Digital intelligent integrated platform terminal display operation and maintenance decision-making method and system

Abstract

The invention discloses a digital intelligent integrated platform terminal display operation and maintenance decision-making method and system, and belongs to the field of GIS equipment partial discharge data monitoring and processing, and the method comprises the steps: S100, collecting partial discharge signals, environment data and equipment operation state data in real time; s200, identifying a discharge type through a discharge type identification model, and generating environment or equipment operation state correlation analysis through gradient back propagation and physical rule matching; s300, generating a feature contribution degree report based on SHAP + LIME, verifying a model decision based on a physical rule base, and dynamically adjusting the confidence coefficient; s400, matching an operation and maintenance strategy based on the knowledge graph; s500, displaying a natural language report of the 3D spectrogram, the thermodynamic diagram and the operation and maintenance strategy on a visual interface; and S600, updating model parameters through incremental learning. The operation and maintenance threshold can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of GIS partial discharge monitoring data processing, and in particular to a digital intelligent integrated platform terminal display operation and maintenance decision method and system. Background Art

[0002] Partial discharge is a pulse discharge that will produce a series of physical phenomena and chemical changes such as light, sound, electrical and mechanical vibration inside and around the power equipment. These various physical and chemical changes caused by partial discharge can provide detection signals for monitoring the insulation status inside the power equipment. When insulation defects occur inside high-voltage electrical equipment, partial discharge signals will be generated. By detecting and analyzing partial discharge signals, it is possible to determine whether there are insulation hazards inside high-voltage electrical equipment and prevent further expansion of potential accidents.

[0003] Gas-insulated switchgear (GIS) is widely used in power systems. Its internal structure is complex, and it is difficult to repair after a failure, which can easily cause significant losses. The causes of GIS equipment failure are diverse, mainly insulation defects, accompanied by partial discharge. Analyzing the severity of the defect based on the partial discharge signal and estimating the defect development trend, conducting effective early warning, and formulating reasonable maintenance strategies are of great significance to ensure the safe and reliable operation of the equipment.

[0004] The existing partial discharge monitoring system has the following problems: the classification model has poor interpretability, and operation and maintenance personnel find it difficult to trust automated decisions; the maintenance strategy relies on a static rule base and cannot dynamically adapt to complex working conditions; and there is a lack of a model optimization mechanism driven by user feedback. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a digital intelligent integrated platform terminal display operation and maintenance decision-making method and system, which can lower the technical threshold, accelerate fault location and response; expose potential defects of the model, promote algorithm and data iteration; build a "monitoring-interpretation-decision-making" closed loop, and improve the reliability of the power grid.

[0006] On the one hand, an embodiment of the present invention provides a digital intelligent integrated platform terminal display operation and maintenance decision-making method, comprising the following steps: S100, collecting partial discharge signals in real time through ultra-high frequency sensors and ultrasonic sensors, and obtaining environmental data and equipment operation status data; the environmental data includes temperature and humidity; the equipment operation status data includes load rate, operation time and voltage; S200, inputting partial discharge signals into a CNN+Transfomer network to extract time-frequency domain features, inputting environmental data and equipment operation status data into a fully connected network to encode auxiliary features, and dynamically weighting time-frequency domain features and auxiliary features through an attention mechanism to obtain a fused feature vector; outputting a discharge type probability distribution based on the fused feature vector, and generating an environment or equipment operation probability distribution based on the auxiliary feature vector and the discharge type classification result through gradient back propagation and physical rule matching. State association analysis; S300, generate feature contribution report based on SHAP+LIME, obtain key features according to the feature contribution report, match physical rules of the physical rule base based on key features, verify model decisions according to physical rules, and determine dynamic adjustment of confidence or output environmental risk prompts based on the model decision verification results; S400, obtain discharge type, discharge type confidence, environmental data and equipment operation status data, and match operation and maintenance strategies based on knowledge graphs; S500, generate 3D spectrum graphs and heat maps based on partial discharge signals, generate natural language reports based on operation and maintenance strategies, and display 3D spectrum graphs, heat maps and natural language reports in a visual interface; S600, record user correction decisions and operation and maintenance strategy execution results, update model parameters through incremental learning, dynamically adjust knowledge graph node weights, and optimize physical rule base logic.

[0007] The embodiments of the present invention achieve at least the following beneficial effects: The embodiments of the present invention improve the adaptability to complex working conditions by adaptively adjusting the weights of environment / state data; explainability-driven strategy generation: environmental contribution quantification and knowledge graph linkage to ensure transparent and reliable decision-making; closed-loop feedback optimization: user ratings drive the knowledge graph and model parameters to update synchronously. SHAP+LIME is deeply integrated with domain rules to achieve a leap from "black box prediction" to "white box decision-making"; dynamic update and multimodal interaction of knowledge graphs improve operation and maintenance response speed and accuracy. Lower technical barriers and accelerate fault location and response; expose potential defects of the model to promote algorithm and data iteration; build a "monitoring-interpretation-decision-making" closed loop to improve power grid reliability.

[0008] According to some embodiments of the present invention, step S200 includes: S210, preprocessing the collected partial discharge signal, including filtering and noise reduction; and dynamically adjusting the signal gain according to temperature data.

[0009] According to some embodiments of the present invention, step S200 includes: S220, extracting the spectral spatial features of the partial discharge signal based on CNN, capturing the time domain sequence dependency based on Transformer, and obtaining the time-frequency domain features of the partial discharge signal; S230, encoding the environmental features and the equipment operation status features based on the fully connected network to obtain auxiliary features; S240, generating the partial discharge signal weight and the environmental / state weight based on the fully connected layer, weightedly fusing the time-frequency domain features and the auxiliary features of the partial discharge signal to obtain a fused feature vector; and outputting the discharge type probability distribution based on the fused feature vector.

[0010] According to some embodiments of the present invention, step S200 includes: S250, calculating the gradient of environmental characteristics or equipment operating status characteristics on classification results through back propagation, and quantifying the influence weight of environmental characteristics or equipment operating status characteristics; S260, comparing the quantified results with preset domain knowledge, verifying the rationality of the discharge type identification model, and obtaining the environment or equipment operating status association analysis results.

[0011] According to some embodiments of the present invention, step S300 includes: S310, performing feature contribution analysis on the discharge type classification results, obtaining a SHAP global interpretation by calculating the Shapley value of each feature of the partial discharge signal, and obtaining a LIME local interpretation by generating a single sample heat map and identifying key discriminant features.

[0012] According to some embodiments of the present invention, step S300 includes: S320, building a physical rule library; S330, when environmental data or equipment operating status data exceeds a preset threshold, triggering physical rule verification; determining whether environmental anomalies or equipment anomalies may cause feature distortion based on environmental data or equipment operating status data; if so, adjusting the confidence level and generating an environmental / equipment anomaly alarm; S340, when the confidence level of the discharge type classification result is lower than the set threshold, triggering physical rule verification; obtaining key features based on the feature contribution of the discharge type classification result, matching the physical rules in the physical rule library based on the key features, and determining whether the partial discharge features on which the discharge type identification model depends comply with the physical rules; when the discharge type identification model depends on conflicting features, triggering a rule conflict alarm.

[0013] According to some embodiments of the present invention, the S400 includes: S410, constructing a knowledge graph; S420, matching a preset operation and maintenance strategy according to an input discharge type; S430, dynamically adjusting the operation and maintenance strategy according to equipment operating status data; S440, calculating the risk level according to confidence, environmental data and equipment operating status data to obtain the operation and maintenance priority; S450, outputting an operation and maintenance text report according to the operation and maintenance strategy and operation and maintenance priority.

[0014] According to some embodiments of the present invention, S600 includes: S610, fine-tuning the classification model using the newly labeled data to optimize the attention weight distribution; S620, updating the knowledge graph node weights based on the operation and maintenance personnel's scoring of the operation and maintenance strategy.

[0015] According to some embodiments of the present invention, step S500 includes: S510, generating AR positioning guidance according to an operation and maintenance strategy.

[0016] Another aspect of the embodiment of the present invention provides a digital intelligent integrated platform terminal display operation and maintenance decision system, including: a data acquisition module, used to collect partial discharge signals in real time through ultra-high frequency sensors and ultrasonic sensors, and obtain environmental data and equipment operation status data; the environmental data includes temperature and humidity; the equipment operation status data includes load rate, operation time and voltage; a partial discharge identification module, used to input partial discharge signals into a CNN+Transfomer network to extract time-frequency domain features, input environmental data and equipment operation status data into a fully connected network to encode auxiliary features, and dynamically weight the time-frequency domain features and auxiliary features through an attention mechanism to obtain a fused feature vector; output a discharge type probability distribution based on the fused feature vector, and generate an environment or equipment operation status association distribution based on the auxiliary feature vector and the discharge type classification result through gradient back propagation and physical rule matching. Analysis; an interpretability module, which is used to generate a feature contribution report based on SHAP+LIME, obtain key features based on the feature contribution report, match the physical rules of the physical rule base based on the key features, verify the model decision based on the physical rules, and determine the dynamic adjustment of confidence or output of environmental risk prompts based on the model decision verification results; an operation and maintenance strategy module, which is used to obtain the discharge type, discharge type confidence, environmental data and equipment operation status data, and match the operation and maintenance strategy based on the knowledge graph; a visualization module, which is used to generate 3D spectrum graphs and heat maps based on partial discharge signals, and generate natural language reports based on the operation and maintenance strategy, and display the 3D spectrum graphs, heat maps and natural language reports in the visualization interface; a feedback optimization module, which is used to record user correction decisions and operation and maintenance strategy execution results, update model parameters through incremental learning, dynamically adjust the knowledge graph node weights, and optimize the physical rule base logic.

[0017] The embodiments of the present invention achieve at least the following beneficial effects: The embodiments of the present invention improve the adaptability to complex working conditions by adaptively adjusting the weights of environment / state data; explainability-driven strategy generation: environmental contribution quantification and knowledge graph linkage to ensure transparent and reliable decision-making; closed-loop feedback optimization: user ratings drive the knowledge graph and model parameters to update synchronously. SHAP+LIME is deeply integrated with domain rules to achieve a leap from "black box prediction" to "white box decision-making"; dynamic update and multimodal interaction of knowledge graphs improve operation and maintenance response speed and accuracy. Lower technical barriers and accelerate fault location and response; expose potential defects of the model to promote algorithm and data iteration; build a "monitoring-interpretation-decision-making" closed loop to improve power grid reliability.

[0018] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0020] Figure 1 A schematic diagram of a method according to an embodiment of the present invention;

[0021] Figure 2 Schematic diagram of the structure of the system according to the embodiment of the present invention.

[0022] Reference numerals:

[0023] Data acquisition module 100, partial discharge identification module 200, explainability module 300, operation and maintenance strategy module 400, visualization module 500, feedback optimization module 600. DETAILED DESCRIPTION

[0024] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0025] In the description of the present invention, "several" means one or more, "more" means two or more, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0026] Under normal circumstances, the insulation performance of power equipment operating in the power grid can withstand the operating voltage. Due to the influence of the manufacturing and installation process, there may be bubbles, impurities, cracks, etc. inside the insulation of electrical equipment. Under the action of high-voltage alternating electric field, the internal or surface electric field strength will be too high, and periodic partial discharge will occur inside the insulation. Since the discharge amount is very small, it will not cause the entire path to break down in a short time, but it can cause the insulation performance to decline, or even lose the withstand voltage performance. With the increasing number of high-voltage power equipment and the increasing voltage level, these problems accumulate and seriously threaten the safe operation of the power system. Partial discharge detection is an effective means to inspect the insulation condition of equipment and is also a hot spot for current applications. In partial discharge detection, commonly used methods include ultra-high frequency detection, ultrasound, high-frequency current and ground wave detection equipment. These devices have solved a large number of accident hazards for maintenance personnel.

[0027] The partial discharge signal is expressed as a single or continuous electrical pulse. The pulse waveform not only contains the discharge mechanism information of the defect, but also contains the defect severity information. Therefore, one of the main tasks of partial discharge detection is to identify the defect type through the partial discharge pulse waveform, so as to diagnose the severity of the defect and formulate reasonable equipment maintenance measures and strategies.

[0028] Ultra High Frequency (UHF) sensor: A sensor with an operating frequency range of 300MHz~3GHz, which has the characteristics of high frequency, wide bandwidth, and strong penetration. It is widely used in communications, industrial monitoring, medical care, the Internet of Things and other fields. The partial discharge process will produce broadband electromagnetic waves. The electrical breakdown process of different types of partial discharge is different, resulting in pulse currents of different amplitudes and steepness, and thus electromagnetic transients and electromagnetic waves of different frequency components. The sensor uses a Hillport fractal antenna, which is a non-frequency-variable antenna whose electrical performance is independent of frequency. It has the advantages of wide frequency, circular polarization, small size, high efficiency, and can be embedded. The amplifier uses a low-noise, high-gain (40db) ultra-high frequency signal.

[0029] Ultrasonic partial discharge sensor: Sound waves propagate mainly in the form of longitudinal waves in liquid and gas media. Longitudinal waves mainly transmit pressure by mutual collision between molecules whose vibration direction is parallel to the wave propagation direction. Sound waves propagate mainly in the form of transverse waves and longitudinal waves in solid media. When propagating in transverse waves, the vibration direction of the particles is perpendicular to the wave propagation direction. This requires sufficient gravitation between the particles so that the vibration of the particles can drive the neighboring particles to vibrate. Therefore, transverse waves only appear in liquids or solids with high concentrations. Transverse waves only appear when the longitudinal waves penetrate the gas or liquid medium and reach the metal shell and continue to propagate in the metal medium.

[0030] In order to lower the technical threshold, the digital intelligent integrated platform terminal display operation and maintenance decision method of the embodiment of the present invention assists technical personnel in making operation and maintenance decisions through interpretability-driven decision support. Interpretability-driven decision support is a technology that assists users in making decisions by revealing the internal decision logic of the machine learning model and converting complex model predictions into human-understandable basis. Its core lies in: Transparent model decision: Through visualization or numerical methods, the key features, data areas or logical paths that the model relies on during classification are displayed. Causal association analysis: Explain how the model associates input features with output categories, such as which signal features (such as spectrum peaks, phase distribution) play a decisive role in identifying "metal tip discharge". Trust building and risk control: Enhance user trust in the model through interpretability, and help discover potential deviations or errors in the model, such as the lack of positioning feature extraction when misjudging "air gap discharge" as "free particle discharge".

[0031] Reference Figure 1 The method of the embodiment of the present invention can realize a digital intelligent integrated platform terminal display operation and maintenance decision method, including the following steps:

[0032] S100, collect partial discharge signals in real time through UHF sensors and ultrasonic sensors, and obtain environmental data and equipment operation status data; the environmental data includes temperature and humidity; the equipment operation status data includes load rate, operation time and voltage.

[0033] The environmental data in this embodiment include temperature and humidity. The temperature is used to correct the signal. High temperature may cause sensor drift, and the signal gain needs to be adjusted dynamically. In addition, the discharge type correction is performed: the probability of air gap discharge increases in high temperature environments. Implementation means: Add a temperature compensation algorithm (such as a linear regression model) in the signal preprocessing stage; and input the temperature as a feature into the classification model to participate in the discharge type probability calculation.

[0034] This embodiment determines interference suppression and sensor protection through humidity. Interference suppression includes that high humidity is prone to cause surface discharge, and it is necessary to distinguish between environmental interference and real discharge; sensor protection includes: triggering an early warning when humidity exceeds the limit. Implementation means include building a humidity-interference association rule base (such as "when humidity>90%, shield low-frequency signals"); and dynamically adjusting the alarm threshold during operation and maintenance decision-making.

[0035] The equipment operating status of this embodiment includes load rate, operating time and voltage fluctuation. The load rate is used to evaluate the severity of discharge: a slight discharge under high load may escalate to a serious fault. The implementation means include combining the load rate with the discharge amplitude to calculate a composite risk index (such as risk = discharge amount × load rate); and triggering a graded alarm in language decision-making.

[0036] In this embodiment, the operating time is used for predictive maintenance: equipment aging may aggravate insulation defects. The "operating time-failure probability" relationship is associated in the knowledge graph to optimize the maintenance cycle suggestion.

[0037] The voltage fluctuation of this embodiment is used to identify transient interference: voltage mutation may cause false alarm of short-term discharge. The implementation means includes marking the voltage mutation time window in the pre-processing stage and filtering abnormal signals within this period.

[0038] S200, input the partial discharge signal into the CNN+Transformer network to extract time-frequency domain features, input the environmental data and equipment operation status data into the fully connected network to encode auxiliary features, and dynamically weight the time-frequency domain features and auxiliary features through the attention mechanism to obtain a fused feature vector; output the discharge type probability distribution based on the fused feature vector, and generate the environment or equipment operation status association analysis through gradient back propagation and physical rule matching based on the auxiliary feature vector and the discharge type classification result. This embodiment adaptively adjusts the weight of the environment / state features through the attention mechanism to improve the robustness under complex working conditions.

[0039] The discharge type recognition model of this embodiment includes: ultra-high frequency / ultrasonic partial discharge signal as the main input feature, environmental data (temperature, humidity) and equipment status data (load rate, voltage) as auxiliary feature vectors; two types of features are weighted and fused through a multimodal fusion network. The multimodal fusion network includes a signal processing branch, which extracts time-frequency domain features through CNN+Transformer; an environmental / equipment status data processing branch, which encodes auxiliary features through a fully connected network (FCN); and a fusion layer dynamically weights and fuses two types of features through an attention mechanism. Output discharge type probability distribution (such as "metal tip discharge: 89%), environmental correlation analysis (such as "high temperature causes a 5% decrease in confidence").

[0040] S300. Generate a feature contribution report based on SHAP+LIME, obtain key features based on the feature contribution report, match physical rules of the physical rule library based on the key features, verify the model decision based on the physical rules, and determine to dynamically adjust the confidence or output environmental risk warnings based on the model decision verification results.

[0041] S400, obtaining discharge type, discharge type confidence, environmental data and equipment operation status data, and matching operation and maintenance strategies based on the knowledge graph.

[0042] S500, generate a 3D spectrum diagram and a thermal map based on the partial discharge signal, generate a natural language report based on the operation and maintenance strategy, and display the 3D spectrum diagram, thermal map and natural language report on a visual interface.

[0043] S600, record the user's revised decision and operation and maintenance strategy execution results, update the model parameters through incremental learning, dynamically adjust the knowledge graph node weights, and optimize the physical rule base logic.

[0044] In some embodiments, step S200 includes: S210, preprocessing the collected partial discharge signal, including filtering and noise reduction; and dynamically adjusting the signal gain according to the temperature data.

[0045] In some embodiments, step S200 includes:

[0046] S220. Extract the spectral spatial features of the partial discharge signal based on CNN, and capture the time domain sequence dependency based on Transformer to obtain the time-frequency domain features of the partial discharge signal.

[0047] S230: Obtain auxiliary features based on the fully connected network coding environment features and the device operation status features.

[0048] S240, generating partial discharge signal weights and environment / state weights based on the fully connected layer, weightedly fusing the time-frequency domain features and auxiliary features of the partial discharge signal to obtain a fused feature vector; and outputting a discharge type probability distribution based on the fused feature vector.

[0049] In some embodiments, step S200 includes:

[0050] S250. Calculate the gradient of the environmental feature or the device operating status feature on the classification result through back propagation, and quantify the influence weight of the environmental feature or the device operating status feature.

[0051] S260, comparing the quantification results with the preset domain knowledge, verifying the rationality of the discharge type identification model, and obtaining the correlation analysis results of the environment or equipment operation status.

[0052] The working principle of the environment / device status data of this embodiment includes: first, the contribution is quantified, and the gradient of the environmental characteristics (such as temperature and humidity) on the classification results is calculated through back propagation to quantify the influence weight. For example: the temperature gradient value = +0.15, which means that the probability of classification as "air gap discharge" increases by 15% for every 1°C increase in temperature. The relative importance of environmental data in fusion is reflected by the attention weight. Then verify through physical rules: compare the quantified results with domain knowledge (such as "when humidity>90%, the probability of surface discharge increases") to verify the rationality of the model. If the model relies on features that contradict the rules (such as the high-frequency band weight is too high at low temperature), an alarm is triggered and manual review is recommended. Confidence adjustment: dynamically correct the classification confidence according to the environmental contribution. For example: the original confidence is 92%, and it decreases by 5% due to high temperature compensation, and the output is "87% (high temperature impact)". When the environmental risk (such as humidity>95%) exceeds the threshold, an additional "high risk of environmental interference" prompt is added.

[0053] In some embodiments, step S300 includes: S310, performing feature contribution analysis on the discharge type classification result, obtaining a SHAP global explanation by calculating the Shapley value of each feature of the partial discharge signal, and obtaining a LIME local explanation by generating a single sample heat map and identifying key discriminant features.

[0054] The SHAP+LIME collaborative interpretation mechanism of this embodiment includes: feature contribution calculation:

[0055] SHAP global analysis: Based on the Shapley value in game theory, the marginal contribution of each feature in all possible subsets is calculated, and the importance of global features (such as spectrum dominant frequency and discharge amplitude dispersion) is output.

[0056] LIME local explanation: Generate a perturbation dataset for a single sample, train a local linear model, and extract key features (such as the phase stability index of a certain discharge).

[0057] Visual output: Use SHAP's summary_plot to display global feature contributions, and LIME to generate heat maps or text reports (such as "1200MHz band contribution +35%").

[0058] In some embodiments, step S300 includes:

[0059] S320, constructing a physical rule base.

[0060] S330. When the environmental data or the equipment operation status data exceeds the preset threshold, the physical rule verification is triggered; based on the environmental data or the equipment operation status data, it is determined whether the environmental anomaly or the equipment anomaly may cause feature distortion; if so, the confidence level is adjusted and an environmental / equipment anomaly alarm is generated.

[0061] S340, when the confidence of the discharge type classification result is lower than the set threshold, trigger the physical rule verification; obtain the key features according to the feature contribution of the discharge type classification result, match the physical rules in the physical rule library according to the key features, and determine whether the partial discharge features that the discharge type identification model depends on are in line with the physical rules; when the discharge type identification model depends on conflicting features, trigger the rule conflict alarm.

[0062] The physical rule base verification in this embodiment includes: Rule base construction: domain knowledge (such as "polarity effect index>0.7→metal tip discharge") is encoded into logical rules and stored in a structured database. Dynamic verification logic: When SHAP / LIME identifies key features (such as high-frequency band energy), the system automatically matches physical rules (such as "high-frequency band is susceptible to communication interference"), and triggers an alarm if there is a conflict; Example: If the model determines that it is "air gap discharge", but the SHAP value shows dependence on atypical features (such as ambient temperature), it is marked as "manual review required" in combination with the rule base. Feedback optimization: User correction results (such as marking misjudged cases as "surface discharge") are transmitted back to the rule base to update rule weights or add new constraints.

[0063] The specific implementation steps include: Data input: The partial discharge signal is pre-processed (filtering, noise reduction) and then input into the hybrid model (such as CNN+Transformer). Model prediction: Output the discharge type and confidence (such as "metal tip discharge, 92%"). Explanation generation: SHAP calculates the importance of global features, and LIME generates single sample explanations; extracts key features (such as "uniformity of discharge times"). Rule matching: Match features with the physical rule library (such as "uniform discharge times → metal tip"); if the rules conflict (such as features depend on the interference frequency band), trigger dynamic threshold adjustment. Decision output: Generate a report containing explanations and rule verification (such as "high risk: maintenance is recommended within 24 hours").

[0064] In some embodiments, S400 includes:

[0065] S410, construct a knowledge graph.

[0066] S420: Match a preset operation and maintenance strategy according to the input discharge type.

[0067] S430. Dynamically adjust the operation and maintenance strategy according to the equipment operation status data.

[0068] S440, calculating the risk level according to the confidence level, environmental data and equipment operation status data to obtain the operation and maintenance priority. This embodiment can achieve more accurate risk classification by integrating the load rate and the discharge amount.

[0069] S450: Output an operation and maintenance text report according to the operation and maintenance strategy and the operation and maintenance priority.

[0070] The knowledge graph construction of this embodiment includes: entity and relationship definition: define entities: equipment type (GIS cabinet), fault mode (metal discharge), maintenance action (power outage maintenance). Construct relationships: equipment-fault association (GIS cabinet → metal discharge), fault-maintenance mapping (metal discharge → replace insulating basin). Integrate data sources, including structured data: historical work orders, equipment parameter tables; unstructured data: operation and maintenance logs, expert experience documents (entity relationships are extracted through NLP).

[0071] The operation and maintenance strategy matching process of this embodiment includes: fault type input: model output discharge type (such as "suspended potential body discharge") and confidence. Graph query: query the associated maintenance strategies in the knowledge graph (such as "suspended discharge → check flange pouring port"); filter high success rate strategies based on historical data (such as "the success rate of strategy A for similar faults in the past 3 months has reached 90%"). Priority sorting: generate a priority list based on strategy effect, implementation cost, and equipment status data; example: high-risk faults automatically trigger work orders to be dispatched to the operation and maintenance system 91214. Multimodal output: text report: "Repair is recommended within 7 days, priority: high"; AR visualization: overlay fault location guidance on the GIS cabinet entity.

[0072] In some embodiments, S600 includes:

[0073] S610: Use the newly labeled data to fine-tune the classification model and optimize the attention weight distribution.

[0074] S620. Update the knowledge graph node weights according to the operation and maintenance personnel's scores on the operation and maintenance strategies.

[0075] Reference Figure 2 The embodiment of the present invention proposes to implement a digital intelligent integrated platform terminal display operation and maintenance decision system, including:

[0076] The data acquisition module 100 is used to collect partial discharge signals in real time through UHF sensors and ultrasonic sensors, and obtain environmental data and equipment operation status data; the environmental data includes temperature and humidity; the equipment operation status data includes load rate, operation time and voltage.

[0077] The partial discharge identification module 200 is used to input the partial discharge signal into the CNN+Transformer network to extract time-frequency domain features, input the environmental data and equipment operation status data into the fully connected network to encode auxiliary features, and dynamically weight the time-frequency domain features and auxiliary features through the attention mechanism to obtain a fused feature vector; output the discharge type probability distribution based on the fused feature vector, and generate the environment or equipment operation status association analysis based on the auxiliary feature vector and the discharge type classification result through gradient back propagation and physical rule matching.

[0078] The explainability module 300 is used to generate a feature contribution report based on SHAP+LIME, obtain key features according to the feature contribution report, match physical rules of the physical rule library based on the key features, verify the model decision according to the physical rules, and determine to dynamically adjust the confidence or output environmental risk prompts based on the model decision verification results.

[0079] The operation and maintenance strategy module 400 is used to obtain the discharge type, discharge type confidence, environmental data and equipment operation status data, and match the operation and maintenance strategy based on the knowledge graph.

[0080] The visualization module 500 is used to generate a 3D spectrum diagram and a thermal map based on the partial discharge signal, generate a natural language report based on the operation and maintenance strategy, and display the 3D spectrum diagram, the thermal map and the natural language report on the visualization interface.

[0081] The feedback optimization module 600 is used to record the user's modified decisions and the execution results of the operation and maintenance strategy, update the model parameters through incremental learning, dynamically adjust the knowledge graph node weights, and optimize the physical rule base logic.

[0082] Although specific embodiments are described herein, those of ordinary skill in the art will recognize that many other modifications or alternative embodiments are also within the scope of the present disclosure. For example, any of the functions and / or processing capabilities described in conjunction with a particular device or component may be performed by any other device or component. In addition, although various exemplary implementations and architectures have been described according to embodiments of the present disclosure, those of ordinary skill in the art will recognize that many other modifications to the exemplary implementations and architectures described herein are also within the scope of the present disclosure.

[0083] Some aspects of the present disclosure are described above with reference to the block diagram and flow chart of the system, method, system and / or computer program product according to the exemplary embodiments.It should be understood that the combination of one or more blocks in the block diagram and the flow chart and the blocks in the block diagram and the flow chart can be realized by executing computer executable program instructions respectively.Equally, according to some embodiments, some blocks in the block diagram and the flow chart may not need to be executed in the order shown, or may not need to be executed in full.In addition, additional components and / or operations beyond those components and / or operations shown in the blocks in the block diagram and the flow chart may be present in certain embodiments.

[0084] Therefore, the blocks in the block diagrams and flow charts support the combination of devices for performing the specified functions, the combination of elements or steps for performing the specified functions, and the program instruction devices for performing the specified functions. It should also be understood that each block in the block diagrams and flow charts and the combination of blocks in the block diagrams and flow charts can be implemented by a special-purpose hardware computer system or a combination of special-purpose hardware and computer instructions that performs a specific function, element or step.

[0085] The program modules, applications, etc. described herein may include one or more software components, including, for example, software objects, methods, data structures, etc. Each such software component may include computer-executable instructions that, in response to execution, cause at least a portion of the functionality described herein (e.g., one or more operations of the illustrative methods described herein) to be performed.

[0086] Software components can be encoded with any of various programming languages. An exemplary programming language can be a low-level programming language, such as an assembly language associated with a specific hardware architecture and / or an operating system platform. The software component comprising assembly language instructions may need to be converted to executable machine code by an assembler before being executed by hardware architecture and / or platform. Another exemplary programming language can be a higher-level programming language, which can be transplanted across multiple architectures. The software component comprising a higher-level programming language may need to be converted to an intermediate representation by an interpreter or a compiler before execution. Other examples of programming languages ​​include but are not limited to macro languages, shells or command languages, job control languages, scripting languages, database queries or search languages ​​or report writing languages. In one or more exemplary embodiments, the software component comprising the instruction of one of the above-mentioned programming language examples can be directly executed by an operating system or other software components, without first being converted into another form.

[0087] Software components may be stored as files or other data storage structures. Software components of similar type or related functions may be stored together, such as in a specific directory, folder, or library. Software components may be static (e.g., preset or fixed) or dynamic (e.g., created or modified at execution time).

[0088] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A digital intelligent integrated platform terminal display operation and maintenance decision method, characterized in that: The following steps are involved: S100, collecting partial discharge signals in real time through UHF sensors and ultrasonic sensors, and acquiring environmental data and equipment operation status data; the environmental data includes temperature and humidity; the equipment operation status includes data load rate, operation time and voltage; S200, identifying the discharge type through the discharge type identification model, including: inputting the partial discharge signal into the CNN+Transformer network to extract time-frequency domain features, inputting the environmental data and the equipment operation status data into the fully connected network to encode the auxiliary features, and dynamically weighting the time-frequency domain features and the auxiliary features through the attention mechanism to obtain a fused feature vector; outputting the discharge type probability distribution based on the fused feature vector, and generating the environment or equipment operation status association analysis based on the auxiliary feature vector and the discharge type classification result through gradient back propagation and physical rule matching; S300, generate a feature contribution report based on SHAP+LIME, obtain key features according to the feature contribution report, match physical rules of the physical rule library based on the key features, verify the model decision according to the physical rules, and determine to dynamically adjust the confidence or output environmental risk prompts according to the model decision verification results; S400, obtaining discharge type, discharge type confidence, environmental data and equipment operation status data, and matching operation and maintenance strategy based on knowledge graph; S500, generating a 3D spectrum diagram and a thermal map based on the partial discharge signal, generating a natural language report based on the operation and maintenance strategy, and displaying the 3D spectrum diagram, the thermal map and the natural language report on a visual interface; S600, record the user's revised decision and operation and maintenance strategy execution results, update the model parameters through incremental learning, dynamically adjust the knowledge graph node weights, and optimize the physical rule base logic.

2. The digital intelligent integrated platform terminal display operation and maintenance decision-making method according to claim 1 is characterized in that: The step S200 includes: S210, pre-processing the collected partial discharge signal, including filtering and noise reduction; and dynamically adjusting the signal gain according to the temperature data.

3. The digital intelligent integrated platform terminal display operation and maintenance decision-making method according to claim 1 is characterized in that: The step S200 includes: S220, extracting the spectral spatial features of the partial discharge signal based on CNN, capturing the time domain sequence dependency based on Transformer, and obtaining the time-frequency domain features of the partial discharge signal; S230, obtaining auxiliary features based on fully connected network coding environment features and device operation status features; S240, generating partial discharge signal weights and environment / state weights based on the fully connected layer, weightedly fusing the time-frequency domain features and auxiliary features of the partial discharge signal to obtain a fused feature vector; and outputting a discharge type probability distribution based on the fused feature vector.

4. The digital intelligent integrated platform terminal display operation and maintenance decision-making method according to claim 1 is characterized in that: The step S200 includes: S250, calculating the gradient of the environmental feature or the device operating status feature on the classification result by back propagation, and quantifying the influence weight of the environmental feature or the device operating status feature; S260, comparing the quantification results with the preset domain knowledge, verifying the rationality of the discharge type identification model, and obtaining the correlation analysis results of the environment or equipment operation status.

5. The digital intelligent integrated platform terminal display operation and maintenance decision-making method according to claim 1 is characterized in that: The step S300 includes: S310, perform feature contribution analysis on the discharge type classification results, obtain the SHAP global interpretation by calculating the Shapley value of each feature of the partial discharge signal, and obtain the LIME local interpretation by generating a single sample heat map and identifying the key discriminant features.

6. The digital intelligent integrated platform terminal display operation and maintenance decision-making method according to claim 1 is characterized in that: The step S300 includes: S320, constructing a physical rule base, including encoding domain knowledge into logical rules, storing them in a structured database, and obtaining a physical rule base; S330, when the environment data or the device operation status data exceeds the preset threshold, trigger the physical rule verification; determine whether the environment abnormality or the device abnormality may cause feature distortion according to the environment data or the device operation status data; if so, adjust the confidence level and generate an environment / device abnormality alarm; S340, when the confidence of the discharge type classification result is lower than the set threshold, trigger the physical rule verification; obtain the key features according to the feature contribution of the discharge type classification result, match the physical rules in the physical rule library according to the key features, and determine whether the partial discharge features that the discharge type identification model depends on are in line with the physical rules; when the discharge type identification model depends on conflicting features, trigger the rule conflict alarm.

7. The digital intelligent integrated platform terminal display operation and maintenance decision-making method according to claim 1 is characterized in that: The S400 includes: S410, obtaining historical work orders, equipment parameter tables, operation and maintenance logs, and expert experience documents, and constructing a knowledge graph, including defining equipment types, failure modes, and operation and maintenance actions as entities, and constructing relationships between entities; S420, matching the associated operation and maintenance strategies in the knowledge graph according to the input discharge type, and screening the operation and maintenance strategies with high success rates in combination with historical data; S430, dynamically adjusting the operation and maintenance strategy according to the equipment operation status data; S440, calculating the risk level according to the confidence level, environmental data and equipment operation status data, and obtaining the operation and maintenance priority; S450: Output an operation and maintenance text report according to the operation and maintenance strategy and the operation and maintenance priority.

8. The digital intelligent integrated platform terminal display operation and maintenance decision method according to claim 1 is characterized in that: The S600 includes: S610, fine-tune the classification model using the newly annotated data and optimize the attention weight distribution; S620. Update the knowledge graph node weights according to the operation and maintenance personnel's scores on the operation and maintenance strategies.

9. The digital intelligent integrated platform terminal display operation and maintenance decision-making method according to claim 1 is characterized in that: The step S500 includes: S510: Generate AR positioning guidance according to the operation and maintenance strategy.

10. A digital intelligent integrated platform terminal display operation and maintenance decision system, used to execute the method according to any one of claims 1 to 9, characterized in that: include: A data acquisition module is used to collect partial discharge signals in real time through a UHF sensor and an ultrasonic sensor, and obtain environmental data and equipment operation status data; the environmental data includes temperature and humidity; the equipment operation status data includes load rate, operation time and voltage; The partial discharge identification module is used to input the partial discharge signal into the CNN+Transformer network to extract the time-frequency domain features, input the environmental data and equipment operation status data into the fully connected network to encode the auxiliary features, and dynamically weight the time-frequency domain features and auxiliary features through the attention mechanism to obtain the fused feature vector; The fusion feature vector outputs the probability distribution of the discharge type. Based on the auxiliary feature vector and the discharge type classification result, the environment or equipment operation status association analysis is generated through gradient back propagation and physical rule matching. The explainability module is used to generate a feature contribution report based on SHAP+LIME, obtain key features based on the feature contribution report, match the physical rules of the physical rule library based on the key features, verify the model decision based on the physical rules, and determine to dynamically adjust the confidence or output environmental risk prompts based on the model decision verification results; The operation and maintenance strategy module is used to obtain the discharge type, discharge type confidence, environmental data and equipment operation status data, and match the operation and maintenance strategy based on the knowledge graph; The visualization module is used to generate 3D spectrum graphs and thermal maps based on partial discharge signals, and natural language reports based on operation and maintenance strategies, and display 3D spectrum graphs, thermal maps and natural language reports on the visualization interface; The feedback optimization module is used to record the user's correction decisions and the execution results of operation and maintenance strategies, update model parameters through incremental learning, dynamically adjust the weights of knowledge graph nodes, and optimize the logic of the physical rule base.

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