target pattern is in a pending confirmation state; The target pattern is in a write state; The target mode is in the write complete state.

11. The method according to claim 10, characterized in that, When the target mode is in the pending confirmation state, the status information includes the pending application settings of the target mode and a confirmation control for the user to confirm whether to execute the target mode; When the target mode is in the write state, the status information includes the execution progress of the target mode; When the target mode is in the write-complete state, the status information includes a success flag indicating that the write to the target mode has been completed or a failure flag indicating that the execution failed.

12. The method according to claim 10, characterized in that, When the target mode writing is completed, the writing status information also includes a jump control; the method further includes: In response to the user's triggering operation on the jump control, the native settings page of the target object is output to the user, and the native settings interface includes the parameters of the target object after executing the target mode.

13. A problem-solving device, characterized in that, include: The acquisition module is used to respond to receiving a user input question to be diagnosed, perform a diagnosis based on the question to be diagnosed, and acquire the diagnosis result; A determination module is used to determine a target pattern for resolving the problem to be diagnosed. The processing module is used to write the control instructions corresponding to the target mode into the target object associated with the diagnostic result, and the control instructions are used to control the target object to execute the target mode.

14. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the method as claimed in any one of claims 1 to 12.

15. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 12.

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