A method and system for managing users of an ups power supply

Abstract

The application relates to the technical field of UPS power user management, in particular to a UPS power user management method and system, which comprises the following steps: obtaining operation behavior information of an uninterrupted power supply system of an operation and maintenance personnel; according to the operation behavior information, analyzing the operation behavior to obtain an evaluation of the operation ability of the operation and maintenance personnel; according to the evaluation of the operation ability of the operation and maintenance personnel, adjusting the information display mode of the operation and maintenance personnel, adjusting the detailed degree of the operation guidance of the operation and maintenance personnel, and providing the operation and maintenance personnel with ability improvement resources. The above method improves the operation and maintenance efficiency and accuracy.

Description

Technical Field

[0001] This invention relates to the technical field of UPS power user management, and specifically to a UPS power user management method and system. Background Technology

[0002] In critical locations such as modern industries and data centers, uninterruptible power supply (UPS) systems play a vital role, providing a continuous and stable power supply to servers, storage devices, and network equipment. To ensure the normal operation of these critical devices, maintenance personnel need to routinely monitor, configure, and maintain multiple UPS systems.

[0003] Existing user management features allow different operations and maintenance personnel to have different operating permissions; for example, administrators can modify configurations, while ordinary operators can only view status. However, as the number of deployed UPS systems continues to increase, the amount of monitoring data presented by the centralized management platform also grows significantly. This brings a practical problem: for operations and maintenance personnel, especially inexperienced novice operators, it is difficult to quickly sift through massive amounts of real-time data and historical records to identify important information directly related to their current tasks or responsibilities. Although the platform provides role-based access control, it does not personalize the information presentation according to the actual needs and concerns of different users. This results in a large amount of non-core data interfering with operators' identification of core information, easily causing information overload, and potentially even causing them to overlook critical early warning information. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned shortcomings by proposing a UPS power supply user management method and system.

[0005] The present invention adopts the following technical solution:

[0006] A UPS power user management method, the method comprising the following steps:

[0007] To obtain information on the operational behavior of maintenance personnel on the uninterruptible power supply system;

[0008] Based on operational behavior information, analyze the operational behavior to obtain an assessment of the operational capabilities of maintenance personnel;

[0009] Based on the assessment of the operational capabilities of operations and maintenance personnel, we will adjust the way information is displayed to them, adjust the level of detail in their operational instructions, and provide resources to enhance their capabilities.

[0010] Through this technical solution, this application can dynamically assess the operational capabilities of maintenance personnel based on their actual operational behavior, and adjust information display, operation guidance, and capability enhancement resources accordingly. This effectively solves the problems of information overload, insufficient guidance, and rigid management models in existing technologies, and significantly improves operational efficiency and accuracy.

[0011] This application also discloses a UPS power user management system applied to the above-mentioned UPS power user management method, the system comprising:

[0012] The acquisition module acquires information about the operational behavior of maintenance personnel on the uninterruptible power supply system.

[0013] The analysis module analyzes operational behavior information to obtain an assessment of the operational capabilities of maintenance personnel.

[0014] The adjustment module adjusts the way information is displayed to operations and maintenance personnel, the level of detail in the operation instructions, and provides resources to enhance their capabilities, based on an assessment of their operational skills.

[0015] This application provides a system that can implement the above-mentioned method through this technical solution. The system, through its modular design, can efficiently acquire and analyze the operational behavior of maintenance personnel and make personalized adjustments based on the evaluation results, thereby providing maintenance personnel with more intelligent and efficient management services and improving the overall operation and maintenance level of the UPS power system.

[0016] This application introduces a dynamic evaluation mechanism for the operational capabilities of maintenance personnel, enabling personalized and intelligent adjustments to information display, operational guidance, and capability enhancement resources. This significantly improves the operational efficiency, accuracy, and safety of UPS power systems, overcoming the limitations of information overload, insufficient guidance, and rigid management in existing technologies. It has significant progressive and practical value.

[0017] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0018] Figure 1 This is a flowchart of a UPS power supply user management method according to the present invention;

[0019] Figure 2 This is a schematic diagram of the structure of a UPS power supply user management system according to the present invention. Detailed Implementation

[0020] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0021] This embodiment provides a UPS power user management method and system, combined with Figure 1 and Figure 2 As shown.

[0022] refer to Figure 1 A UPS power supply user management method, the method comprising the following steps:

[0023] To obtain information on the operational behavior of maintenance personnel on the uninterruptible power supply system;

[0024] Based on operational behavior information, analyze the operational behavior to obtain an assessment of the operational capabilities of maintenance personnel;

[0025] Based on the assessment of the operational capabilities of operations and maintenance personnel, we will adjust the way information is displayed to them, adjust the level of detail in their operational instructions, and provide resources to enhance their capabilities.

[0026] In critical environments such as modern industries and data centers, uninterruptible power supply (UPS) systems play a vital role, providing a continuous and stable power supply to servers, storage devices, and network equipment. To ensure the normal operation of these critical devices, maintenance personnel need to routinely monitor, configure, and maintain multiple UPS systems. However, traditional UPS user management methods, when faced with increasing system scale, complex and varied fault scenarios, and differences in the experience levels of maintenance personnel, suffer from difficulties in quickly filtering out important information directly related to current tasks or responsibilities. This can easily lead to information overload and even cause the overlooking of critical warning information. Furthermore, inexperienced operators may struggle to transform scattered data into actionable suggestions, thus prolonging fault diagnosis time and potentially leading to incorrect judgments, resulting in response delays or operational errors. Simultaneously, existing user management methods are often fixed and cannot dynamically adapt to the operational performance of maintenance personnel and the current state of the system. They fail to automatically adjust the detail of information pushes, the loudness of alarms, or the intensity of prompts on the user interface based on the severity of risks and the operator's historical response efficiency, to ensure that the most appropriate information reaches the users who need it most efficiently.

[0027] The "uninterruptible power supply system" mentioned in this application generally refers to a UPS system, whose core function is to provide uninterrupted power to load equipment in the event of a mains power outage or abnormality, ensuring the continuity of critical business operations. This system typically consists of a rectifier, inverter, battery pack, static switch, etc., and is capable of AC / DC conversion, energy storage, and rapid switching of power supply paths. In practical applications, uninterruptible power supply systems are widely used in scenarios with extremely high requirements for power continuity, such as data centers, communication base stations, industrial control systems, and medical equipment.

[0028] "Operations and maintenance personnel" refers to professionals responsible for the daily monitoring, maintenance, fault diagnosis and handling, and configuration management of uninterruptible power supply (UPS) systems. Their operational behavior directly affects the stable operation and fault recovery efficiency of the UPS system.

[0029] "Operational behavior information" refers to all recordable data generated by maintenance personnel during their interaction with the uninterruptible power supply (UPS) system, including but not limited to login / logout records, parameter queries, configuration modifications, alarm confirmations, fault diagnosis steps, execution of operation commands, and data input. This information forms the basis for evaluating the operational capabilities of maintenance personnel.

[0030] "Operational capability assessment" is a comprehensive evaluation of the proficiency, accuracy, efficiency, and problem-solving abilities of operations and maintenance personnel in performing uninterruptible power supply (UPS) related operations under specific circumstances. The assessment results can reflect the personnel's experience level, professional knowledge, and ability to handle complex situations.

[0031] "Information display method" refers to the interface layout, content filtering, visualization format, and priority sorting of the uninterruptible power supply (UPS) management system when presenting data, alarms, status, and other information to maintenance personnel. For example, the level of detail of the displayed data, chart type, alarm color, or flashing frequency can be adjusted.

[0032] "The level of detail in the operation instructions" refers to the granularity of the guidance information provided by the system when operations and maintenance personnel perform operations. For example, for experienced operations and maintenance personnel, the instructions may only provide key steps; while for less experienced operations and maintenance personnel, the instructions may include detailed text and image descriptions, operation videos, or step-by-step instructions.

[0033] "Capacity Building Resources" refers to learning materials, training courses, simulation exercises, expert experience sharing, etc., recommended by the system based on the operational capability assessment results of maintenance personnel, which can help improve their professional skills and knowledge.

[0034] Specifically, various methods can be used to acquire information about the operational behavior of maintenance personnel on the uninterruptible power supply (UPS) system. For example, the system can record in real time every click, input, and command execution by maintenance personnel on the management interface, along with the corresponding system feedback. This includes, but is not limited to, what parameters maintenance personnel viewed, what configurations they modified, what diagnostic commands they executed, what alarm information they confirmed after logging into the system, and the timestamps of these operations. As one implementation method, the system can deploy a behavior logging module that captures and stores all event data related to interactions with maintenance personnel. For example, when maintenance personnel select to view the voltage curve of a battery pack on the monitoring interface, the system will record the action "User A viewed the voltage curve of battery pack B at time T." When maintenance personnel attempt to modify an output voltage parameter, the system will record the action "User A attempted to modify the output voltage parameter at time T, inputting a value of X." This raw operational behavior information forms the basis for subsequent analysis and evaluation.

[0035] In assessing the operational capabilities of maintenance personnel by analyzing operational behavior information, the system requires in-depth processing and analysis of the collected raw operational behavior information. For example, the system can analyze the diagnostic path, operational efficiency, error rate, and response speed to system alarms when handling faults. As one implementation method, the system can preset a series of evaluation indicators, such as "fault diagnosis time," "operational accuracy," and "key information identification ability." The system quantifies the operational capabilities of maintenance personnel by comparing their actual operational behavior with these preset indicators. For example, if maintenance personnel can quickly locate the problematic battery pack and perform the correct charge-discharge test when facing a typical battery fault, their "fault diagnosis time" and "operational accuracy" scores will be high. Conversely, if maintenance personnel frequently attempt incorrect diagnostic steps or fail to identify key alarms for a long time when handling complex faults, their evaluation score will decrease accordingly.

[0036] Regarding adjusting the information display method for maintenance personnel based on their operational capabilities, the system can personalize the information content and presentation format they see according to the assessment results. For example, for less experienced maintenance personnel, the system can default to displaying less and more concise core data, highlighting key alarm information, and hiding some advanced or infrequently used configuration options to avoid information overload. As an implementation method, the system can set different information display templates for maintenance personnel of different skill levels. For example, for "junior maintenance personnel," the system interface may only display the basic operating status of the UPS system (such as input / output voltage, current, and battery level), and prominently display any existing alarms. For "advanced maintenance personnel," the system can provide more detailed operating parameters, historical trend graphs, advanced configuration options, and access to diagnostic tools.

[0037] Regarding adjusting the level of detail in operational guidance for maintenance personnel, the system can dynamically adjust the level of detail provided when performing specific operations or handling faults based on the operational capability assessment results of the maintenance personnel. For example, for experienced maintenance personnel, the system may only provide brief operation prompts or key steps; while for less experienced maintenance personnel, the system can provide detailed step-by-step instructions, illustrated explanations, and even operation videos or simulations. As one implementation method, the system can maintain an operational guidance library containing guidance at various levels of detail for different operation and fault scenarios. When maintenance personnel need to perform an operation, the system will select the guidance version most suitable for their current skill level from the guidance library based on their current operational capability assessment results. For example, when replacing a battery pack, for experienced maintenance personnel, the system may only prompt "Please replace the battery pack according to the standard procedure"; while for less experienced maintenance personnel, the system will display detailed steps such as "Step 1: Disconnect the mains power input; Step 2: Turn off the UPS output; Step 3: Wear insulating gloves; Step 4: Disconnect the battery connection cable..."

[0038] In providing resources to enhance the capabilities of operations and maintenance (O&M) personnel, the system can recommend personalized learning and training resources based on their operational competence assessment results to help them address knowledge and skill deficiencies. For example, if the assessment results show that an O&M personnel has weaknesses in handling battery faults, the system can automatically recommend relevant battery maintenance courses, fault diagnosis manuals, or expert experience sharing. As one implementation method, the system can establish a capability enhancement resource library, containing various training materials, technical documents, online courses, simulators, etc. Based on the O&M personnel's assessment report, the system identifies their weaknesses and matches the most relevant resources from the library for delivery. For example, if an O&M personnel scores low in "harmonic analysis," the system can recommend a "UPS system harmonic mitigation course" or an "advanced oscilloscope usage tutorial."

[0039] The UPS power supply user management method of this application acquires operational behavior information of maintenance personnel on the uninterruptible power supply system, analyzes this information to assess the operational capabilities of the maintenance personnel, and based on this assessment, the system can dynamically adjust the way information is displayed to maintenance personnel, the level of detail in operational instructions, and provide personalized capability enhancement resources.

[0040] This application further proposes the following steps for analyzing operational behavior based on operational behavior information to obtain an assessment of the operational capabilities of maintenance personnel:

[0041] Obtain the correlation characteristics of the operating parameters of the uninterruptible power supply system. The correlation characteristics represent a quantitative description of the interaction between the operating parameters.

[0042] Monitor the evolution trajectory of associated features;

[0043] Identify deviations from the evolution trajectory and trigger contextual change events;

[0044] Based on the triggered context change events, the evaluation background of the operation and maintenance personnel's operation behavior is adjusted, the current context is marked as an unknown complex disturbance, and the direct comparison between the efficiency of the operation and maintenance personnel's diagnostic path and the preset single fault template is suspended, and multiple diagnostic direction suggestions are dynamically generated.

[0045] Based on multiple diagnostic direction suggestions, a list of diagnostic direction suggestions is generated. The priority of each diagnostic direction is adjusted according to each operation and maintenance personnel's operation and maintenance personnel, and an evidence weight is assigned to each operation. Based on the adjusted priority of each diagnostic direction, the evidence weight corresponding to each operation, and the accumulated evidence of the operation and maintenance personnel's operation, a diagnostic path is constructed.

[0046] Quantify the information value generated by each operation and maintenance personnel's actions;

[0047] The evaluation weights of the performance indicators of operations and maintenance personnel are adjusted based on the diagnostic path and the information value brought by each operation. Furthermore, the performance scores of operations and maintenance personnel are dynamically adjusted based on their contribution to path optimization for each operation.

[0048] Specifically, the correlation characteristics of uninterruptible power supply (UPS) system operating parameters refer to the quantitative description of the degree and manner in which different operating parameters within the system depend on and influence each other. For example, the correlation, causality, or synchronous change trends of parameters such as voltage, current, frequency, temperature, and battery charge / discharge state under specific operating conditions can be modeled. The aim is to gain a more comprehensive and in-depth understanding of the system's operating state, rather than simply focusing on the absolute value of a single parameter. The evolution trajectory of the correlation characteristics can be understood as the dynamic process of these quantitative descriptions changing over time. By continuously monitoring these trajectories, subtle changes in the system from normal operation to abnormal states can be captured, and potential problems can even be discovered before faults manifest. In practical applications, deviations from the evolution trajectory refer to the differences between the dynamic changes of the correlation characteristics and the preset normal or known abnormal patterns. When such deviations reach a certain threshold, a situational change event is triggered, indicating that the system may have entered a new, unknown, or complex operating situation.

[0049] Based on triggered contextual change events, the evaluation context of operations and maintenance personnel's operational behavior is adjusted. Specifically, the system no longer simply compares the operations of operations and maintenance personnel with a preset single fault diagnosis process mechanically. Instead, the current context is marked as an unknown complex disturbance, which means that the system acknowledges that the current problem may not have a ready-made solution or standard diagnostic path. In this context, the system will suspend the direct comparison of the efficiency of the operations and maintenance personnel's diagnostic path with the preset single fault template, and instead dynamically generate multiple diagnostic direction suggestions to encourage operations and maintenance personnel to conduct exploratory diagnosis.

[0050] Furthermore, based on dynamically generated diagnostic direction suggestions, the system generates a list of suggested diagnostic directions. Each action taken by operations personnel, such as executing a test, checking a component, or querying a log, is assigned an evidence weight, reflecting the action's verification or exclusion effect on the current diagnostic direction. Simultaneously, the system adjusts the priority of each diagnostic direction in real time based on these evidence weights and the accumulated evidence from the operations personnel's actions, ultimately constructing a diagnostic path that reflects the operations personnel's actual thinking and actions. Quantifying the information value brought by each operation personnel's action refers to assessing the amount of new information or decision-making value contributed by each action (such as data collection, parameter adjustment, component inspection, etc.) in narrowing the scope of the fault, verifying or excluding diagnostic hypotheses, and advancing the diagnostic process. This can be quantified by analyzing the degree to which the operation results reduce uncertainty and the degree to which they change the priority of diagnostic directions.

[0051] Therefore, based on the constructed diagnostic path and the information value brought by each operation by the operations and maintenance personnel, the system will adjust the evaluation weight of the personnel's performance indicators. Specifically, in complex scenarios, exploratory operations and the ability to solve unknown problems will be given higher weight. At the same time, based on the contribution of each operation by the operations and maintenance personnel to path optimization, such as significantly shortening the diagnosis time or avoiding misjudgment through a key operation, the system will dynamically adjust the performance score of the operations and maintenance personnel to more fairly and accurately reflect their actual performance in solving complex problems.

[0052] In some preferred embodiments, suppose that during the operation of an uninterruptible power supply (UPS) system, an unprecedented pattern of correlation and fluctuation appears among the voltage, current, and temperature parameters of its battery pack. Traditional monitoring systems may only report a parameter exceeding a threshold but fail to identify the deeper problems indicated by this correlation.

[0053] The proposed solution first acquires and analyzes the correlation characteristics of these operating parameters, such as calculating the dynamic correlation coefficient between voltage and current, and the nonlinear relationship between temperature and charge / discharge efficiency. When the evolution trajectory of these correlation characteristics begins to deviate from the historical normal pattern, the system identifies this deviation and triggers a situational change event, for example, marked as "unknown abnormal correlation fluctuation of battery pack".

[0054] Based on this changing scenario, the system will adjust the evaluation context of the operations and maintenance personnel's actions, marking the current scenario as an unknown complex disturbance. At this time, the system no longer expects the operations and maintenance personnel to directly apply single fault templates such as "battery overvoltage" or "battery overtemperature" for diagnosis. Instead, it dynamically generates multiple diagnostic direction suggestions, such as: "Check the integrity of the battery management system (BMS) firmware", "Analyze the trend of battery internal impedance changes", "Investigate abnormalities of the ambient temperature sensor", etc.

[0055] When maintenance technician Xiao Li began the diagnostic process, he first selected the "Check BMS Firmware Integrity" operation. The system assigns an evidence weight to this operation and adjusts the priority of items in the diagnostic direction suggestion list based on the operation's result (e.g., firmware is normal). Subsequently, Xiao Li discovered an abnormal communication error in the BMS logs. He further performed the "Analyze Battery Internal Impedance Change Trend" operation and obtained new data. The system quantifies the information value brought by each of Xiao Li's operations. For example, the first operation ruled out a possibility, and the second operation provided new clues. These operations all contributed to narrowing down the fault scope and advancing the diagnostic process.

[0056] Based on Xiao Li's diagnostic path and the information value of each action, the system dynamically adjusts the evaluation weights of his performance indicators. In the current unknown and complex perturbation situation, exploratory actions and the ability to discover new clues are given higher weights. Ultimately, the system dynamically adjusts his performance score based on his contribution to path optimization throughout the diagnostic process (for example, through a series of exploratory actions, he ultimately located a rare software defect in the BMS, avoiding the cost of replacing the entire battery pack), to accurately reflect his outstanding ability to solve complex problems.

[0057] Specifically, "dynamically generating multiple diagnostic direction suggestions" is a key step in providing flexible, non-preset diagnostic guidance for maintenance personnel when uninterruptible power supply (UPS) systems encounter complex or unknown fault scenarios. This process is not simply about matching known fault templates, but rather based on in-depth analysis of the system's operating status and dynamic judgment of the situation.

[0058] Specifically, the technical means employed and the parameters upon which the generated suggestions are based are mainly reflected in the following aspects:

[0059] First, adjusting the triggering mechanism and assessment background are prerequisites for dynamically generating recommendations. When the system acquires the correlation characteristics of the uninterruptible power supply system's operating parameters and monitors the evolution trajectory of these correlation characteristics, and then identifies deviations from the evolution trajectory to trigger situational change events, the system determines that the current situation may exceed the usual known fault modes. At this point, the system marks the current situation as an "unknown complex disturbance" and suspends the direct comparison between the efficiency of the maintenance personnel's diagnostic path and the preset single fault template. This means that traditional diagnostic methods based on fixed rules or preset fault libraries are no longer applicable, and the system needs to adopt a more flexible strategy to generate diagnostic recommendations.

[0060] Secondly, the core parameters upon which the recommendations are based are the "correlation characteristics of uninterruptible power supply system operating parameters" and the "situation change events" derived from them.

[0061] The correlation characteristics of operating parameters: This refers to the quantitative description of the interactions between various operating parameters in an uninterruptible power supply (UPS) system (such as input voltage, output voltage, battery current, battery temperature, load rate, frequency, harmonic content, etc.). For example, under normal circumstances, there may be a stable positive correlation between battery charging current and battery temperature; when the system malfunctions, this correlation may change, such as an abnormal increase in charging current without a significant change in battery temperature, or unexpected fluctuations between the two. The quantitative description of these correlation characteristics provides the system with richer and deeper system state information than a single parameter threshold.

[0062] Contextual Change Events: When the evolution trajectory of the aforementioned correlated features deviates significantly, the system triggers a contextual change event. This event not only includes the type and degree of deviation but also implies the anomalous nature that the current system may be facing. For example, if the correlation between multiple key parameters changes drastically at the same time, it may indicate multiple faults or deep-seated unknown problems in the system.

[0063] Third, the technical means of dynamically generating multiple diagnostic direction suggestions:

[0064] Since the system has marked the scenario as "unknown complex disturbance" and suspended single fault template comparison, the technical means for generating recommendations need to have stronger reasoning and exploratory capabilities. Those skilled in the art can employ one or more of the following technical means:

[0065] Anomaly pattern-based reasoning: The system can maintain a broader anomaly pattern library. This library does not directly correspond to specific faults but rather describes the types of deviations in associated characteristics of different parameters. When a situational change event occurs, the system matches or infers several possible anomaly types from the anomaly pattern library based on the deviation patterns of the current associated characteristics. Each anomaly type corresponds to one or more diagnostic directions. For example, if abnormal fluctuations in the phase angles of voltage and current are detected but no known fault is matched, the system might suggest "checking the power factor correction circuit" or "investigating changes in load characteristics."

[0066] Based on knowledge graphs or expert systems: The system can construct a knowledge graph for the uninterruptible power supply (UPS) system, which includes component functions, interdependencies, common fault mechanisms (even if not direct templates), and diagnostic principles. When faced with unknown disturbances, the system can use the knowledge graph for heuristic search or reasoning, deduce the components or subsystems that may be causing these anomalies based on the correlation characteristics of current abnormal parameters, and generate diagnostic suggestions for these components or subsystems. For example, if the correlation between battery temperature and charging current is abnormal, the system might suggest "checking the battery management system sensors" or "evaluating the output stability of the charging module."

[0067] Data-driven similarity analysis: The system can store a large amount of historical operational data, including normal operation, known faults, and some complex anomalies that are not clearly categorized. When an "unknown complex disturbance" occurs, the system can use machine learning algorithms (such as clustering and dimensionality reduction) to analyze the correlation features of the current situation and find the most similar anomaly patterns in historical data. Even if these historical patterns do not have explicit fault labels, their corresponding diagnostic operation sequences can serve as a reference for generating new suggestions. For example, if the current parameter fluctuation pattern is similar to a complex anomaly in the past that was ultimately resolved by "replacing the control board," the system may suggest "checking the control board status."

[0068] Heuristic rules and general diagnostic principles: In situations where the problem is completely unknown, the system can revert to more general diagnostic principles, such as "from simple to complex" or "from upstream to downstream." By combining the physical location and functional characteristics of the current abnormal parameters, some general troubleshooting directions can be generated. For example, if multiple output parameters are abnormal, the system might suggest "checking the main control unit" or "checking the common bus."

[0069] For example:

[0070] Suppose that a UPS power system in a data center suddenly experiences a situational change event at night: the system detects a sharp increase in the harmonic content of the output voltage within a short period of time, accompanied by slight fluctuations in the temperature of the inverter module, while the input current and battery status remain normal. This combination of events does not perfectly match the system's preset single fault templates such as "inverter failure" or "load overload," therefore the system labels it as an "unknown complex disturbance."

[0071] At this point, the system will dynamically generate multiple diagnostic suggestions instead of simply indicating "inverter fault". This might be based on the following reasoning:

[0072] Based on anomaly pattern reasoning: Increased harmonic content may be related to nonlinear loads or the inverter's own control. Inverter temperature fluctuations may indicate abnormal operating conditions.

[0073] Based on knowledge graphs: the inverter module is related to the output filter, control unit, load characteristics, etc.

[0074] Data-driven similarity analysis: In historical data, are there similar cases of atypical inverter failures with elevated harmonics, and what effective diagnostic steps were taken by maintenance personnel at that time?

[0075] Based on these analyses, the system may dynamically generate the following diagnostic recommendations: Check the output filter status: Increased harmonics may be related to aging or damage to the output filter. Analyze load characteristic changes: Is there a new nonlinear load being connected, causing harmonic reinjection? Evaluate inverter control parameters: The inverter control algorithm may generate harmonics under specific operating conditions; it is recommended to check its control parameter settings. Monitor the waveforms of the inverter's internal switching devices: Although temperature fluctuations may be slight, early device failures may exist; further investigation is recommended.

[0076] These suggestions are dynamically generated and are designed to provide operations and maintenance personnel with multi-faceted diagnostic approaches, helping them to conduct exploratory diagnostics in an orderly manner when facing unknown and complex disturbances.

[0077] This application proposes a process for analyzing operational behavior based on operational behavior information to obtain an assessment of the operational capabilities of maintenance personnel, including the following steps:

[0078] Identify the operating conditions of uninterruptible power supply systems. These conditions include the rate of change of the conditions, the range of operating parameters involved, and the degree of deviation of the conditions from known fault modes.

[0079] Based on the characteristics of the operating environment, the evaluation rules used to quantify the information value brought by each operation by the operations and maintenance personnel are adjusted. The evaluation rules are used to determine the contribution of the operations and maintenance personnel's operations to the current diagnostic path.

[0080] Based on the real-time feedback from operations and maintenance personnel performing operations in the current context, the quantitative standards corresponding to the information value brought by each operation are adjusted. The real-time feedback includes the success status of the operation, whether it leads to new data discovery, and the role of new data discovery in promoting the current diagnostic direction.

[0081] Based on the revised evaluation rules and quantitative standards, each operation by operations and maintenance personnel is assigned information value. The evaluation weight of operations and maintenance personnel's performance indicators is adjusted based on the accumulation of information value assigned to each operation. Furthermore, the performance score of operations and maintenance personnel is dynamically adjusted based on their contribution to path optimization for each operation.

[0082] Specifically, identifying the operational context characteristics of an uninterruptible power supply (UPS) system refers to the system's ability to monitor and analyze various operational data in real time to extract the specific operational state of the current system. The rate of change of the context can be understood as the frequency and amplitude of fluctuations in key operational parameters (such as voltage, current, and temperature) over a short period, reflecting the dynamic nature of the system state. The range of operational parameters involved refers to the number and types of system components or modules affected by the current anomaly or diagnostic activities, reflecting the breadth of the problem. The degree of deviation between the context and known fault modes refers to the similarity or difference between the current system state and preset, known fault modes, reflecting the unknown and complex nature of the problem. Through comprehensive analysis of these characteristics, the operational context faced by the current UPS system can be fully characterized.

[0083] The purpose of adjusting the evaluation rules based on the characteristics of the operational scenario is to enable the evaluation system to adapt to different levels of complexity and urgency. For example, in situations where the scenario changes rapidly, involves a wide range of parameters, and deviates significantly from known fault modes, each effective operation by operations personnel may have higher informational value. Therefore, the evaluation rules will be adjusted to more favor exploratory operations and rapid responses. Specifically, the evaluation rules are used to determine the contribution of operations personnel's actions to the current diagnostic path. For instance, in complex scenarios, even preliminary troubleshooting operations will have their contribution increased if they effectively narrow down the fault scope or eliminate erroneous assumptions.

[0084] In practical applications, the quantification standards are adjusted based on real-time feedback from operations personnel performing operations in the current context. The aim is to ensure that the assessment of information value fully reflects the actual effectiveness of the operations and their contribution to the diagnostic process. Real-time feedback includes the success of the operation, such as whether a test command was successfully executed and returned the expected result; whether it led to new data discoveries, such as revealing previously undetected anomalies in logs or sensor readings; and the impact of these new data discoveries on the current diagnostic direction, such as whether newly discovered data clearly points to a faulty component or rules out a diagnostic hypothesis. This real-time feedback provides the system with immediate calibration information, making the quantification of the information value brought by each operation by operations personnel more accurate and dynamic.

[0085] Therefore, based on the adjusted evaluation rules and quantitative standards, each operation by operations and maintenance (O&M) personnel is assigned information value, and the evaluation weight of O&M personnel's performance indicators is adjusted according to the accumulation of information value. This means that the higher the information value accumulated by O&M personnel through effective operations in complex situations, the higher their weight in performance evaluation may be. Furthermore, based on the contribution of each O&M personnel's operation to path optimization, their performance scores are dynamically adjusted. The aim is to encourage O&M personnel not only to solve problems, but also to solve them efficiently and accurately, thereby optimizing the entire diagnosis and recovery path.

[0086] This application's solution introduces the identification of uninterruptible power supply (UPS) system operating scenario characteristics, enabling the assessment of maintenance personnel's operational capabilities to move beyond a static approach and instead adaptively adjust based on dynamic changes in the scenario. When the system identifies an accelerated rate of scenario change, an expanded range of operating parameters, or an increased deviation from known fault modes, it indicates that the current scenario is more complex or unknown. In such cases, traditional assessment methods based on fixed templates may fail to accurately measure the true capabilities of maintenance personnel. This solution, by adjusting assessment rules, such as increasing the weight of exploratory operations and key information acquisition, can more reasonably quantify the operational value of maintenance personnel in complex scenarios.

[0087] Furthermore, by acquiring real-time feedback from operations and maintenance personnel—such as the success or failure of the operation, whether it yields new data discoveries, and the impact of these discoveries on diagnostic direction—this solution can adjust the quantitative standards for information value. This ensures that even in highly dynamic situations, every effective attempt and key discovery by operations and maintenance personnel can be accurately captured and given corresponding value. For example, in an unknown fault scenario, a seemingly simple parameter query operation will have significantly increased information value if its results reveal crucial new clues. This dynamic adjustment mechanism allows the performance evaluation of operations and maintenance personnel to more accurately reflect their actual contributions and capabilities in dealing with complex and uncertain problems.

[0088] In some preferred embodiments, suppose an uninterruptible power supply (UPS) system suddenly experiences abnormal output voltage fluctuations overnight, accompanied by a rapid increase in temperature sensor readings for multiple critical modules. The system first identifies the characteristics of the current operating situation: the situation changes rapidly (high frequency of voltage and temperature fluctuations), involves a wide range of operating parameters (multiple modules are affected), and deviates significantly from known voltage anomaly fault modes (typical voltage anomalies are not accompanied by such a widespread temperature rise).

[0089] Based on these contextual characteristics, the system dynamically adjusts the evaluation rules for the value of operational information from maintenance personnel. For example, the weight of exploratory operations such as "in-depth analysis of system logs" and "isolation testing of specific modules" is increased, because these operations are more likely to reveal the root cause in unknown contexts.

[0090] At this point, maintenance personnel A intervened and first performed a comprehensive search of the system logs. Real-time system feedback showed that the operation was successful and uncovered a series of previously unnoticed internal communication error logs, pointing to an intermittent failure in a certain control unit. Based on this real-time feedback, the system assigned high informational value to maintenance personnel A's log search operation and adjusted the quantification criteria, as this operation led to the discovery of new critical data and significantly advanced the diagnostic direction (pointing to the control unit).

[0091] Subsequently, based on the newly discovered clues, maintenance personnel A further performed diagnostic testing on the control unit. The system again assigned high information value based on the success of the operation and the test results (confirming the intermittent fault in the control unit). Ultimately, the system adjusted the evaluation weights of maintenance personnel A's performance indicators based on the information value accumulated throughout the diagnostic process, and dynamically adjusted their performance score based on their contribution to optimizing the diagnostic path with each operation (quickly locating and confirming complex faults), thus accurately reflecting maintenance personnel A's outstanding fault diagnosis capabilities in complex situations.

[0092] Specifically, in the UPS power user management methodology, the specific value of "contribution" in the phrase "assessment rules are used to determine the contribution of maintenance personnel's operations to the current diagnostic path" is determined comprehensively based on a series of dynamically adjusted assessment rules and quantitative standards. Its core lies in quantifying the information value brought about by each operation by maintenance personnel. This process is not simply assigning a fixed value, but rather adaptively adjusting based on the operating characteristics of the uninterruptible power supply system and the real-time feedback from maintenance personnel's operations.

[0093] Specifically, the process of determining the specific value of "contribution" (i.e., the information value brought about by each operation) can be elaborated as follows:

[0094] The evaluation rules are adjusted based on the characteristics of the operating context to determine the initial contribution tendency of the operation:

[0095] First, the system identifies the current operating situation characteristics of the uninterruptible power supply (UPS) system. These characteristics include: the rate of change of the situation (referring to the frequency and amplitude of fluctuations in system operating parameters such as voltage, current, and temperature over a short period of time); the range of operating parameters involved (referring to the number and type of system components or modules affected by the current anomaly or diagnostic activity); and the degree of deviation between the situation and known fault modes (referring to the similarity or difference between the current system state and preset, known fault modes).

[0096] Based on these identified operational scenario characteristics, the system dynamically adjusts the evaluation rules used to quantify the information value brought by each operation by maintenance personnel. These evaluation rules determine the contribution tendency of maintenance personnel's operations to the current diagnostic path. For example: When the scenario changes rapidly and is highly urgent, the evaluation rules tend to assign higher contribution weights to operations that can quickly stabilize the system, obtain key instantaneous data in a timely manner, or quickly eliminate high-risk options. This is because in time-sensitive environments, rapid and effective action has higher value. When the operational parameters involved are wide-ranging and complex, the evaluation rules tend to assign higher contribution weights to operations that can effectively narrow down the scope of the fault, identify the core area of ​​impact, or establish correlations between parameters. This helps to quickly focus on the problem in complex systems. When the scenario deviates significantly from known failure modes and is highly unknown, the evaluation rules tend to assign higher contribution weights to operations that are exploratory, can discover new clues, and verify or refute existing hypotheses. This is because it helps to solve unprecedented unknown problems.

[0097] In this way, the evaluation rules set different "contribution" benchmarks or tendencies for different types of operations based on the dynamic changes in the situation.

[0098] The quantitative standards are adjusted based on real-time feedback from operations and maintenance personnel to determine the actual contribution value of the operation.

[0099] Under the revised evaluation framework described above, the quantitative standard corresponding to the information value of each operation by operations personnel will be adjusted based on the real-time feedback from the operations personnel performing the operation in the current context. This real-time feedback directly affects the actual "contribution" value of the operation:

[0100] Operation success rate: If the operations personnel successfully execute the operation and achieve the expected results (e.g., a test command runs successfully and returns valid data), the "contribution" value of the operation will increase accordingly. If the operation fails or fails to achieve the expected results, its "contribution" value will decrease.

[0101] Does it lead to new data discoveries? If the operations personnel's actions reveal critical data, abnormal logs, or new system behaviors that were previously undetected by the system or operations personnel, the "contribution" value of the action will be significantly increased. If the action only repeats known information, its "contribution" value will be relatively low.

[0102] The contribution of new data findings to the current diagnostic direction: If newly discovered data can clearly narrow down the scope of the fault, strongly validate a diagnostic hypothesis, or provide a clear new direction for subsequent diagnoses, then the "contribution" of this operation will be further increased. If the new data does not significantly contribute to the diagnostic direction, its "contribution" will be relatively low.

[0103] The final contribution value (information value) is determined comprehensively based on the adjusted evaluation rules and quantitative standards: Ultimately, the specific value of the "contribution" brought about by each operation by maintenance personnel, i.e., its "information value," is determined comprehensively based on the adjusted evaluation rules and quantitative standards. This means that the "contribution" of an operation depends not only on its importance in a specific context (determined by the adjusted evaluation rules), but also on its specific effects in actual execution and its actual contribution to the diagnostic process (determined by the quantitative standards adjusted through real-time feedback). Through this dynamic and adaptive mechanism, the system can assign an "information value" reflecting the true "contribution" of each operation by maintenance personnel.

[0104] For example, in an emergency fault scenario where the situation changes rapidly and deviates significantly from known fault modes, operations personnel might perform a seemingly simple parameter query. If this query successfully identifies a previously unnoticed critical parameter anomaly, and this anomaly clearly points to a new diagnostic direction, significantly narrowing the fault scope, then even if the operation itself is not complex, its "contribution" (information value) will be evaluated as very high. This is because the adjusted evaluation rules give higher weight to exploratory operations and the discovery of key information, while real-time feedback confirms that the operation brought new key data and drove diagnostic progress.

[0105] Furthermore, in the UPS power user management methodology, determining the specific value of "contribution" in "based on the contribution of each operation by maintenance personnel to path optimization" is a multi-dimensional and dynamic evaluation process. It comprehensively considers factors such as the information value brought by each operation by maintenance personnel, the actual role of this information in the diagnostic chain, and the complexity of the current diagnostic situation. The determination process will be explained in detail below:

[0106] First, understand the essence of "contribution." Here, "contribution" refers to how each action taken by operations and maintenance personnel effectively advances fault diagnosis, narrows down the fault scope, verifies or corrects diagnostic hypotheses, and ultimately optimizes the entire diagnostic path from problem discovery to resolution. Quantifying this contribution forms the basis for evaluating operations and maintenance personnel's operational capabilities and dynamically adjusting their performance scores.

[0107] Secondly, determining the specific value of the "contribution" requires consideration of the following core aspects:

[0108] Initial Assignment of Information Value and Retrospective Adjustment: Initial Information Value Assignment: Each operation performed by maintenance personnel and its direct results (including operation type, operation object, system feedback data, operation timestamp, etc.) are recorded in real time. The system, combined with the operational context characteristics of the uninterruptible power supply (UPS) system (rate of change of context, range of operating parameters involved, degree of deviation of context from known fault modes), assigns an initial information value to each operation. This initial value reflects the immediate utility of the operation. Retrospective Adjustment: After initially assigning information value, the system continuously tracks the subsequent operation sequence of maintenance personnel and monitors the evolution of UPS system operating parameters. When subsequent operations or system parameter evolution reveal new diagnostic clues or lead to revisions of early diagnostic assumptions, the system retrospectively adjusts the information value assigned to the early operations. Specifically, if the information obtained from the early operations proves to be critical in subsequent diagnoses, its information value is increased; if it proves to be secondary or misleading, its information value is decreased.

[0109] Calculation of the contribution of early procedural information to subsequent diagnostic steps:

[0110] Before retrospectively adjusting the value of early operational information, it is necessary to accurately calculate the contribution of the information obtained from early operations to each subsequent diagnostic step. This includes the following aspects: Identifying correlation paths, such as: Direct correlation paths: Identifying direct correlation paths between early operational information and subsequent diagnostic steps, i.e., diagnostic steps where the information is cited, verified, or refuted in subsequent diagnostic stages. Indirect correlation paths: Identifying the indirect impact of early operational information on the subsequent diagnostic behavior of operations personnel based on the diagnostic tools selected, data sources consulted, and changes in the operation sequence of operations personnel in subsequent operations, thereby constructing indirect correlation paths. Heuristic correlation paths: Extracting mentions, evaluations, or applications of the information obtained from early operations from the text descriptions or voice recordings entered by operations personnel during subsequent diagnostic processes, thereby constructing heuristic correlation paths. Regarding the calculation of contribution values, initial weight adjustment: Adjusting the initial weights of the above correlation paths, indirect correlation paths, and heuristic correlation paths based on the current diagnostic situation characteristics of the uninterruptible power supply system (urgency of the situation, complexity of the situation, deviation of the situation from known failure modes). Role Identification and Contribution Calculation: Identify the role of early operational information in narrowing the fault scope, validating diagnostic hypotheses, or advancing diagnostic progress in each subsequent diagnostic step. Calculate the contribution value of early operational information in the corresponding subsequent diagnostic step based on the adjusted initial weights and the strength and nature of the role. Contribution Accumulation: Continuously track the propagation and impact of early operational information in the diagnostic chain. When early operational information is repeatedly cited, verified, or refuted in subsequent diagnostic steps, accumulate the contribution value of early operational information in different subsequent diagnostic steps based on its actual impact at different diagnostic stages. Exploratory Contribution: Based on the mentions, evaluations, or applications of early operational information in text descriptions or voice recordings entered by maintenance personnel during subsequent diagnostic processes, assign additional exploratory contributions to the accumulated contribution value of early operational information to form a modified contribution value.

[0111] Differential weighting to determine the final contribution: After obtaining the modified contribution value, the system comprehensively considers the characteristics of the diagnostic steps themselves and applies differential weighting to determine the final contribution of the information obtained from early operations to each subsequent diagnostic step. Evaluation factor adjustment: The complexity evaluation factor for the diagnostic steps is dynamically adjusted based on the rate of change of the situation. The risk level evaluation factor for the diagnostic steps is dynamically adjusted based on the range of operating parameters involved in the situation. The criticality evaluation factor for the diagnostic steps is dynamically adjusted based on the degree of deviation of the situation from known failure modes. Differential weighting is assigned: Based on the adjusted complexity evaluation factor, risk level evaluation factor, and criticality evaluation factor, differentiated weighting is assigned to the modified contribution value. Finally, through the above refined calculation and adjustment process, the system can re-accumulate the total information value of operations personnel throughout the entire diagnostic chain. Based on this re-accumulated total information value, as well as the situation complexity factor and exploration score, the system will dynamically adjust the evaluation weights of the operations personnel's performance indicators, and ultimately dynamically adjust the operations personnel's performance score based on the contribution of each operation to path optimization. The specific value of this "contribution" is ultimately determined through the aforementioned progressive quantification, tracking, adjustment, and weighting mechanisms.

[0112] This application further proposes that, based on operational behavior information, the steps for analyzing operational behavior to obtain an assessment of the operational capabilities of maintenance personnel include:

[0113] It records every operation and maintenance personnel perform in real time and the direct results of each operation. The direct results include the operation type, the operation object, the system feedback data, and the operation timestamp.

[0114] Based on each operation performed by maintenance personnel and the direct results of each operation, and combined with the operational context characteristics of the uninterruptible power supply system, each operation is initially assigned an information value. The operational context characteristics include the rate of change of the context, the range of operational parameters involved in the context, and the degree of deviation of the context from known fault modes.

[0115] After initially assigning informational value, we continuously track the subsequent operation sequence of maintenance personnel and monitor the evolution of uninterruptible power supply system operating parameters;

[0116] When subsequent operation sequences by maintenance personnel or the evolution of uninterruptible power supply system operating parameters reveal new diagnostic clues or lead to revisions of early diagnostic assumptions, the information value assigned to the early operations should be retrospectively adjusted. Adjustments may include: increasing the information value assigned to the early operations if the information obtained from the early operations proves to be critical in subsequent diagnoses; and decreasing the information value assigned to the early operations if it proves to be secondary or misleading.

[0117] Based on the adjusted information value, the total information value of operations and maintenance personnel throughout the entire diagnostic chain is re-accumulated;

[0118] Based on the sum of the re-accumulated information value, as well as the situational complexity factor and exploration score, the evaluation weight of the performance indicators of operation and maintenance personnel is dynamically adjusted, and the performance score of operation and maintenance personnel is dynamically adjusted based on the contribution of each operation of operation and maintenance personnel to path optimization.

[0119] Specifically, real-time recording of every operation and its direct results by maintenance personnel refers to the system's detailed logging of every action performed by maintenance personnel on the uninterruptible power supply (UPS) system. These direct results may include, but are not limited to, the type of operation performed by maintenance personnel, such as inspection, testing, and configuration modification; the specific object targeted by the operation, such as a battery module, inverter unit, or sensor; real-time feedback data returned by the system after the operation, such as changes in voltage, current, and temperature readings; and the exact timestamp of the operation. The purpose is to provide comprehensive and accurate raw data for subsequent information value assessment.

[0120] Assigning an initial informational value to each operation can be understood as the system initially quantifying its potential contribution to the diagnostic process after an operation is performed by maintenance personnel, based on the operation's immediate impact and the current operational context characteristics of the uninterruptible power supply (UPS). Operational context characteristics specifically refer to the rate of change of the current context, such as the severity of system parameter fluctuations; the range of operational parameters involved in the context, such as the number of affected device modules or the types of parameters; and the degree of deviation of the current context from known fault modes, such as whether it closely matches typical patterns in the historical fault database. The purpose is to make an immediate and preliminary value judgment on each operation performed by maintenance personnel, laying the foundation for subsequent dynamic adjustments.

[0121] In practical applications, continuously tracking the subsequent operation sequence of maintenance personnel and monitoring the evolution of uninterruptible power supply (UPS) system operating parameters means that the system continuously collects all operations performed by maintenance personnel during the diagnostic process and monitors the changing trends of various operating parameters of the UPS system in real time, such as key indicators like voltage, current, frequency, and temperature. The purpose is to capture the dynamics of the diagnostic process and provide data support for identifying new diagnostic clues or revising early diagnostic hypotheses.

[0122] Furthermore, when subsequent operations by maintenance personnel or the evolution of uninterruptible power supply (UPS) system operating parameters reveal new diagnostic clues or lead to revisions of early diagnostic assumptions, the system retrospectively adjusts the information value attributed to earlier operations. For example, if maintenance personnel performed a seemingly unrelated voltage measurement early on, but subsequent abnormal fluctuations in system parameters and further diagnostic operations ultimately prove that the early measurement data was crucial for identifying the root cause of the fault, then the information value of that early operation will be enhanced. Conversely, if the information obtained from an early operation proves to be secondary, redundant, or even misleading in subsequent diagnostics, its information value will be reduced. The aim is to ensure that the assessment of maintenance personnel's operational capabilities fully reflects their true contributions and the accuracy of their information judgments in complex, dynamic diagnostic situations.

[0123] Therefore, based on the adjusted information value, the total information value of operations and maintenance personnel throughout the entire diagnostic chain is re-accumulated. This means that the system will sum up the adjusted information value of all operations and maintenance personnel's actions throughout the diagnostic process to obtain a more comprehensive and accurate overall measurement of information contribution. Its purpose is to provide a reliable quantitative basis for the final performance evaluation.

[0124] Ultimately, based on the sum of the re-accumulated information value, as well as the situation complexity factor and exploration score, the evaluation weights of the operations and maintenance personnel's performance indicators are dynamically adjusted. Furthermore, the performance score of operations and maintenance personnel is dynamically adjusted based on their contribution to path optimization for each operation. Specifically, the situation complexity factor can be dynamically adjusted according to the complexity of the current fault situation, such as the unknown nature of the fault mode or the severity of parameter fluctuations; the exploration score is used to reward the value of operations and maintenance personnel's exploratory operations in unknown or complex situations. The aim is to make the performance evaluation of operations and maintenance personnel more fair and objective, and to encourage them to actively explore and optimize diagnostic paths in complex situations.

[0125] This application's solution effectively addresses the limitations of traditional assessment methods by introducing a dynamic, retrospective adjustment mechanism for the value of operational information from maintenance personnel. Specifically, this mechanism addresses the inability to accurately assess the true contribution of early operations in complex diagnostic scenarios. By recording each operation and its direct results in real time, and combining this with the operational characteristics of the uninterruptible power supply (UPS) system, the system can initially assign an information value to each operation. This lays the foundation for subsequent dynamic assessment. More importantly, by continuously tracking the evolution of subsequent operational sequences and system operating parameters, this solution can identify new diagnostic clues or corrections to early diagnostic assumptions. Once this occurs, the system can retrospectively adjust the information value assigned to early operations. For example, an operation that initially seems unimportant may have its information value increased if the information it acquires proves crucial to problem-solving in subsequent diagnoses; conversely, if it proves secondary or misleading, its information value will be reduced. This retrospective adjustment mechanism ensures that the assessment of the value of operations by maintenance personnel is based on the final result and information evolution process of the entire diagnostic chain, rather than solely on the immediate effects of the operation. Therefore, by re-accumulating and adjusting the total value of information, and combining the situational complexity factor and exploration score, the system can dynamically adjust the evaluation weights and performance scores of the operation and maintenance personnel's performance indicators, thereby more accurately and fairly reflecting the true capabilities and contributions of operation and maintenance personnel in complex fault diagnosis, and motivating them to conduct effective exploration and path optimization in unknown situations.

[0126] This application further proposes the following steps before retrospectively adjusting the information value assigned to earlier operations:

[0127] When the subsequent operation sequence of maintenance personnel or the evolution of uninterruptible power supply system operating parameters reveals new diagnostic clues or leads to the revision of the assumptions of early diagnosis, identify the correlation path between the information obtained from the early operation and the subsequent diagnostic steps. The correlation path includes the diagnostic links where the information is cited, verified or refuted.

[0128] Based on the associated path, the contribution of information obtained in the early operations to each subsequent diagnostic step is calculated. The contribution is determined by evaluating the role of the information in narrowing the scope of the fault, verifying diagnostic hypotheses, or promoting diagnostic progress in a specific diagnostic stage.

[0129] Based on the degree of contribution, the information value of the early actions is allocated to each subsequent diagnostic step affected by the early actions. After allocation, the information value assigned to the early actions is retrospectively adjusted based on the degree of contribution.

[0130] Specifically, identifying the correlation path between information obtained from early operations and subsequent diagnostic steps refers to how system or operations personnel track how data, observations, or conclusions obtained from early operations are used in subsequent diagnostic stages. For example, if operations personnel explicitly cite a parameter value obtained from early operations in subsequent steps to support or refute a diagnostic hypothesis, or if anomalies discovered in early operations are further verified in subsequent steps, then a correlation path can be considered to exist. This correlation path includes not only direct citations but also situations where information is indirectly verified or refuted. The correlation path can be constructed as a directed graph, where nodes represent diagnostic steps, and edges represent information flow and influence relationships.

[0131] Furthermore, calculating the contribution of information acquired in early operations to each subsequent diagnostic step can be understood as quantifying the actual utility of early information in a specific diagnostic stage. For example, if the information acquired in early operations narrows the range of possible faults from ten to three, its contribution is high; if the information verifies a key diagnostic hypothesis, avoiding unnecessary investigation, its contribution is also high. The determination of the contribution level can be based on pre-set machine learning algorithms or expert experience; for example, it can be quantified based on indicators such as the degree to which the information reduces diagnostic uncertainty and the clarity of the diagnostic direction.

[0132] In practical applications, allocating the information value of early actions to each subsequent diagnostic step affected by those actions means distributing the overall information value of the early actions proportionally or by weight to each subsequent diagnostic stage based on their calculated contribution. For example, if the information from an early action contributes 60% to subsequent step A and 40% to subsequent step B, the overall information value of the early action will be allocated proportionally. After allocation, the original information value of the early actions is retrospectively adjusted based on these allocated contribution levels to ensure the accuracy and rationality of the adjustment.

[0133] This application addresses the aforementioned issues by introducing the identification of the correlation path between early operational information and subsequent diagnostic steps, and by quantifying the contribution of early information in each subsequent step. Specifically, when new diagnostic clues emerge, instead of simply adjusting the overall value of early operational information, it first precisely identifies which subsequent steps in the entire diagnostic chain the early information specifically affects, and how these effects occur (e.g., being cited, verified, or refuted). Subsequently, by calculating the contribution of early information to each affected subsequent diagnostic step, this application can meticulously assess the specific role of early information in narrowing the scope of the fault, verifying diagnostic hypotheses, or advancing diagnostic progress. Thus, the information value of early operations is rationally allocated to the various subsequent diagnostic steps it affects, ensuring finer granularity and more sufficient evidence for information value adjustments. This mechanism avoids a rough judgment of the value of early information, making retrospective adjustments more scientific and fair.

[0134] In some preferred embodiments, assuming an abnormal alarm occurs in the uninterruptible power supply (UPS) system, maintenance personnel A performs an early operation 1, "checking the battery pack voltage," and records the battery pack voltage data. Subsequently, maintenance personnel A continues with operations 2, "checking the inverter output waveform," and 3, "analyzing the load distribution." In operation 2, maintenance personnel A discovers a slight distortion in the inverter output waveform and, combined with the battery pack voltage data obtained in operation 1, initially determines that the problem may be caused by unstable battery power supply. In operation 3, maintenance personnel A further analyzes the load distribution, discovers that a certain load is excessively high, and, combining the information from operations 1 and 2, ultimately determines that the system abnormality is caused by a combination of battery performance degradation and localized overload.

[0135] At this point, according to the scheme of this application, the system will identify the correlation path between the battery pack voltage information obtained in Operation 1 and Operations 2 (checking the inverter output waveform) and 3 (analyzing the load distribution). For example, the information in Operation 1 is referenced in Operation 2 to verify the correlation between the inverter malfunction and battery power supply, and in Operation 3 it is used to comprehensively determine the cause of the fault. The system will calculate the contribution of the information in Operation 1 to Operations 2 and 3. Assuming that the calculation shows that the contribution of Operation 1 to Operation 2 is 60% and to Operation 3 is 40%, then the original information value of Operation 1 will be allocated to the diagnostic stages represented by Operations 2 and 3 in a ratio of 60% and 40%, respectively. After the allocation, the system will retrospectively adjust the information value assigned to Operation 1 based on these allocated contribution levels. For example, if the information in Operation 1 is proven to be critical in subsequent diagnostics, its information value will be increased, and this increase is weighted according to its contribution to subsequent specific steps, thereby making the performance evaluation of maintenance personnel A more accurate and fair.

[0136] This application further proposes steps for calculating the contribution of information acquired in early operations to each subsequent diagnostic step, including:

[0137] Based on the diagnostic tools selected by operations and maintenance personnel in subsequent operations, the data sources consulted, and the changes in the operation sequence of operations and maintenance personnel, the indirect impact of early operation information on the subsequent diagnostic behavior of operations and maintenance personnel is identified, thereby constructing indirect correlation paths;

[0138] Based on the text descriptions or voice recordings entered by maintenance personnel during subsequent diagnosis, extract the mentions, evaluations, or applications of information obtained from earlier operations to construct heuristic association paths;

[0139] Based on the correlation path, indirect correlation path, and heuristic correlation path, calculate the contribution of information obtained from early operations to each subsequent diagnostic step.

[0140] Specifically, when identifying the indirect impact of early operational information on the subsequent diagnostic actions of operations and maintenance (O&M) personnel, it's possible to monitor the specific diagnostic tools used by O&M personnel in subsequent diagnostic stages, such as oscilloscopes, multimeters, or dedicated diagnostic software. The selection of these tools may be inspired by the information acquired earlier. Simultaneously, the data sources consulted by O&M personnel, such as system logs, historical fault records, or equipment manuals, may also indirectly reflect the guiding role of early information. Furthermore, changes in the O&M personnel's operational sequence, such as shifting from routine checks to in-depth investigation of specific components, can also reveal the indirect impact of early information on the diagnostic direction. By comprehensively analyzing these behavioral patterns, indirect correlation paths between early operational information and subsequent diagnostic actions can be constructed.

[0141] In constructing heuristic association paths, natural language processing and semantic analysis can be performed on text descriptions entered by operations and maintenance personnel during the diagnostic process, such as fault reports, diagnostic notes, or chat logs, as well as voice recordings, such as on-site communication recordings or voice memos. Through techniques such as keyword extraction, sentiment analysis, or topic modeling, the mention, evaluation, or application of information obtained from earlier operations can be identified. For example, if an operations and maintenance personnel mention in their description, "Based on the voltage data from the last inspection, I suspect it's a problem with the power module," it indicates that the earlier voltage data has provided insights into the current diagnosis. In this way, the heuristic contribution of early information to the operations and maintenance personnel's thinking and decision-making process can be quantified, thereby constructing heuristic association paths.

[0142] In practical applications, when calculating the contribution of information acquired through early operations to each subsequent diagnostic step, not only the direct correlation path between early operational information and subsequent diagnostic steps is considered, but also indirect and heuristic correlation paths are comprehensively taken into account. This means that the impact of early information is no longer limited to the scenarios in which it is directly referenced, but extends to its comprehensive influence on operations personnel's tool selection, data retrieval, operational sequence adjustments, and thought processes. By comprehensively evaluating these three correlation paths, the actual contribution of early operational information in the entire diagnostic chain can be quantified more comprehensively and accurately.

[0143] This application's solution effectively overcomes the shortcomings of relying solely on direct correlation paths for contribution evaluation by introducing indirect and heuristic correlation paths. The construction of indirect correlation paths allows for the identification and quantification of the implicit impact of early operational information on subsequent diagnostic actions by operations and maintenance personnel. For example, early information may prompt operations and maintenance personnel to choose specific diagnostic tools or consult specific data sources; these indirect actions are also significant for diagnostic progress. The construction of heuristic correlation paths further captures the role of early information in the cognitive level and decision-making process of operations and maintenance personnel. By analyzing the text or voice recordings of operations and maintenance personnel, it reveals how early information inspired their diagnostic thinking or validated their diagnostic hypotheses.

[0144] This application further proposes steps for calculating the contribution of information acquired in early operations to each subsequent diagnostic step, including:

[0145] Based on the diagnostic scenario characteristics of the current uninterruptible power supply system, adjust the initial weights of associated paths, indirect associated paths, and heuristic associated paths. The diagnostic scenario characteristics include the urgency and complexity of the scenario, as well as the degree of deviation of the scenario from known fault modes.

[0146] Identify the role of early operational information in narrowing the fault scope, verifying diagnostic hypotheses, or promoting diagnostic progress in each subsequent diagnostic step. Calculate the contribution value of early operational information in the corresponding subsequent diagnostic step based on the adjusted initial weights and the strength and nature of the role.

[0147] Continuously track the spread and impact of early operational information in the diagnostic chain. When early operational information is cited, verified or refuted multiple times in subsequent diagnostic steps, accumulate the contribution value of early operational information in different subsequent diagnostic steps based on the actual impact of early operational information at different diagnostic stages.

[0148] Based on the mentions, evaluations, or applications of early operation information in the text descriptions or voice recordings entered by maintenance personnel during subsequent diagnostics, additional exploratory contributions are assigned to the accumulated contribution value of the early operation information to form a modified contribution value.

[0149] Taking into account the complexity and risk level of the diagnostic steps themselves, as well as their criticality to the final fault resolution, the modified contribution values ​​are weighted differently to determine the degree of contribution of the information obtained in the early operations to each subsequent diagnostic step.

[0150] Specifically, diagnostic scenario characteristics refer to the current operating state and fault background of the uninterruptible power supply (UPS) system. These characteristics include the urgency of the scenario, its complexity, and its deviation from known fault modes. The urgency of the scenario can be understood as the immediacy and severity of the fault's impact on system operation, such as whether it has already led to downtime or data loss. The complexity of the scenario refers to the diversity of fault phenomena, the breadth of associated parameters, and the ambiguity of potential causes. The deviation from known fault modes measures the degree of matching between the current fault and typical patterns in the historical fault database. By identifying these diagnostic scenario characteristics, the initial weights of the aforementioned correlation paths, indirect correlation paths, and heuristic correlation paths can be dynamically adjusted. The aim is to make the evaluation of early operational information more aligned with the current actual diagnostic needs and challenges. For example, in emergency scenarios, operational information that can quickly eliminate significant risks should receive a higher initial weight.

[0151] Identifying the role of early operational information in narrowing the scope of the fault, validating diagnostic hypotheses, or advancing diagnostic progress in each subsequent diagnostic step refers to assessing the specific utility of information acquired through early operations in subsequent diagnostic stages. Narrowing the scope of the fault means eliminating some potential sources of failure through early information; validating diagnostic hypotheses means that early information provides evidence to support or refute a diagnostic inference; advancing diagnostic progress means that early information provides new directions or basis for subsequent diagnostic operations or decisions. Based on the adjusted initial weights and the strength and nature of these effects, the contribution value of early operational information in the corresponding subsequent diagnostic steps can be calculated, with the aim of quantifying the direct value of information at a specific stage.

[0152] Specifically, understanding and determining the "strength and nature of the effect" is crucial when assessing the contribution of early operational information to each subsequent diagnostic step. This ensures that the evaluation of operations personnel's operational capabilities fully reflects their true contribution in complex diagnostic scenarios.

[0153] How to determine the "intensity and nature of the action":

[0154] Determining the nature of the action:

[0155] "Nature of effect" refers to the specific type of utility that early operational information exerts in subsequent diagnostic processes. According to the description in this application, it mainly includes the following three properties:

[0156] Narrowing the scope of the fault: This means that early information effectively reduces the set of possibilities that need to be considered for diagnosis by eliminating some potential sources of failure. For example, ruling out a power module fault through a single voltage measurement allows the diagnostic focus to shift to other components.

[0157] Validating diagnostic hypotheses: This means that early information provides evidence to support or refute a diagnostic inference. For example, operations personnel may propose an initial hypothesis based on an anomaly, and early data may confirm or refute that hypothesis.

[0158] Driving diagnostic progress: This refers to early information providing new directions or basis for subsequent diagnostic operations or decisions, enabling the diagnostic process to continue or accelerate. For example, an early detection of an abnormal parameter, even if not a direct point of failure, might guide maintenance personnel to inspect another related system component.

[0159] The system identifies the nature of the role of early information by analyzing the specific behaviors of maintenance personnel in subsequent diagnostic steps (such as the diagnostic tools selected, the data sources consulted, and changes in the operation sequence) as well as their mentions, evaluations, or applications of early information in their input text descriptions or voice recordings.

[0160] Determining the intensity of the effect:

[0161] "Intensity of effect" refers to the significance or influence of early operational information in achieving the aforementioned properties. Its quantification method varies depending on the nature of the effect:

[0162] Regarding “reducing the scope of failures”: The intensity can be quantified by the proportion by which the set of possible failures is reduced. For example, if early information reduces the number of potential failure sources from 10 to 2, the intensity is high; if it reduces by only 1, the intensity is low.

[0163] For "validating a diagnostic hypothesis": Strength can be measured by the certainty or confidence of the evidence. For example, if early information provides conclusive evidence that fully confirms or refutes a key hypothesis, the strength is high; if it only provides partial support or suggestion, the strength is low.

[0164] Regarding "driving diagnostic progress": the intensity can be assessed by the importance of the new direction or evidence, and the degree to which the diagnostic pathway is optimized. For example, if early information moves the diagnosis from a standstill to a clear next step, or significantly shortens the diagnostic time, the intensity is high.

[0165] These strengths are typically determined through pre-defined quantitative models, expert rules of experience, or statistical analysis based on historical data.

[0166] Regarding the role of "the nature of the effect" in the subsequent calculation of the contribution value:

[0167] The nature of the effect plays a crucial classification and guiding role in calculating the contribution value. It determines: the choice of intensity quantification method: Different effects require different indicators to quantify their intensity. For example, "reducing the scope of the fault" might be measured as a percentage, while "validating diagnostic hypotheses" might be measured as a confidence score. The nature of the effect guides the system in choosing the correct intensity quantification method. The basis for weight allocation: In certain diagnostic scenarios, effects of different natures may be assigned different importance weights. For example, in an emergency fault scenario, "reducing the scope of the fault" might receive a higher base weight than "advancing diagnostic progress" because it is directly related to quickly locating the problem. The nature of the effect provides the logical basis for these differentiated weight allocations. The choice of evaluation model: The calculation of the contribution value may involve multiple evaluation models, each optimized for a specific effect. The nature of the effect helps the system select the model most suitable for the current evaluation scenario.

[0168] Regarding the specific calculation method for contribution value:

[0169] According to the description in this application, the contribution of early operational information to the corresponding subsequent diagnostic steps is calculated based on the "adjusted initial weights" and the "intensity and nature of the effect." The calculation logic can be summarized as follows:

[0170] Contribution value = Adjusted initial weight × Intensity factor × Nature weight

[0171] Adjusted initial weights: These are the initial weights of associated paths, indirect associated paths, and heuristic associated paths, dynamically adjusted based on the diagnostic context characteristics of the current uninterruptible power supply system (including the urgency, complexity, and deviation from known failure modes). For example, in urgent and complex unknown situations, the initial weights are increased to encourage exploratory operations.

[0172] Action intensity factor: This is a factor that quantifies the "intensity of action" mentioned above and converts it into a standardized or normalized factor. For example, if the intensity of "reducing the fault range" is 60%, then the action intensity factor might be 0.6.

[0173] Functional nature weight: This is a specific weight assigned based on the "nature of the function". For example, the system can preset the function weight of "reducing the scope of the fault" to 1.2, the function weight of "verifying the diagnostic hypothesis" to 1.0, and the function weight of "promoting the progress of the diagnosis" to 0.8 (these values ​​are only examples and can be determined based on system design and expert experience).

[0174] By multiplying or weighting these three factors, the contribution of early operational information to a specific subsequent diagnostic step can be determined.

[0175] For example, suppose a UPS power system experiences an intermittent output voltage drop fault. Maintenance technician Zhang performed the following operations during the initial diagnosis:

[0176] Early Operation (A): The stability of the input mains voltage of the UPS power system was checked.

[0177] Information obtained: Slight and occasional momentary drops in mains voltage were detected, but all were within the allowable input range of the UPS power system.

[0178] Diagnostic scenario characteristics: The urgency of the fault is moderate (intermittent drops affect the load), the complexity of the scenario is moderate (multiple possible causes), and the deviation from known fault modes is low (instantaneous drops in mains power are a common phenomenon).

[0179] Adjusted initial weights: Assume that, based on the current context, the system assigns an adjusted initial weight of 0.7 to directly related paths (such as the relationship between input voltage and output voltage).

[0180] Subsequent diagnostic steps (B): Xiao Zhang then checked the switching log of the inverter module inside the UPS power system.

[0181] Now, we determine the "strength and nature of the effect" of the information from the early procedure A in the subsequent diagnostic step B, and calculate the contribution value:

[0182] Nature of the identification function: The information obtained in the early operation A, that "the mains voltage is within the allowable range," mainly serves to narrow down the fault range in the subsequent diagnostic step B. It eliminates the possibility that "severe and persistent instability of the mains voltage is causing the output drop," thus shifting the diagnostic focus more towards the inverter module or battery module inside the UPS power system.

[0183] Determining the strength of the effect: Suppose that when Xiao Zhang begins diagnosis, there are five main categories of potential causes for the output voltage drop (e.g., severe mains instability, inverter failure, battery failure, control board failure, instantaneous load overload). Early operation A eliminates the category of "severe mains instability". This reduces the number of potential fault categories from five to four, narrowing the fault range by 20%. We can quantify this strength as 0.2.

[0184] The weight of the effect: Assume that the system defaults to a weight of 1.2 for the effect of "reducing the scope of the fault".

[0185] Calculate contribution value:

[0186] Contribution value = Adjusted initial weight × Intensity factor × Nature weight

[0187] Contribution value = 0.7 × 0.2 × 1.2 = 0.168

[0188] Therefore, the information obtained in the early operation A contributes 0.168 to narrowing the scope of the fault in the subsequent diagnostic step B. This contribution will be used to accumulate the total value of information provided by maintenance personnel throughout the entire diagnostic chain, and will ultimately affect their performance evaluation.

[0189] In practical applications, continuously tracking the propagation and impact of early operational information throughout the diagnostic chain means that the system records the number and manner in which this information is cited, verified, or refuted in subsequent diagnostic processes. When early operational information is cited, verified, or refuted multiple times in subsequent diagnostic steps, it indicates that the information has sustained importance or controversy. Based on the actual impact of early operational information at different diagnostic stages—for example, its repeated mention at key decision points or its multiple corrections to the diagnostic direction—the contribution value of early operational information in different subsequent diagnostic steps can be accumulated. The purpose is to reflect the cumulative value and influence of information over time.

[0190] Furthermore, based on the mentions, evaluations, or applications of early operational information in the text descriptions or voice recordings entered by maintenance personnel during subsequent diagnostic processes, additional exploratory contributions can be assigned to the accumulated contribution value of the early operational information, resulting in a modified contribution value. This contribution aims to reward maintenance personnel for their in-depth thinking, innovative applications, or unique insights into early information, even if the system did not fully recognize its value in the initial stage. For example, if maintenance personnel explicitly state in the diagnostic log that "although the voltage data checked in the early stages seemed normal at the time, combined with subsequent temperature anomalies, it indicated a potential problem with the power module," then this early operational information will receive an additional exploratory contribution.

[0191] Finally, considering the complexity and risk level of the diagnostic steps themselves, as well as their criticality to the final fault resolution, the modified contribution values ​​are weighted differentially to determine the degree to which the information obtained from early operations contributes to each subsequent diagnostic step. The complexity of a diagnostic step refers to the technical difficulty and resources required; the risk level refers to the potential negative impact of improper operation of that step; and criticality refers to the decisive role of that step in ultimately resolving the fault. By weighting these factors differentially, the ultimate value of early operational information throughout the entire diagnostic process can be assessed more comprehensively and accurately.

[0192] The proposed solution effectively addresses the static and one-sidedness issues that may exist in existing methods when assessing the contribution of early operational information by introducing dynamic adjustments to initial weights based on diagnostic context features, refined identification of information effects, continuous tracking of information dissemination and impact, exploratory contributions from subjective feedback from operations and maintenance personnel, and differentiated weighting of diagnostic step characteristics.

[0193] In some preferred embodiments, suppose a UPS power system suddenly alarms at night, indicating abnormal fluctuations in battery voltage, but the system does not immediately shut down. Upon receiving the alarm, maintenance personnel A first performs a seemingly routine "quick battery health check" and records the voltage, current, and temperature data at that time. Initial analysis shows the data is within the normal fluctuation range, and no obvious anomalies are immediately detected. However, as time progresses, the system load suddenly increases, the battery voltage fluctuations intensify, and a new "battery overload risk" alarm is triggered, significantly increasing the urgency and complexity of the situation.

[0194] At this point, the proposed solution dynamically adjusts the initial weights of associated paths, indirect associated paths, and heuristic associated paths based on the urgency and complexity of the current situation, thereby giving higher attention to diagnostic information related to the battery pack status. The system retrospectively identifies information obtained from the "quick battery pack health check" performed earlier by maintenance personnel A. Although this information did not directly point to a fault in the initial stage, it was repeatedly cited in subsequent "battery pack overload risk" scenarios to compare changes in battery status before and after the load increase, helping maintenance personnel A rule out the assumption that the battery pack itself had serious physical damage, thus focusing the diagnostic direction on anomalies in load management or charging modules.

[0195] Furthermore, during the subsequent diagnostic process, maintenance personnel A mentioned in a voice recording: "Fortunately, I checked the detailed data of the battery pack early on. Although I didn't see the problem at the time, the voltage fluctuation trend now matches the slight abnormal trend before the overload alarm, which allowed me to rule out a battery failure and instead check the charging module." The system assigns an additional exploratory contribution to the cumulative contribution value of the information obtained from the early operations based on maintenance personnel A's text description or voice recording. Ultimately, considering the high risk level of the "battery pack overload risk" diagnostic step (which may lead to system downtime) and its criticality to the final fault resolution, the system differentiates the weighted contribution value of the modified contribution. Thus, maintenance personnel A's seemingly insignificant early operations, due to their optimization of the diagnostic path and their role in driving key decisions in a dynamic context, receive a higher contribution assessment, thus more accurately reflecting maintenance personnel A's actual operational capabilities and value.

[0196] This application further proposes steps for differentially weighting the modified contribution values, including:

[0197] Adjust the complexity assessment factors of the diagnostic steps according to the rate of change of the situation;

[0198] Adjust the risk level assessment factors for the diagnostic steps based on the range of operating parameters involved in the scenario;

[0199] Adjust the key assessment factors of the diagnostic steps based on the degree of deviation between the situation and the known failure mode;

[0200] Based on the adjusted complexity assessment factor, risk level assessment factor, and key assessment factor, differentiated weighting is assigned to the modified contribution value.

[0201] Specifically, the rate of change of the situation refers to the rate at which the operating state or fault mode of an uninterruptible power supply (UPS) system evolves. For example, the fluctuation frequency, amplitude, and trend rate of change of system parameters (such as voltage, current, and temperature) can all be used to quantify the rate of change of the situation. When the rate of change of the situation is rapid, it means that the diagnostic environment is more dynamic and uncertain. In this case, the complexity assessment factor of the diagnostic steps will be adjusted accordingly to reflect the increased difficulty of diagnosis under rapidly changing situations.

[0202] The scope of operating parameters involved in the scenario refers to the number and types of uninterruptible power supply (UPS) system operating parameters that are affected or require attention in the current diagnostic scenario. For example, if a fault affects multiple key operating parameters (such as battery pack voltage, inverter output frequency, load power, etc.), the scope of operating parameters involved in the scenario is considered broad. The broader the scope of operating parameters involved in the scenario, the greater the impact of the fault and the higher the potential risk. In this case, the risk level assessment factor for the diagnostic steps will be adjusted accordingly to reflect the systemic risks that may be faced during the diagnostic process.

[0203] In practical applications, the degree of deviation between the current operating scenario and known fault modes refers to the degree of match between the current operating scenario of the uninterruptible power supply system and preset or historical fault modes. For example, machine learning can be used to compare the characteristics of the current scenario with a library of known fault modes to calculate their similarity or distance. The greater the deviation, the more novel the current fault scenario is and the more difficult it is to resolve using conventional experience or preset solutions. In this case, the critical evaluation factors of the diagnostic steps will be adjusted accordingly to emphasize the unique value of information obtained from early operations in unknown or complex scenarios and its crucial role in breakthrough diagnosis.

[0204] Therefore, after the aforementioned assessment factors (complexity assessment factor, risk level assessment factor, and criticality assessment factor) are dynamically adjusted according to real-time context characteristics, these adjusted factors will be comprehensively applied to assign differentiated weights to the modified contribution values. This means that in diagnostic scenarios that are complex, high-risk, and highly critical, the key information obtained by operations and maintenance personnel in the early stages will receive higher weights, thus more accurately reflecting their actual contribution to problem-solving.

[0205] Specifically, when the uninterruptible power supply (UPS) system's situation changes rapidly, the system recognizes the increased instantaneous complexity of the diagnostic environment and accordingly increases the complexity assessment factor for diagnostic steps. This ensures that the information value of each operation by maintenance personnel in rapidly changing situations, especially those that stabilize the situation or provide crucial clues, is more fully recognized. Simultaneously, when the range of operating parameters involved in the situation expands, indicating a wider impact and higher potential risk, the system increases the risk level assessment factor for diagnostic steps, giving greater weight to effective information obtained in risky situations. Furthermore, when the situation deviates significantly from known fault modes, it signifies novelty and difficulty in resolving the fault through conventional means; the system increases the criticality assessment factor for diagnostic steps, highlighting the breakthrough value of information provided by maintenance personnel in early operations exploring unknown fault modes. In this way, this application ensures that the assessment of the information value brought by each operation by maintenance personnel is more closely aligned with the actual diagnostic situation, avoiding assessment bias caused by situational changes.

[0206] In some preferred embodiments, it is assumed that a UPS power system suddenly experiences drastic fluctuations in multiple critical parameters (such as output voltage, battery discharge current, and inverter temperature) during operation. In this situation, the system recognizes that the situation is changing extremely rapidly and involves a wide range of operating parameters. Simultaneously, by comparing with a historical fault mode database, it finds that the current parameter combination deviates significantly from any known fault mode, indicating a possible new or complex fault. Based on these situational characteristics, the system dynamically increases the complexity assessment factor, risk level assessment factor, and criticality assessment factor for the current diagnostic step. For example, the complexity assessment factor might be adjusted from 0.5 to 0.8, the risk level assessment factor from 0.6 to 0.9, and the criticality assessment factor from 0.7 to 0.95. In such highly complex, high-risk, and highly critical situations, early actions by maintenance personnel, such as accurately measuring an abnormal parameter and promptly isolating a module, even if the operation itself seems simple, will have a significantly higher weighted contribution value due to the crucial diagnostic clues provided in extreme situations, based on these adjusted high assessment factors. This allows the contributions of operations and maintenance personnel in dealing with such urgent and complex failures to be more accurately and fully recognized.

[0207] The steps for adjusting the complexity assessment factors of the diagnostic process based on the rate of change of the situation include:

[0208] Real-time acquisition of the rate of change of the situation, as well as the fluctuation range and frequency of multiple operating parameters related to the rate of change of the situation;

[0209] Based on the rate of change of the situation, and the fluctuation range and frequency of multiple operating parameters related to the rate of change of the situation, the instantaneous complexity of the current situation is dynamically identified.

[0210] Adjust the complexity assessment factor of the diagnostic steps based on the instantaneous complexity of the current situation and the characteristics of the current diagnostic steps.

[0211] Specifically, the rate of change of the real-time environment refers to the dynamic rate of change of the uninterruptible power supply (UPS) system's operating environment, continuously monitored by the system. This includes, for example, acquiring the instantaneous change rate of key operating parameters such as temperature, humidity, voltage, and current through sensor data, system logs, or external environmental monitoring equipment. Simultaneously, the fluctuation range and frequency of multiple operating parameters related to the rate of change of the environment refer to the difference between the maximum and minimum values ​​of these parameters within a specific time window, as well as the number of times the parameter values ​​change per unit time. These data collectively depict the dynamics and instability of the current system operating environment.

[0212] Furthermore, dynamically identifying the instantaneous complexity of the current situation refers to the system using a preset algorithm to instantly assess the current operating situation of the uninterruptible power supply system based on real-time acquired information about the rate of change of the situation, the fluctuation range and frequency of operating parameters, in order to quantify its complexity. For example, when the rate of change of the situation is fast and the fluctuation range and frequency of operating parameters are large, it indicates that the complexity of the situation is high; conversely, the complexity of the situation is low.

[0213] Specifically, dynamically identifying the instantaneous complexity of the current situation refers to the system using a preset algorithm to instantly assess the current operating situation of the uninterruptible power supply system based on real-time acquired data such as the rate of change of the situation, the fluctuation range and frequency of operating parameters, in order to quantify its complexity. The "preset algorithm" here typically refers to an algorithm capable of processing time-series data, performing pattern recognition, or statistical analysis; its specific selection and application will vary depending on actual needs and system capabilities.

[0214] Specifically, "preset algorithms" can include the following types:

[0215] Rule engine based on statistical analysis and thresholds:

[0216] Algorithm Description: This type of algorithm establishes a baseline and fluctuation range for normal operation by statistically analyzing historical data of key operating parameters (such as voltage, current, temperature, frequency, etc.). Then, it calculates in real time the instantaneous rate of change of the current parameter (situational change rate), the fluctuation amplitude within a specific time window (fluctuation range of the operating parameter, such as standard deviation or range), and the frequency of exceeding a preset threshold (fluctuation frequency).

[0217] Usage: The system defines a series of thresholds and rules for each key parameter. For example, it can set "normal," "moderate fluctuation," and "violent fluctuation" ranges for voltage change rate; "stable," "slightly abnormal," and "severely abnormal" ranges for temperature fluctuation range; and set frequency thresholds for parameter exceeding limits. When the real-time monitored data characteristics (situation change rate, fluctuation range, fluctuation frequency) meet specific rule combinations, the system will output a corresponding complexity score or level. For example, if multiple key parameters simultaneously exhibit violent fluctuations and a high frequency of exceeding limits, the situation complexity will be judged as "high."

[0218] Pattern recognition or machine learning algorithms:

[0219] Algorithm Description: These algorithms build models by learning feature patterns from historical data of varying complexity. For example, a large amount of data on the rate of change, fluctuation range, and frequency of operating parameters of an uninterruptible power supply (UPS) system under different operating conditions (such as normal, minor faults, complex faults, and unknown disturbances) can be collected, and experts can label the complexity of these conditions. Commonly used algorithms include classification algorithms such as support vector machines, decision trees, random forests, or simple neural networks.

[0220] Application Method: During the model training phase, historical context data is used as input, and corresponding complexity labels are used as output to train a model capable of recognizing context complexity. In the real-time evaluation phase, the system extracts features of the current context (context change rate, range and frequency of parameter fluctuations) in real time and inputs these features into the trained model. Based on the learned patterns, the model outputs the instantaneous complexity of the current context, such as a complexity score between 0 and 1, or a direct classification into complexity levels like "low," "medium," and "high."

[0221] Fuzzy logic system:

[0222] Algorithm Description: Fuzzy logic systems can handle imprecise or fuzzy input information by defining fuzzy sets and fuzzy rules to map multiple input variables to a single output variable. In assessing situation complexity, the rate of change, fluctuation range, and fluctuation frequency of the situation may themselves be fuzzy concepts (e.g., "fast," "large," "high").

[0223] Application method: The system defines a fuzzy set (e.g., "slow", "medium", "fast") for each input feature (situation change speed, fluctuation range, fluctuation frequency) and establishes a series of fuzzy rules (e.g., "if the situation changes quickly and the fluctuation range is large, the complexity is high"). The real-time acquired numerical data is fuzzified, then calculated by the fuzzy inference engine in combination with the fuzzy rules, and finally defuzzified to obtain an accurate instantaneous complexity score.

[0224] The specific process of using a preset algorithm for real-time evaluation is as follows:

[0225] Real-time data acquisition: The system continuously acquires real-time data on various operating parameters from the uninterruptible power supply system, including voltage, current, frequency, temperature, battery status, etc.

[0226] Feature extraction and computation:

[0227] Rate of change of situation: Calculates the rate of change of key parameters within a very short time window. For example, the change in voltage per second.

[0228] Fluctuation range of operating parameters: Calculate the difference between the maximum and minimum values ​​of key parameters, or their standard deviation, within a sliding time window (e.g., the past 5 minutes).

[0229] Fluctuation frequency: The number of times a key parameter exceeds its normal threshold within a sliding time window (e.g., the past 10 minutes).

[0230] Algorithm execution and complexity quantification: The features extracted above are used as input to the preset algorithm.

[0231] If a rule engine based on statistical analysis and thresholds is used, the system will directly calculate and output the instantaneous complexity according to predefined logical rules (e.g., if the rate of voltage change exceeds X and the temperature fluctuation range exceeds Y, then the complexity is Z).

[0232] If pattern recognition or machine learning algorithms are used, the system will input the extracted features into the trained model, and the model will output a prediction complexity score or level based on its internal logic.

[0233] If a fuzzy logic system is used, the system will fuzzify the features, obtain a fuzzy level of complexity through fuzzy reasoning, and then defuzzify to obtain a precise complexity score.

[0234] Output and Feedback: The instantaneous complexity of the algorithm output (e.g., a score from 0 to 100, or a "low", "medium", "high" level) will be used as the basis for adjusting the background of the assessment of the operational capabilities of maintenance personnel.

[0235] For example:

[0236] Suppose a UPS power system needs to evaluate the instantaneous complexity of its battery modules. The system pre-defines an algorithm based on statistical analysis and rules.

[0237] Real-time data acquisition: The system collects battery voltage, battery current and battery temperature data every second.

[0238] Feature extraction:

[0239] Battery voltage change rate: Calculate the average rate of change of battery voltage over the past 10 seconds.

[0240] Battery temperature fluctuation range: Calculate the standard deviation of battery temperature over the past 5 minutes.

[0241] Battery current fluctuation frequency: Counts the number of times the battery current exceeds the normal operating range (e.g., excessive charging or discharging current) in the past minute.

[0242] Preset algorithm (rule engine):

[0243] Definition Rule 1 (Low Complexity): If the battery voltage change rate is less than 0.01V / s, the battery temperature standard deviation is less than 0.5°C, and the number of times the battery current exceeds the limit is 0, then the instantaneous complexity is "low".

[0244] Definition Rule 2 (Medium Complexity): If the rate of change of battery voltage is between 0.01V / s and 0.05V / s, or the standard deviation of battery temperature is between 0.5°C and 2°C, or the number of times battery current exceeds the limit is 1 to 2, then the instantaneous complexity is "Medium".

[0245] Definition Rule 3 (High Complexity): If the battery voltage change rate is higher than 0.05V / s, or the battery temperature standard deviation is higher than 2°C, or the battery current exceeds the limit more than twice, then the instantaneous complexity is "high".

[0246] Definition Rule 4 (Extremely High Complexity): If the battery voltage change rate is greater than 0.05V / s and the battery temperature standard deviation is greater than 2°C, then the instantaneous complexity is "extremely high".

[0247] Immediate assessment:

[0248] Scenario 1: The system monitors the battery voltage change rate in real time as 0.005V / s, the battery temperature standard deviation as 0.3°C, and the number of times the battery current exceeds the limit as 0. According to Rule 1, the system immediately assesses the instantaneous complexity of the current situation as "low".

[0249] Scenario 2: The system monitors in real time that the battery voltage change rate is 0.03V / s, the battery temperature standard deviation is 0.8°C, and the number of times the battery current exceeds the limit is 1. According to rule 2, the system immediately assesses the instantaneous complexity of the current situation as "medium".

[0250] Scenario 3: The system monitors in real time that the battery voltage change rate is 0.06V / s, the battery temperature standard deviation is 2.5°C, and the number of times the battery current exceeds the limit is 3. According to rule 4, the system immediately assesses the instantaneous complexity of the current situation as "extremely high".

[0251] In this way, the system can dynamically and quantitatively identify the instantaneous complexity of the current situation based on real-time changing operational data, providing accurate background information for subsequent assessment of the operational capabilities of maintenance personnel and adjustment of management strategies.

[0252] Therefore, adjusting the complexity assessment factor of a diagnostic step based on its instantaneous complexity and the characteristics of the current diagnostic step means that after identifying the instantaneous complexity of the current situation, the system dynamically adjusts the factors used to assess the complexity of the diagnostic step based on its specific nature (e.g., preliminary investigation, in-depth analysis, or fault location). For example, for an in-depth analysis step performed in a highly complex situation, its complexity assessment factor will be increased to more accurately reflect the actual difficulty and resources required for that step.

[0253] This application's solution comprehensively and dynamically captures the instantaneous state of the uninterruptible power supply (UPS) system's operating environment by acquiring the rate of change of the situation and the fluctuation range and frequency of relevant operating parameters in real time. Based on this real-time data, the system can dynamically identify the instantaneous complexity of the current situation, thereby avoiding biases that may result from using static or preset complexity assessment standards. Furthermore, by combining the characteristics of the current diagnostic steps, the complexity assessment factors for the diagnostic steps are adjusted, making the complexity assessment more accurate and personalized, ensuring that the evaluation of the operational contributions of maintenance personnel can fully consider the dynamic challenges of the actual working environment.

[0254] refer to Figure 2 This application proposes a UPS power user management system, applied to the above-mentioned UPS power user management method, the system comprising:

[0255] The acquisition module is used to acquire information about the operation and maintenance personnel's actions on the uninterruptible power supply system.

[0256] The analysis module is used to analyze operational behavior based on operational behavior information to obtain an assessment of the operational capabilities of maintenance personnel.

[0257] The adjustment module is used to adjust the way information is displayed to operations and maintenance personnel, adjust the level of detail in the operation instructions, and provide resources to enhance the capabilities of operations and maintenance personnel based on the assessment of their operational capabilities.

[0258] Specifically, the acquisition module can be understood as the data collection unit in the system, aiming to collect all relevant behavioral data of maintenance personnel when operating the uninterruptible power supply (UPS) system in real time and accurately. This behavioral information may include, but is not limited to: login / logout time, executed commands, accessed interfaces, modified parameters, fault diagnosis paths, operation time, and operation results. In practical applications, the acquisition module can be implemented through various technical means, such as embedding logging functions in the UPS system or its management interface, monitoring network traffic, analyzing screenshots or screen recordings of the operation interface, or interacting with the maintenance personnel's terminal. Its purpose is to provide a comprehensive and reliable data foundation for subsequent operational behavior analysis.

[0259] The analysis module can be understood as the intelligent processing unit in the system. Its purpose is to deeply mine and intelligently analyze the massive amounts of operational behavior information collected by the acquisition module, thereby quantitatively evaluating the operational capabilities of maintenance personnel. Specifically, the analysis module can employ technologies such as machine learning algorithms, expert system rules, and behavioral pattern recognition to comprehensively evaluate maintenance personnel across multiple dimensions, including their operational sequences, operational efficiency, fault diagnosis accuracy, and problem-solving abilities. For example, it can compare the actual operational paths of maintenance personnel with preset best practice paths, or analyze their response speed and decision-making quality in specific fault scenarios to obtain an evaluation result of their operational capabilities. This evaluation result can be a comprehensive score or a detailed evaluation for different capability dimensions.

[0260] Furthermore, the adjustment module can be understood as an intelligent feedback and intervention unit within the system. Its purpose is to dynamically adjust the information display method and the level of detail in operational guidance for operations and maintenance personnel based on the operational capability assessment results obtained from the analysis module, and to provide personalized capability enhancement resources. Specifically, if the assessment results indicate that the operations and maintenance personnel have weak capabilities or insufficient experience, the adjustment module can increase the level of detail in the information display and provide more specific and intuitive operational guidance, such as through pop-up prompts, step-by-step animated instructions, or voice prompts. Conversely, if the operations and maintenance personnel have strong capabilities, the information display can be simplified, reducing unnecessary guidance to improve their operational efficiency. In addition, the adjustment module can also intelligently recommend relevant training courses, technical documents, case studies, or simulation exercises based on the assessment results to help operations and maintenance personnel address their weaknesses and improve their skills.

[0261] The UPS power user management system of this application, through its modular design, effectively solves the problems of inefficiency, untimely data processing, and delayed system response that may exist in the actual implementation of traditional methods. Specifically, the acquisition module, as the data entry point, can collect the operational behavior information of maintenance personnel in real time and comprehensively, ensuring the timeliness and completeness of the data foundation for subsequent analysis and avoiding errors and delays caused by manual recording or delayed data collection. It is precisely because of the real-time data stream of the acquisition module that the analysis module can make dynamic evaluations based on the latest operational behavior information, thereby overcoming the drawback of evaluation results that may lose timeliness due to outdated data in traditional methods. Through intelligent analysis of this real-time data, the analysis module can quickly and accurately quantify the operational capabilities of maintenance personnel, providing a scientific basis for subsequent personalized adjustments and avoiding evaluation biases caused by subjective judgment or empiricism. On this basis, the adjustment module can adjust the information display method and the level of detail of the operation instructions for maintenance personnel in real time according to the evaluation results of the analysis module, and provide targeted capability enhancement resources. This instant feedback and personalized intervention mechanism enables operations and maintenance personnel to receive support that matches their current skill level, thereby effectively avoiding operational errors or inefficiencies caused by information overload or insufficient information, and thus improving the overall efficiency and effectiveness of operations and maintenance management.

[0262] Through the above technical solutions, the UPS power user management system of this application can achieve automated and intelligent management of the operation behavior of maintenance personnel, significantly improving the efficiency and accuracy of UPS power user management. This systematic management approach not only optimizes the work experience of maintenance personnel but also promotes the continuous improvement of their skills, ultimately ensuring the stable operation and efficient maintenance of the uninterruptible power supply system.

[0263] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A UPS power supply user management method, characterized in that, The method includes the following steps: To obtain information on the operational behavior of maintenance personnel on the uninterruptible power supply system; Based on operational behavior information, analyze the operational behavior to obtain an assessment of the operational capabilities of maintenance personnel; Based on the assessment of the operational capabilities of operations and maintenance personnel, adjust the way information is displayed to them, adjust the level of detail in the operational instructions, and provide resources to enhance their capabilities. The steps involved in analyzing operational behavior to assess the operational capabilities of maintenance personnel include: Obtain the correlation characteristics of the operating parameters of the uninterruptible power supply system. The correlation characteristics represent a quantitative description of the interaction between the operating parameters. Monitor the evolution trajectory of associated features; Identify deviations from the evolution trajectory and trigger contextual change events; Based on the triggered context change events, the evaluation background of the operation and maintenance personnel's operation behavior is adjusted, the current context is marked as an unknown complex disturbance, and the direct comparison between the efficiency of the operation and maintenance personnel's diagnostic path and the preset single fault template is suspended, and multiple diagnostic direction suggestions are dynamically generated. Based on multiple diagnostic direction suggestions, a list of diagnostic direction suggestions is generated. The priority of each diagnostic direction is adjusted according to each operation and maintenance personnel's operation and maintenance personnel, and an evidence weight is assigned to each operation. Based on the adjusted priority of each diagnostic direction, the evidence weight corresponding to each operation, and the accumulated evidence of the operation and maintenance personnel's operation, a diagnostic path is constructed. Quantify the information value generated by each operation and maintenance personnel's actions; The evaluation weights of the performance indicators of operations and maintenance personnel are adjusted based on the diagnostic path and the information value brought by each operation. Furthermore, the performance scores of operations and maintenance personnel are dynamically adjusted based on their contribution to path optimization for each operation.

2. The UPS power user management method as described in claim 1, characterized in that, Based on operational behavior information, the steps for analyzing operational behavior to assess the operational capabilities of maintenance personnel include: Identify the operating conditions of uninterruptible power supply systems. These conditions include the rate of change of the conditions, the range of operating parameters involved, and the degree of deviation of the conditions from known fault modes. Based on the characteristics of the operating environment, the evaluation rules used to quantify the information value brought by each operation by the operations and maintenance personnel are adjusted. The evaluation rules are used to determine the contribution of the operations and maintenance personnel's operations to the current diagnostic path. Based on the real-time feedback from operations and maintenance personnel performing operations in the current context, the quantitative standards corresponding to the information value brought by each operation are adjusted. The real-time feedback includes the success status of the operation, whether it leads to new data discovery, and the role of new data discovery in promoting the current diagnostic direction. Based on the revised evaluation rules and quantitative standards, each operation by operations and maintenance personnel is assigned information value. The evaluation weight of operations and maintenance personnel's performance indicators is adjusted based on the accumulation of information value assigned to each operation. Furthermore, the performance score of operations and maintenance personnel is dynamically adjusted based on their contribution to path optimization for each operation.

3. The UPS power user management method as described in claim 1, characterized in that, Based on operational behavior information, the steps for analyzing operational behavior to assess the operational capabilities of maintenance personnel include: It records every operation and maintenance personnel perform in real time and the direct results of each operation. The direct results include the operation type, the operation object, the system feedback data, and the operation timestamp. Based on each operation performed by maintenance personnel and the direct results of each operation, and combined with the operational context characteristics of the uninterruptible power supply system, each operation is initially assigned an information value. The operational context characteristics include the rate of change of the context, the range of operational parameters involved in the context, and the degree of deviation of the context from known fault modes. After initially assigning informational value, we continuously track the subsequent operation sequence of maintenance personnel and monitor the evolution of uninterruptible power supply system operating parameters; When subsequent operation sequences by maintenance personnel or the evolution of uninterruptible power supply system operating parameters reveal new diagnostic clues or lead to revisions of early diagnostic assumptions, the information value assigned to the early operations should be retrospectively adjusted. Adjustments may include: increasing the information value assigned to the early operations if the information obtained from the early operations proves to be critical in subsequent diagnoses; and decreasing the information value assigned to the early operations if it proves to be secondary or misleading. Based on the adjusted information value, the total information value of operations and maintenance personnel throughout the entire diagnostic chain is re-accumulated; Based on the sum of the re-accumulated information value, as well as the situational complexity factor and exploration score, the evaluation weight of the performance indicators of operation and maintenance personnel is dynamically adjusted, and the performance score of operation and maintenance personnel is dynamically adjusted based on the contribution of each operation of operation and maintenance personnel to path optimization.

4. The UPS power user management method as described in claim 3, characterized in that, Before retrospectively adjusting the information value assigned to earlier actions, the following steps are also included: When the subsequent operation sequence of maintenance personnel or the evolution of uninterruptible power supply system operating parameters reveals new diagnostic clues or leads to the revision of the assumptions of early diagnosis, identify the correlation path between the information obtained from the early operation and the subsequent diagnostic steps. The correlation path includes the diagnostic links where the information is cited, verified or refuted. Based on the associated path, the contribution of information obtained in the early operations to each subsequent diagnostic step is calculated. The contribution is determined by evaluating the role of the information in narrowing the scope of the fault, verifying diagnostic hypotheses, or promoting diagnostic progress in a specific diagnostic stage. Based on the degree of contribution, the information value of the early actions is allocated to each subsequent diagnostic step affected by the early actions. After allocation, the information value assigned to the early actions is retrospectively adjusted based on the degree of contribution.

5. A UPS power user management method as described in claim 4, characterized in that, The steps for calculating the contribution of information acquired in earlier operations to each subsequent diagnostic step include: Based on the diagnostic tools selected by operations and maintenance personnel in subsequent operations, the data sources consulted, and the changes in the operation sequence of operations and maintenance personnel, the indirect impact of early operation information on the subsequent diagnostic behavior of operations and maintenance personnel is identified, thereby constructing indirect correlation paths; Based on the text descriptions or voice recordings entered by maintenance personnel during subsequent diagnosis, extract the mentions, evaluations, or applications of information obtained from earlier operations to construct heuristic association paths; Based on the correlation path, indirect correlation path, and heuristic correlation path, calculate the contribution of information obtained from early operations to each subsequent diagnostic step.

6. The UPS power user management method as described in claim 5, characterized in that, The steps for calculating the contribution of information acquired in earlier operations to each subsequent diagnostic step include: Based on the diagnostic scenario characteristics of the current uninterruptible power supply system, adjust the initial weights of associated paths, indirect associated paths, and heuristic associated paths. The diagnostic scenario characteristics include the urgency and complexity of the scenario, as well as the degree of deviation of the scenario from known fault modes. Identify the role of early operational information in narrowing the fault scope, verifying diagnostic hypotheses, or promoting diagnostic progress in each subsequent diagnostic step. Calculate the contribution value of early operational information in the corresponding subsequent diagnostic step based on the adjusted initial weights and the strength and nature of the role. Continuously track the spread and impact of early operational information in the diagnostic chain. When early operational information is cited, verified or refuted multiple times in subsequent diagnostic steps, accumulate the contribution value of early operational information in different subsequent diagnostic steps based on the actual impact of early operational information at different diagnostic stages. Based on the mentions, evaluations, or applications of early operation information in the text descriptions or voice recordings entered by maintenance personnel during subsequent diagnostics, additional exploratory contributions are assigned to the accumulated contribution value of the early operation information to form a modified contribution value. Taking into account the complexity and risk level of the diagnostic steps themselves, as well as their criticality to the final fault resolution, the modified contribution values ​​are weighted differently to determine the degree of contribution of the information obtained in the early operations to each subsequent diagnostic step.

7. A UPS power user management method as described in claim 6, characterized in that, The steps for applying differential weighting to the modified contribution values ​​include: Adjust the complexity assessment factors of the diagnostic steps according to the rate of change of the situation; Adjust the risk level assessment factors for the diagnostic steps based on the range of operating parameters involved in the scenario; Adjust the key assessment factors of the diagnostic steps based on the degree of deviation between the situation and the known failure mode; Based on the adjusted complexity assessment factor, risk level assessment factor, and key assessment factor, differentiated weighting is assigned to the modified contribution value.

8. A UPS power user management method as described in claim 7, characterized in that, The steps for adjusting the complexity assessment factors of the diagnostic process based on the rate of change of the situation include: Real-time acquisition of the rate of change of the situation, as well as the fluctuation range and frequency of multiple operating parameters related to the rate of change of the situation; Based on the rate of change of the situation, and the fluctuation range and frequency of multiple operating parameters related to the rate of change of the situation, the instantaneous complexity of the current situation is dynamically identified. Adjust the complexity assessment factor of the diagnostic steps based on the instantaneous complexity of the current situation and the characteristics of the current diagnostic steps.

9. A UPS power user management system, applied to the UPS power user management method as described in claim 1, characterized in that, The system includes: The acquisition module acquires information about the operational behavior of maintenance personnel on the uninterruptible power supply system. The analysis module analyzes operational behavior information to obtain an assessment of the operational capabilities of maintenance personnel. The adjustment module adjusts the way information is displayed to operations and maintenance personnel, the level of detail in the operation instructions, and provides resources to enhance their capabilities, based on an assessment of their operational skills.

Images