A cable branch box digital twin early warning method fusing environmental coupling effects

Abstract

The application relates to the technical field of power system intelligent operation and maintenance, in particular to a cable branch box digital twin early warning method fusing environment coupling effects, which can effectively quantify the influence of environmental stress on equipment aging, dynamically adjust the early warning threshold, and thus improve early warning accuracy and equipment operation and maintenance reliability. The application realizes quantification of environment-equipment coupling effects and adaptive optimization of the early warning threshold by acquiring multi-source data, calculating an environment stress index, constructing an environment-equipment coupling aging model, and dynamically adjusting the early warning threshold, thereby solving the problems of neglecting the influence of environmental stress and false positives and false negatives caused by fixed thresholds in the prior art, effectively quantifying the influence of environmental stress on equipment aging, dynamically adjusting the early warning threshold, and thus improving early warning accuracy and equipment operation and maintenance reliability.

Description

Technical Field

[0001] This application relates to the field of intelligent operation and maintenance technology of power systems, and more specifically, to a digital twin early warning method for cable branch boxes that integrates environmental coupling effects. Background Technology

[0002] With the deepening of smart grid construction, the reliable operation of cable branch boxes is crucial to grid security. Digital twin technology, as a bridge connecting the physical and virtual worlds, enables real-time monitoring, simulation analysis, and fault early warning of physical equipment by constructing virtual mapping models of devices, and has become a core technology for the intelligent operation and maintenance of cable branch boxes. However, existing digital twin systems for cable branch boxes face significant challenges in practical applications. Current solutions mainly rely on operational data collected by internal sensors to construct a single device model, or use historical fault records to train prediction algorithms, while employing fixed parameter thresholds to trigger early warning mechanisms. These methods fail to fully integrate the dynamic impact of environmental stress on equipment degradation. For example, drastic fluctuations in temperature and humidity, chemical contaminant corrosion, and external mechanical vibrations can significantly accelerate the aging of cable branch box insulation materials and the corrosion of metal components.

[0003] In coastal areas with high salt spray, environmental stress causes insulation performance to degrade at a rate far exceeding expectations. In industrially polluted environments, metal corrosion rates exhibit nonlinear growth, but existing models lack a quantification mechanism for the environment-equipment coupling effect, resulting in significant deviations in risk prediction under complex operating conditions. Furthermore, early warning thresholds do not consider the phased differences in equipment service life; during the new commissioning period, overly sensitive thresholds lead to frequent false alarms, while during the aging period, overly lenient thresholds result in missed potential faults.

[0004] Furthermore, data sources are limited to internal sensors and fail to effectively integrate external information sources such as weather forecasts and power grid dispatching. For example, sudden increases in humidity before heavy rainfall or vibration changes caused by extreme weather cannot be included in the early warning analysis in a timely manner, resulting in a lag in system response. The remaining life assessment is also inaccurate because it ignores the nonlinear accelerating effect of environmental stress. The differences in aging rates of the same equipment under different environmental conditions cannot be accurately captured, affecting the scientific nature of operation and maintenance decisions.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] (a) Technical problems to be solved The purpose of this application is to provide a digital twin early warning method for cable branch boxes that integrates environmental coupling effects, which can effectively quantify the impact of environmental stress on equipment aging and dynamically adjust the early warning threshold, thereby improving the accuracy of early warning and the reliability of equipment operation and maintenance.

[0007] (II) Technical Solution A digital twin early warning method for cable branch boxes that integrates environmental coupling effects is proposed, and the technical solution is as follows: include: Acquire operational, environmental, and external data for the cable branch box; Calculate the environmental stress index based on the environmental data; An environment-equipment coupled aging model is constructed based on the environmental stress index. The early warning threshold is dynamically adjusted based on the service life of the cable branch box and the environmental stress index. The warning results are output based on the environment-equipment coupled aging model and the adjusted warning threshold.

[0008] Furthermore, this application also proposes an electronic device comprising: At least one processor; The memory stores instructions that, when executed by at least one processor, cause the at least one processor to perform the aforementioned digital twin early warning method for cable branch boxes that incorporates environmental coupling effects.

[0009] Furthermore, this application also proposes a non-transitory machine-readable storage medium storing executable instructions that, when executed, cause the machine to perform the aforementioned digital twin early warning method for cable branch boxes that incorporates environmental coupling effects.

[0010] (III) Beneficial Effects Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By acquiring multi-source data, calculating the environmental stress index, constructing an environment-equipment coupled aging model, and dynamically adjusting the warning threshold, this invention realizes the quantification of the environment-equipment coupling effect and the adaptive optimization of the warning threshold. This solves the problems of ignoring the influence of environmental stress and the false alarms and missed alarms caused by fixed thresholds in the prior art. It can effectively quantify the influence of environmental stress on equipment aging and dynamically adjust the warning threshold, thereby improving the accuracy of warning and the reliability of equipment operation and maintenance. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic diagram of the logical structure of the digital twin early warning method for cable branch boxes. Detailed Implementation

[0013] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0014] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0015] Example 1

[0016] Traditional digital twin early warning systems for cable branch boxes primarily focus on the equipment's own operating parameters, failing to fully integrate the accelerating effect of environmental stress on equipment degradation, resulting in significant deviations in risk prediction under complex operating conditions. Furthermore, the commonly used fixed early warning thresholds cannot adapt to changes in the equipment's condition throughout its entire lifecycle, easily leading to false alarms and missed alarms. In addition, the single data source and lack of external information integration result in insufficient timeliness and accuracy of early warnings, and low precision in remaining life assessment.

[0017] In this regard, such as Figure 1 As shown, this application proposes a digital twin early warning method for cable branch boxes that integrates environmental coupling effects. By acquiring operational data, environmental data, and external data of the cable branch boxes, it comprehensively perceives the equipment status and external environment. Based on the environmental data, an environmental stress index is calculated, and an environment-equipment coupled aging model is constructed to quantify the impact of the environment on equipment aging.

[0018] Furthermore, the warning threshold is dynamically adjusted based on the service life of the cable branch box and the environmental stress index to adapt to the equipment's entire life cycle. Finally, the warning results are output based on the environment-equipment coupled aging model and the adjusted warning threshold, thereby improving the accuracy and reliability of the warning.

[0019] For ease of understanding, the following explains some key terms in this embodiment: The digital twin early warning method for cable branch boxes refers to a technical solution that constructs a virtual mapping model of the cable branch box, monitors its operating status in real time, and combines environmental factors to predict aging and provide early warnings of faults. This method aims to improve the intelligence level of cable branch box operation and maintenance.

[0020] Operational data refers to various parameters reflecting the internal working status of the cable distribution box, such as temperature, current, voltage, and partial discharge signals. This data is typically collected by sensors installed on the equipment.

[0021] Environmental data refers to various parameters of the external environment in which the cable distribution box is located, such as ambient temperature, humidity, light intensity, rainfall, wind speed, and air pollutant concentration. This data is used to assess the impact of the environment on the equipment.

[0022] External data refers to information related to the operation of cable branch boxes and environmental changes, in addition to operational and environmental data. Examples include weather forecasts, power grid dispatch instructions, and information on nearby construction projects. This data helps provide more comprehensive background information.

[0023] Environmental stress index: This refers to a value obtained after a comprehensive quantitative assessment of multiple environmental parameters, used to characterize the overall impact of the environment on the aging or performance degradation of cable branch boxes. This index simplifies complex environmental factors into a single quantifiable indicator.

[0024] Environment-equipment coupled aging model: This refers to a mathematical or physical model that describes the interaction between the environmental stress index and the aging rate of cable branch box materials or components. This model is used to predict the degradation trend of equipment performance under specific environmental conditions.

[0025] Service life: refers to the length of time since the cable distribution box was put into operation. This parameter is an important basis for assessing the current aging degree of the equipment and its stage in the life cycle.

[0026] Warning threshold: This refers to the critical value used to determine whether the operating status of the cable branch box is abnormal or whether there is a potential risk. When the monitored operating parameters exceed or fall below this threshold, the system will trigger a warning.

[0027] This application includes the following steps: S100, acquiring operational data, environmental data, and external data of the cable branch box. Specifically, operational data can be collected through various sensors installed inside or on the surface of the cable branch box. For example, temperature sensors can periodically measure the internal temperature of the equipment, and current sensors can monitor the load current in real time. Environmental data can be acquired through environmental monitoring devices deployed around the cable branch box. For example, ambient temperature and humidity sensors can record the air temperature and humidity. External data can be acquired from external information sources related to the operation of the cable branch box, such as obtaining meteorological information manually from public information platforms or obtaining dispatch information from power grid operation departments. These data are collected and stored independently as the basis for subsequent analysis.

[0028] The acquisition of external data specifically includes: Methods for obtaining weather forecast data: Method 1: Subscribe to regional weather forecast data in real time from the China Meteorological Administration or commercial weather service providers (such as China Meteorological Data Network, Hefeng Weather, Caiyun Weather, etc.) via API interface. The subscription frequency can be set to update every 1 hour, 3 hours, or 6 hours according to the warning needs. The acquired data includes forecast values ​​of meteorological elements such as temperature, humidity, precipitation probability, wind speed, and air pressure for the next 24-72 hours.

[0029] Method 2: Utilize micro-weather stations deployed near or within cable distribution boxes, combined with historical meteorological data and local meteorological models (such as WRF models and numerical weather prediction models) for short-term forecasting. This method is suitable for scenarios with special requirements for local micro-meteorological environments and can provide more accurate local weather forecasts.

[0030] Methods for obtaining power grid dispatch data: Method 1: Obtain operational data and dispatching instructions in real time from the power grid dispatch automation system (SCADA / EMS). Establish data interfaces through standard communication protocols such as OPC UA, IEC 61850, or IEC 60870-5-104 to achieve real-time transmission of power grid operational data (such as load forecasting, power flow distribution, and switch status).

[0031] Method 2: Obtain future load forecast data and grid operation plans by connecting with power trading centers or load forecasting systems. Data transmission can be achieved through Web Services, RESTful APIs, or message queues (such as RabbitMQ and Kafka).

[0032] The acquisition and integration of this external data enables the early warning system to anticipate the impact of weather changes and power grid operation fluctuations on equipment, transforming early warning from a passive response to an active prediction and significantly extending the lead time for early warnings.

[0033] S200. Calculate the environmental stress index based on the acquired environmental data. This index aims to quantify the comprehensive impact of environmental factors on the cable distribution box. For example, environmental parameters such as ambient temperature, humidity, and air pollutant concentration can be selected, and these parameters can be simply averaged. Alternatively, a dominant environmental factor can be set based on experience (e.g., considering only ambient temperature), and its value can be directly used as the environmental stress index. As another implementation method, a fixed weight can be preset for each environmental parameter, and then each environmental parameter can be multiplied by its corresponding weight and summed to obtain the environmental stress index.

[0034] S300. Construct an environment-equipment coupled aging model based on the environmental stress index. This model describes the impact of environmental stress on the aging process of cable branch boxes. For example, a simple lookup table can be established, containing different environmental stress index ranges and their corresponding equipment aging rates or aging acceleration factors. After calculating the current environmental stress index, the corresponding acceleration factor can be obtained by looking up the table. As another implementation method, an approximate linear relationship can be assumed between the environmental stress index and the equipment aging rate, and the aging rate under the current environmental stress can be estimated using a preset linear function. Thus, the coupling effect of environmental factors on equipment performance degradation can be preliminarily assessed.

[0035] S400: Dynamically Adjusting Early Warning Thresholds Based on the Service Life and Environmental Stress Index of Cable Branch Boxes. Traditional fixed thresholds cannot adapt to changes in equipment status at different life stages and under different environmental conditions. In this application, the service life of the cable branch box can be roughly divided into several operating stages, such as the initial operating stage and the later operating stage. For each stage, a basic early warning threshold adjustment factor can be set. At the same time, combined with the currently calculated environmental stress index, the basic adjustment factor is simply modified. For example, when the environmental stress index is high, the adjustment factor is appropriately reduced to make the early warning threshold more stringent. Thus, the initial early warning threshold can be multiplied by the service life adjustment factor and the environmental stress adjustment factor to obtain a dynamic early warning threshold adapted to the current state.

[0036] The S500 system outputs warning results based on an environment-equipment coupled aging model and adjusted warning thresholds. Specifically, the environment-equipment coupled aging model can predict the performance degradation trend of cable branch boxes. Real-time monitored operating data is compared with dynamically adjusted warning thresholds. When any operating parameter exceeds its corresponding adjusted warning threshold, the system determines that there is an anomaly or potential risk and outputs a warning message. This warning message can be presented in various forms, such as displaying a warning icon on the monitoring interface, or notifying maintenance personnel via SMS, email, etc.

[0037] The specific logic of the early warning comparison includes: Step A: Parameter Classification and Threshold Matching Based on the monitored data types, they are matched with corresponding early warning thresholds. Early warning thresholds are categorized by parameter type as follows: Temperature warning threshold:

[0038] Current warning threshold:

[0039] Partial discharge warning threshold:

[0040] Insulation resistance warning threshold:

[0041] Step B: Comparison of real-time data with thresholds The real-time monitored operational data is compared with its corresponding dynamically adjusted warning threshold: For rising parameters (such as temperature and current): when the operating parameter is greater than... Time-triggered warning For decreasing parameters (such as insulation resistance): when the operating parameter is less than... Time-triggered warning Step C: Multi-parameter fusion judgment When multiple operating parameters simultaneously approach or exceed their respective warning thresholds, a weighted fusion judgment method is adopted:

[0042] in, For the first The weights of each parameter (determined based on the importance of the parameter to equipment safety). For the first The risk level of each parameter (between 0 and 1, calculated based on the degree to which the parameter deviates from the threshold).

[0043] when When the overall risk threshold (e.g., 0.7) is exceeded, an early warning may be triggered even if a single parameter does not exceed the threshold, thus achieving multi-parameter collaborative early warning.

[0044] Step D: Determining the Warning Level Based on the degree to which the parameters exceed the threshold, the warnings are divided into several levels: Level 1 Warning (Attention): Parameters exceed the threshold by 0%-10%. Level 2 Warning: Parameter exceeds the threshold by 10%-30%. Level 3 Warning (Severe): Parameters exceed the threshold by more than 30%. Step E: Output of early warning information The early warning information includes: warning level, warning parameter name, current parameter value, warning threshold, exceedance range, predicted development trend, and recommended handling measures.

[0045] This application integrates multi-source data, quantifies environmental stress, and constructs an environment-equipment coupled aging model, achieving comprehensive perception and accurate prediction of the cable branch box status. Dynamically adjusting the early warning threshold adapts it to the equipment's entire lifecycle status and real-time environmental conditions, effectively reducing false alarms and missed alarms. Therefore, this method overcomes the limitations of traditional early warning systems that ignore environmental stress, have fixed thresholds, and rely on single data sources, significantly improving the accuracy, timeliness, and reliability of digital twin early warning systems for cable branch boxes.

[0046] In some of the embodiments described above in this application, an environmental stress index is proposed to be calculated based on environmental data to quantify the impact of the environment on equipment aging. However, in the implementation process, environmental parameters may have different dimensions and ranges, resulting in inaccurate calculations and failing to effectively reflect the comprehensive impact of each environmental parameter on aging.

[0047] In response, this application further proposes a method for calculating the environmental stress index based on the aforementioned environmental data, the method comprising: The environmental data is normalized to obtain normalized environmental parameters, wherein the normalization process uses the following formula:

[0048] in, To normalize environmental parameters, These are the current environmental parameter values. This represents the minimum value of the environmental parameters. This represents the maximum value of the environmental parameter. The environmental stress index is obtained by weighted summation of the normalized environmental parameters based on preset weights, wherein the preset weights are determined by the analytic hierarchy process based on the significance of the influence of each environmental parameter on the aging of the cable branch box.

[0049] Specifically, the environmental data is normalized to obtain normalized environmental parameters. This normalization process eliminates differences in dimensions and numerical ranges between different environmental parameters (such as temperature, humidity, and salt spray concentration), transforming them to a unified scale space to make them comparable. This ensures that in subsequent comprehensive calculations, the environmental parameters will not unfairly influence the results due to differences in their original numerical values ​​or units, thus more accurately reflecting their actual contribution to the aging of cable distribution boxes. In addition to the Min-Max normalization method explicitly stated in the claims, Z-score normalization can also be used. ,in The mean of the environmental parameters. The standard deviation is used to transform the data into a distribution with a mean of 0 and a standard deviation of 1, which is suitable for cases where the data distribution is approximately normal.

[0050] The normalization process described above uses the following formula: ,in, To normalize environmental parameters, These are the current environmental parameter values. This represents the minimum value of the environmental parameters. This represents the maximum value of the environmental parameters. This formula is a specific implementation of Min-Max normalization, and its function is to normalize the original environmental parameters. The linear transformation is applied to the interval [0,1]. In this way, regardless of whether the original parameter is temperature, humidity, or other physical quantities, its normalized value falls between 0 and 1, thereby completely eliminating dimensional differences and ensuring that different environmental parameters have the same weighting basis when performing subsequent weighted summation. and The determination can be based on statistical analysis of historical operating data, such as taking the minimum and maximum values ​​of historical data, or taking the extreme values ​​within a certain confidence interval; it can also be set according to the design specifications, material characteristics or industry standards of the cable branch box. For example, for temperature parameters, it can be set as the lower and upper limits of the temperature range that the cable branch box material can withstand.

[0051] The environmental stress index is obtained by weighted summation of the normalized environmental parameters based on preset weights. Weighted summation is a method that comprehensively calculates multiple values ​​with different importance. Here, its function is to combine multiple normalized environmental parameters according to their relative importance to the aging of cable distribution boxes, thereby obtaining a single "environmental stress index" that comprehensively reflects the degree of environmental impact on equipment aging. This allows multi-dimensional environmental impacts to be quantified into a unified indicator, facilitating subsequent model construction and early warning judgment. Besides the analytic hierarchy process mentioned in the claims, the preset weights can also be determined through expert experience, i.e., experts in the field of cable distribution boxes score or rank the importance of each environmental parameter based on their professional knowledge and experience, thereby determining the weights.

[0052] The preset weights are determined using the Analytic Hierarchy Process (AHP) based on the significance of each environmental parameter's impact on the aging of the cable branch box. AHP is a decision analysis method that quantifies qualitative problems. Its function is to systematically and scientifically determine the weights of each environmental parameter, avoiding subjective arbitrariness. By constructing a hierarchical model and performing pairwise comparisons, the relative importance weights of each factor are calculated. This method fully considers the complexity and interrelationships of the impact of each environmental parameter on the aging of the cable branch box, ensuring that the determined weights truly reflect their significance in equipment aging. In practice, the judgment matrix for AHP can be filled out jointly by multiple experts, and then the expert opinions are synthesized using the geometric mean or arithmetic mean method to improve the objectivity and reliability of the weights.

[0053] Through the above technical solution, this application solves the technical problem that the calculation of environmental stress index is distorted due to differences in the dimensions and numerical ranges of different environmental parameters, and cannot truly reflect the comprehensive impact of multiple environmental factors on equipment aging. This is achieved by normalizing environmental data using a specific formula and then using the analytic hierarchy process to determine the weights before performing weighted summation.

[0054] Specifically, adopt Normalization processes map environmental parameters with vastly different physical meanings and value ranges, such as temperature, humidity, salt spray concentration, and corrosive gas concentration, to a standard range of 0 to 1. This eliminates dimensional and magnitude differences without altering the relative distribution of data, providing a basis for direct superposition and comparison of various environmental factors and ensuring the fairness and rationality of subsequent comprehensive calculations. Based on this, weighted summation of the normalized environmental parameters integrates dispersed single-dimensional environmental stresses into a unique comprehensive environmental stress index, achieving a unified characterization of the overall environmental stress intensity. The weights are not empirically set or fixed values, but scientifically determined using the analytic hierarchy process (AHP) based on the significance of each environmental parameter's impact on cable branch box aging. This ensures that the weight allocation aligns with the physical mechanisms of equipment aging and achieves a structured quantification of the influence of multiple factors, avoiding calculation biases caused by subjective weighting. This allows the environmental stress index to accurately reflect the contribution of dominant environmental factors and the true aging-driving intensity under the coupling effect of multiple factors.

[0055] This processing method endows the environmental stress index with stable physical meaning and engineering comparability, adapting to environmental characteristics under different regions and operating conditions. It is suitable for both conventional operating environments and can accurately quantify the accelerated aging effects of harsh environments such as coastal salt spray and industrial pollution. This provides a reliable and unified input basis for subsequent construction of environment-equipment coupled aging models and dynamic threshold adjustments, improving the stability and accuracy of the entire early warning method from the data source. Compared to conventional environmental assessment methods that use fixed weights, simple averaging, or no standardization, this solution combines standardized formulas with weights from the analytic hierarchy process (AHP), significantly improving the accuracy and consistency of environmental stress quantification. It elevates the accelerated aging effect of the environment on equipment from qualitative judgment to quantitative calculation. Furthermore, the calculation process is simple, engineerable, and can stably improve the characterization ability of environmental coupling effects, providing reliable environmental quantification support for the entire digital twin early warning system.

[0056] In some of the solutions mentioned above in this application, an environment-equipment coupled aging model based on the environmental stress index is proposed to quantify the impact of the environment on equipment aging. However, in the implementation process, the existing technology has failed to establish a specific mapping relationship between the environmental stress index and the material aging rate, resulting in the aging model lacking an accurate quantification mechanism and failing to dynamically reflect the cumulative effect of environmental stress, thus causing large deviations in the assessment of equipment performance degradation and inaccurate prediction of remaining life.

[0057] In response, this application further proposes an environment-equipment coupled aging model based on the environmental stress index, which specifically includes: A mapping relationship between the environmental stress index and the aging rate of cable branch box materials is established, whereby the material aging rate is calculated as follows:

[0058] in, As of the current aging rate, As the baseline aging rate, For environmental acceleration factor, Environmental stress index; The degradation of material properties is calculated based on the mapping relationship and the historical integral of the environmental stress index.

[0059] Among them, the mapping relationship between the environmental stress index and the aging rate of cable branch box materials was established, aiming to clarify how environmental factors (quantified by the environmental stress index ESI) directly affect the deterioration rate of key materials (such as insulation materials, seals, etc.) inside the cable branch box.

[0060] This mapping relationship is central to understanding and predicting the aging process of equipment; it transforms abstract environmental impacts into calculable material aging rates. This provides a foundation for subsequent calculations of performance degradation. In practical implementation, accelerated aging experiments can be conducted on the key materials of the cable branch box. Under different environmental stresses (such as temperature, humidity, and corrosive medium concentration), the changes in material properties (such as dielectric strength, mechanical strength, and sealing performance) over time can be monitored, and the corresponding environmental stress indices can be recorded. Subsequently, statistical regression analysis, machine learning algorithms (such as support vector machines and Gaussian process regression), or curve fitting methods can be used to extract and establish a mathematical model between the environmental stress index and the material aging rate, forming a clear mapping relationship. Alternatively, based on the physical aging mechanism of materials science, combined with chemical kinetics theory and simulation tools such as finite element analysis (FEA), the microstructural evolution, chemical bond breaking, and diffusion processes of the cable branch box material under different environmental stresses can be modeled. Through theoretical derivation and simulation verification, a physical model or semi-empirical model that reflects the influence of the environmental stress index on the material aging rate can be constructed, thereby establishing a mapping relationship between the two.

[0061] Material aging rate is expressed by the formula The calculation is performed, and the formula specifically defines the environmental stress index. How to affect the aging rate of materials in, It represents the baseline aging rate under standard or ideal environmental conditions and is an inherent aging characteristic of the material. The environmental acceleration factor characterizes the degree to which a unit environmental stress index accelerates the aging rate. The environmental stress index is the result of comprehensive quantification of environmental data. This formula uses the environmental stress index... Compared with the baseline aging rate Coupling dynamically adjusts the actual aging rate of the material, enabling the aging model to respond to environmental changes in real time. In practical applications, the baseline aging rate... Environmental acceleration factor can be obtained by conducting long-term aging tests on new materials under standard laboratory conditions, or by referring to industry standards and benchmark aging data provided by material suppliers. This can be achieved by conducting comparative aging experiments under different environmental stress levels, observing the differences in material aging rates, and then fitting the results using optimization algorithms such as the least squares method. Furthermore, and Alternatively, it can be determined by analyzing historical operating data, maintenance records, and fault reports of cable branch boxes. For example, data collected under different environmental stress indices can be used to deduce this information. Based on the equipment failure time or performance degradation data, combined with the initial properties of the materials, iterative adjustments are made using reverse engineering, Bayesian inference, or optimization-based machine learning methods. and The value of is determined until the aging rate calculated by the model closely matches the actual observed equipment degradation trend or failure time.

[0062] Based on the mapping relationship and historical integral of the environmental stress index, the material performance degradation is calculated, aiming to quantify the cumulative performance loss of cable branch box materials under long-term environmental stress. This is achieved by analyzing the material aging rate. Integrating over time yields the cumulative degradation of material properties. This integration takes into account the changes in environmental stress over time and its dynamic impact on the aging rate, thus accurately reflecting the actual degree of material degradation throughout its service life.

[0063] In practical applications, the environmental stress index Typically, this is discrete-time series data. Therefore, the degradation of material properties can be determined by analyzing each discrete time interval. Instantaneous aging rate The calculation is performed by summing up the results. Specifically, at each sampling time... According to the current The instantaneous aging rate is calculated using the established mapping relationship. Then Multiply by the time interval The performance degradation increment within this interval is obtained. The final amount of material property degradation This means accumulating the attenuation increment over all time intervals. Alternatively, if the environmental stress index... The changing trend can be approximated as a continuous function, or more precise calculations may be required, which can be achieved using numerical integration methods. For example, numerical integration algorithms such as the trapezoidal rule, Simpson's rule, or Runge-Kutta method can be used to approximate the aging rate function. Integrating over the entire service life of the cable branch box yields a more accurate estimate of material property degradation. This typically requires interpolation or fitting of historical environmental stress index data to construct a continuous... function.

[0064] Through the above technical solution, this application establishes a clear mapping relationship between the environmental stress index and the aging rate of cable branch box materials, and calculates the material performance degradation by combining the historical integral of the environmental stress index. This effectively solves the problems of inaccurate quantification of environmental coupling effects and lack of dynamic cumulative degradation calculation mechanism in the prior art. Specifically, through the formula... By organically combining the baseline aging rate, environmental acceleration factor, and environmental stress index, the accelerated contribution of environmental stress to equipment aging is clearly defined. This allows the material aging rate to change in real time with the environmental stress index, accurately reflecting the true degradation rate of cable branch box materials under different environmental intensities. This avoids using fixed or empirically estimated aging rates, ensuring the rationality and accuracy of aging calculations from a mechanistic perspective. Based on this, the historical integral of the environmental stress index is used to calculate material performance degradation, extending the impact of the environment on equipment from an instantaneous state to a cumulative effect over the entire time dimension, fully considering the gradual degradation caused by the continuous environmental stress that the cable branch box endures during long-term operation. This allows the material performance degradation to be dynamically updated with operating time and environmental history, enabling the environment-equipment coupled aging model to accurately track the complete degradation trajectory of the cable branch box from commissioning to aging, significantly improving the accuracy of performance degradation assessment and the reliability of remaining life prediction. This technical solution transforms the environment-equipment coupling effect from a qualitative description into a calculable, traceable, and reproducible quantitative model. It can adapt to environmental changes in different regions, seasons, and extreme weather conditions, reflecting both rapid degradation caused by instantaneous high environmental stress and slow degradation accumulated under long-term mild environments. By accurately quantifying the impact of the environment on the aging of cable branch box materials, it provides a reliable basis for the subsequent output of early warning results based on the environment-equipment coupling aging model and adjusted early warning thresholds, thereby improving the accuracy and timeliness of early warnings.

[0065] Parameter value range description Reference aging rate The range of values ​​for: Reference aging rate This refers to the aging rate of materials under standard environmental conditions (temperature 25°C, humidity 50%RH, no corrosive media). Based on experimental test data of commonly used materials for cable branch boxes, The typical value range is 0.001-0.005 / year, and the specific value depends on the material type. Cross-linked polyethylene insulation material:

[0066] EPDM rubber seals:

[0067] Copper conductor material:

[0068] Metal casing (steel):

[0069] The methods for determining this include: Method 1: Accelerated Aging Test: Long-term aging tests are conducted on new materials under standard laboratory conditions to monitor changes in material properties (such as dielectric strength and mechanical strength) over time. Data fitting is then used to obtain... value.

[0070] Method 2: Industry Standard Method: Refer to the relevant industry standards for cable distribution boxes (such as GB / T 11017, IEC 60502) regarding the provisions on material performance degradation, and determine the appropriate method based on the technical parameters provided by the manufacturer. value.

[0071] Method 3: Historical Data Inversion Method: Collect failure time and performance degradation data of similar cable branch boxes under different environmental conditions, and use reverse engineering methods to infer the failure time and performance degradation. value.

[0072] Environmental acceleration factor The range of values ​​for: Environmental acceleration factor The environmental stress index characterizes the degree to which a unit of environmental stress accelerates the aging rate; its typical value ranges from 2.0 to 5.0, with the specific value depending on the severity of the environment. Clean up the inland environment: (In a mild environment, the aging process is not significantly accelerated.) Typical urban environment: (Medium-level environment, with some pollution) Coastal salt spray environment: (Harsh environment, significant salt spray corrosion) Heavy industry pollutes the environment: (Extreme environment, extremely corrosive) The methods for determining this include: Method 1: Comparative Aging Test: Accelerated aging tests are conducted on the same material under different environmental stress levels (simulated by an artificial climate chamber), and the differences in aging rates are observed. The aging rate is then fitted using the least squares method. value.

[0073] Method 2: Historical Data Analysis: Analyze the performance degradation rate changes of the same cable branch box in different seasons (corresponding to different environmental stresses), establish the statistical relationship between the environmental stress index and the aging rate, and calculate... value.

[0074] Environmental parameter normalization range The criteria for determination: Temperature parameters: (Extremely low temperature) (Extreme high temperature) Humidity parameters: ,

[0075] Salt spray concentration: , (Upper limit of heavy salt spray environment) Corrosive gas concentration: , (Upper limit for heavily polluted environments) The above value range can be adjusted according to the actual installation environment of the cable branch box. For example, in high-latitude cold regions, the lower limit of the temperature can be adjusted to -40°C, and in tropical regions, the upper limit of the temperature can be adjusted to 60°C.

[0076] In some of the solutions mentioned above in this application, a dynamic adjustment of the early warning threshold based on the service life of the cable branch box and the environmental stress index is proposed to reduce false alarms and missed alarms. However, in this process, the adjustment method may not be precise enough and fails to fully consider the different impacts of the service stage on the changes in equipment status, resulting in a lack of specificity in the threshold adjustment within different life cycles of the equipment, thereby affecting the early warning effect.

[0077] In response, this application further proposes a digital twin early warning method for cable branch boxes that integrates environmental coupling effects. The method involves dynamically adjusting the early warning threshold based on the service life of the cable branch box and the environmental stress index, including: determining the service stage of the cable branch box based on its service life, where the service stage includes the new commissioning period, stable operation period, and aging period; and determining a threshold adjustment coefficient based on the service stage and the environmental stress index, wherein the adjusted early warning threshold is calculated using the following formula:

[0078] in, for The warning threshold is adjusted in real time. The initial warning threshold, This is a service life adjustment factor. Environmental stress adjustment factor; The adjusted warning threshold is calculated based on the initial warning threshold and the threshold adjustment coefficient.

[0079] Specifically, when determining the service stage of a cable distribution box, the service life can be calculated based on its actual operating years, cumulative operating time, or the difference between its manufacturing date and the current date. The division of service stages, such as the new commissioning period, stable operation period, and aging period, can be based on industry experience, life curves provided by equipment manufacturers, historical failure data statistical analysis, or expert knowledge bases. For example, the new commissioning period typically refers to the initial stage after the equipment is put into operation, which may involve early failure risks; the stable operation period is a stage where the equipment performance is relatively stable; and the aging period refers to the stage where the equipment performance begins to decline significantly and the failure rate increases. This division can more accurately reflect the performance differences of the equipment under different aging states, providing a basis for subsequent threshold adjustments.

[0080] When determining the threshold adjustment coefficient, the initial warning threshold The service life adjustment factor can be determined based on the cable branch box design standards, factory test data, industry specifications, or through statistical analysis of a large amount of historical normal operation data (e.g., set as the mean of normal operation parameters plus or minus three standard deviations). This factor aims to quantify the impact of the inherent aging of equipment at different service stages on the warning threshold. It can be set based on the performance degradation pattern, failure rate trend, or remaining life assessment results of the equipment at different service stages. For example, during the initial commissioning period, the equipment may experience early failures, so the threshold can be appropriately tightened; during the stable operation period, the threshold can remain relatively stable; and during the aging period, as equipment performance declines, the threshold can be appropriately relaxed to avoid false alarms. The environmental stress adjustment factor... This is used to quantify the environmental stress index. The accelerated aging rate of equipment, in turn, affects the warning threshold. This factor can be based on the mapping relationship between the environmental stress index and the material aging rate (as mentioned in the methods above). This can be established through experimental data or simulation models. For example, when the environmental stress index is high, equipment aging accelerates, and the warning threshold may need to be adjusted accordingly to reflect the higher risk.

[0081] Through the above technical solution, this application divides service stages according to service years and adopts a service years adjustment factor. Environmental stress adjustment factor Two-factor coupled calculation solves the technical problems of insufficient accuracy in adjusting warning thresholds, mismatched differences in the full life cycle status of equipment, and inability to simultaneously account for the dual impacts of equipment aging and the external environment. Specifically, it divides equipment into new commissioning period, stable operation period, and aging period based on service life, aligning with the typical performance change patterns of cable branch boxes from commissioning and stable operation to gradual aging. This allows threshold adjustments to accurately match the normal state range and risk tolerance of equipment at different life stages, avoiding the poor adaptability problems caused by using a uniform adjustment logic. Based on this, through... The formula calculates thresholds by coupling and superimposing the inherent aging differences brought about by service life with the real-time accelerated degradation effects of environmental stress. This achieves dual adaptation between internal state and external environment, allowing threshold adjustments to simultaneously possess life-cycle regularity and real-time environmental responsiveness. This explicit formulaic adjustment method replaces experience-based judgment with objective parameters, eliminating the subjectivity and arbitrariness of manual adjustments. It enables the threshold to dynamically and adaptively change with the aging process of equipment and the intensity of environmental stress, fundamentally improving the industry-wide problem of false alarms in new equipment and missed alarms in old equipment, and significantly enhancing the stability and relevance of early warning results. This dual-factor dynamic threshold mechanism can maintain reasonable early warning sensitivity at different service stages and under different environmental conditions. It can promptly detect early defects in the new commissioning period, balance false alarms and missed alarms in the stable operation period, and strictly control risks in the aging period. This ensures that the early warning system closely matches the actual operating state of the equipment throughout its lifecycle, providing a reliable judgment benchmark for subsequent accurate output of early warning results.

[0082] In some of the solutions mentioned above in this application, the acquisition of external data is proposed to enhance the accuracy of early warning. However, in the process of implementation, the specific content and fusion method of the external data are not clear, resulting in a single data source, which cannot fully respond to changes in the external environment, and the timeliness and accuracy of early warning are insufficient.

[0083] In response, this application further proposes that the external data includes meteorological forecast data and power grid dispatch data; the method also includes: performing time alignment, spatial registration and weighted fusion on the operational data, environmental data and external data to obtain fused state data, wherein the weighted fusion is based on the reliability of the data source and the accuracy of the sensor to assign weights.

[0084] Specifically, the external data includes meteorological forecast data and power grid dispatch data. Meteorological forecast data refers to predicted information on meteorological elements such as temperature, humidity, precipitation, wind speed, and air pressure for a future period (e.g., the next 24, 48, or 72 hours). Its function is to provide future trends in the equipment operating environment, enabling the early warning system to anticipate potential environmental stress risks. For example, high-precision, high-timeliness regional meteorological forecast data can be obtained through real-time data subscriptions via data interfaces with the China Meteorological Administration or commercial meteorological service providers; or short-term forecasts can be made using micro-meteorological stations deployed near or within the cable branch box, combined with historical meteorological data and local meteorological models. Power grid dispatch data refers to data related to the power grid's operating status and dispatch instructions, such as load forecasts, power flow distribution, switch status, planned maintenance information, and abnormal event records. Its function is to reflect the potential impact of power grid operating conditions on the cable branch box; for example, sudden load changes may cause equipment temperature increases, and maintenance plans may affect equipment operating modes. For example, operational data and dispatch instructions can be obtained in real time from the power grid dispatch automation system (SCADA / EMS) and integrated through data interfaces or message queue services; or future load forecast data and power grid operation plans can be obtained by connecting with the power trading center or load forecasting system.

[0085] Based on this, time alignment is performed on the operational data, environmental data, and external data. Time alignment is a data preprocessing technique designed to ensure consistency in the time dimension of data from different data sources, meaning all data points correspond to the same timestamp or time interval. Its purpose is to eliminate time deviations caused by differences in the acquisition frequencies and transmission delays of different sensors or systems, ensuring the accuracy of subsequent data fusion and model calculations. For example, a unified timestamp standard (such as UTC time) can be used to convert and synchronize the timestamps of all data sources. For data with inconsistent acquisition frequencies, interpolation (such as linear interpolation, spline interpolation) or resampling (such as downsampling, upsampling) methods can be used to unify them to the least common multiple or a preset sampling frequency; alternatively, a time synchronization server can be established, with all data acquisition devices and systems synchronizing with this server to ensure the accuracy of the timestamps during data acquisition. During data fusion, the timestamp of one data source is used as a benchmark, and nearest neighbor matching or in-time window averaging is performed on other data sources.

[0086] Simultaneously, spatial registration is performed on the operational data, environmental data, and external data. Spatial registration is a data preprocessing technique designed to ensure spatial consistency of data from different data sources, meaning all data points can be accurately mapped to their corresponding physical locations or equipment components. Its purpose is to address the issue that different data sources may have different geographic coordinate systems, spatial resolutions, or equipment identifiers, ensuring accurate association with the same physical entity during data fusion. For example, a unified Geographic Information System (GIS) or equipment asset management system can be established to assign unique spatial identifiers and precise geographic coordinates to each cable distribution box, its sensors, and external environmental monitoring points. During data fusion, spatial matching is performed based on these identifiers and coordinates; or, for data with spatial distribution characteristics (such as meteorological data), spatial interpolation (such as Kriging interpolation or inverse distance weighted interpolation) or regional aggregation methods can be used to associate it with the geographical location of the cable distribution box, ensuring that environmental data accurately reflects the environmental conditions of the specific equipment location.

[0087] Furthermore, the time-aligned and spatially registered data are weighted and fused to obtain the fused state data. Weighted fusion is a method that combines information from multiple data sources according to preset weights to generate a more comprehensive and accurate integrated data representation. Its function is to comprehensively utilize the advantages of multi-source data, compensate for the shortcomings of a single data source, and reflect the reliability and importance of different data sources through weight allocation, thereby improving the quality and reliability of the final state data. For example, a linear weighted average method can be used, where the fused state data is the sum of each original data point multiplied by its corresponding weight; or state estimation algorithms based on Kalman filtering or extended Kalman filtering can be used for fusion. These algorithms can dynamically adjust the weights according to the noise characteristics of the data and the model prediction error, achieving better data fusion results. The weighted fusion assigns weights based on data source reliability and sensor accuracy. Assigning weights based on data source reliability means that during weighted fusion, the importance of the data source in the fusion process is determined based on factors such as the trustworthiness of the data source, its historical performance, data integrity, and data update frequency. Data sources with higher reliability will be assigned greater weights. For example, a static reliability score can be assigned to each data source through expert experience assessment, historical data analysis (such as data missing rate and outlier ratio), and the reputation of the data provider, and then converted into weights. Alternatively, a dynamic reliability assessment mechanism can be established to dynamically adjust the reliability score of the data source based on its real-time operating status (such as sensor failure rate, communication interruption, and data volatility), and thus adjust the weights accordingly. Assigning weights based on sensor accuracy means that during weighted fusion, the importance of each sensor in the fusion process is determined based on its technical indicators such as measurement accuracy, resolution, and calibration status. Sensors with higher accuracy will be given greater weights. For example, the measurement accuracy indicators (such as error range and uncertainty) of each sensor can be obtained based on the sensor's factory calibration parameters, calibration reports, or actual test results, and then converted into weights. Alternatively, statistical methods such as Bayesian estimation or maximum likelihood estimation can be used to dynamically calculate weights based on the variance or covariance matrix of the sensor data; sensors with smaller variance (i.e., higher accuracy) will have greater weights.

[0088] Through the aforementioned technical solutions, external data is clearly defined as meteorological forecast data and power grid dispatch data. This allows the early warning system to move beyond information collected by the equipment itself and acquire key external information such as meteorological trends and power grid load fluctuations in advance, providing a data foundation for early risk prediction and extending the lead time for early warnings. By sequentially performing time alignment, spatial registration, and weighted fusion on operational data, environmental data, and external data, heterogeneous data from different sources, with different collection frequencies, and locations can be unified under the same spatiotemporal reference. This eliminates data conflicts and calculation errors caused by time asynchrony and spatial mismatch, ensuring that the state data input to the model accurately reflects the real state of the equipment and environment at the same time and location. Weighted fusion based on data source reliability and sensor accuracy objectively allocates the contribution of each data point, weakening the influence of low-quality data and strengthening the role of high-precision data, further improving the accuracy and reliability of the fused state data. The resulting unified, high-quality fused data provides comprehensive and reliable input support for environmental stress calculation, coupled aging modeling, and dynamic threshold adjustment, enabling the entire early warning system to respond quickly to changes in the external environment and fluctuations in power grid operation, significantly improving the timeliness, accuracy, and robustness of early warnings. This multi-source data fusion method organically combines the internal status of equipment, the external environment, and the operating conditions of the power grid, realizing a shift from passive monitoring to proactive prediction. In particular, it can identify risks in advance in scenarios such as extreme weather and sudden load changes, greatly improving the predictive ability and engineering practical value of digital twin models.

[0089] In some of the embodiments described above in this application, a method for dynamically adjusting the early warning threshold is proposed to adapt to changes in equipment status. However, in the implementation process, the preset threshold adjustment coefficient may not be accurate enough, resulting in a still high false alarm rate and false negative rate.

[0090] To address this, this application further proposes a digital twin early warning method for cable branch boxes that integrates environmental coupling effects. This method also includes a threshold self-learning mechanism. This threshold self-learning mechanism aims to improve the accuracy and reliability of early warnings through continuous learning and optimization.

[0091] Specifically, this threshold self-learning mechanism automatically optimizes the threshold adjustment coefficient based on historical early warning accuracy feedback. Historical early warning accuracy feedback refers to data on early warning accuracy obtained by comparing issued early warning results with actual equipment status during actual system operation. For example, it can record whether each early warning was a true positive (correct warning), a false positive (false alarm), a true negative (no warning and no fault), or a false negative (missed warning). This feedback data forms the basis for evaluating the performance of the early warning system. Automatically optimizing the threshold adjustment coefficient means that the system automatically adjusts the service life adjustment factor used in the dynamic adjustment of the early warning threshold based on this historical feedback data and through an algorithm. and environmental stress adjustment factor For example, gradient descent-based optimization algorithms can be used to fine-tune these coefficients according to the objective function; or reinforcement learning methods can be used to learn the optimal adjustment strategy through trial and error and reward mechanisms; or genetic algorithms can be used to search for the optimal combination of coefficients by simulating the natural selection process. In this way, the system can get rid of its dependence on fixed preset parameters, and the threshold adjustment coefficients can more accurately reflect the actual operating status and environmental impact of the cable branch box.

[0092] This threshold self-learning mechanism aims to reduce both false alarm and false negative rates. False alarm rate refers to the proportion of cases where the system issues a warning but no actual fault occurs. An excessively high false alarm rate increases the workload of maintenance personnel and reduces trust in the warning system. False negative rate refers to the proportion of cases where a fault actually occurs but the system fails to issue a warning. An excessively high false negative rate may lead to equipment faults not being detected in time, causing greater losses. Therefore, making the reduction of false alarm and false negative rates the core optimization objective directly enhances the practical value of the warning system.

[0093] To achieve this optimization goal, the mechanism minimizes the following objective function:

[0094] in, The objective function value, For false alarm rate, The false negative rate, and This is a weighting coefficient used to balance the relative importance of false positives and false negatives during the optimization process. For example, in scenarios with extremely high security requirements, it can be appropriately increased. The weight of the false alarms should be increased to more strictly punish false alarms; however, in scenarios where resources are limited and frequent deployments are not desired, the weight can be appropriately increased. The weights are adjusted to reduce false alarms. By continuously adjusting the threshold adjustment coefficients and calculating new objective function values, the system iteratively searches for the optimal value. The combination of coefficients that minimizes the value is used to find an optimal balance between false alarms and false negatives.

[0095] Through the above technical solution, the early warning method of this application can overcome the problems of traditional dynamic threshold adjustment, where the threshold adjustment coefficient depends on preset values ​​and does not match the actual operating conditions on site. Based on feedback from historical early warning accuracy, the system can continuously learn and adapt to the actual aging characteristics of cable branch boxes under different service stages and environmental conditions, enabling the early warning threshold to more accurately reflect the health status of the equipment. By minimizing the objective function of weighted false alarm rate and missed alarm rate, the early warning system can achieve the best balance between avoiding unnecessary on-site inspections (reducing false alarms) and ensuring timely detection of potential faults (reducing missed alarms) according to actual operation and maintenance needs. This self-learning and adaptive capability enables the cable branch box digital twin early warning system to maintain high accuracy and high reliability over a long period of time, significantly improving the timeliness and accuracy of early warning, thereby effectively supporting the safe and stable operation of the smart grid. This mechanism enables the early warning threshold system to have the ability to continuously evolve, continuously improving early warning accuracy with the accumulation of operating data, and maintaining stable performance with low false alarms and low missed alarms over a long period of time.

[0096] In some of the embodiments described above in this application, an environment-equipment coupled aging model based on the environmental stress index is proposed to dynamically adjust the warning threshold and output the warning result. However, in its implementation, the accuracy of the assessment of the remaining life of the equipment is insufficient because the impact of the uncertainty of environmental changes on the aging rate is not fully considered, resulting in deviations in the remaining life estimation and failing to provide reliable equipment condition prediction.

[0097] In this regard, this application further proposes that the method also includes: calculating the remaining life of the cable branch box based on the environment-equipment coupled aging model, wherein the remaining life is dynamically updated based on the material performance degradation and aging rate; considering the uncertainty of environmental changes, calculating the confidence interval of the remaining life, and outputting the remaining life and the confidence interval.

[0098] Specifically, calculating the remaining life of the cable branch box refers to assessing the length of time that the equipment or its critical components can continue to operate safely under the current operating conditions without failure or performance degradation. Accurately assessing remaining life is crucial for developing preventative maintenance strategies, optimizing spare parts inventory, and reducing operating costs. This calculation can be based on comparing the material performance degradation output from an environment-equipment coupled aging model with a preset failure threshold. For example, when the material performance degradation reaches a certain critical value, the equipment is considered to have failed, and the remaining life is the time required to reach that critical value. Alternatively, it can be based on the predicted aging rate, combined with the current material performance state, through integration or iterative calculation to predict the time required for the material performance to reach the failure threshold. For example, if the aging rate is... The current performance degradation is The failure threshold is Then the remaining lifespan can be approximated as .

[0099] The remaining lifetime is dynamically updated based on material performance degradation and aging rate. This means that the remaining lifetime assessment results are adjusted and corrected in real time based on the latest operational data, environmental data, and model feedback to reflect the actual aging process of the equipment. This ensures the real-time nature and accuracy of the remaining lifetime assessment and avoids the biases that may arise from static assessments. For example, state estimation algorithms such as Kalman filtering or particle filtering can be used to iteratively correct the remaining lifetime estimate using real-time material performance degradation and aging rate as observations. Alternatively, a Bayesian update method can be used to update the posterior probability distribution of the remaining lifetime using new material performance degradation and aging rate data as evidence, thereby obtaining a more accurate remaining lifetime estimate.

[0100] Considering the uncertainty of environmental changes, the confidence interval for remaining lifetime is calculated. This uncertainty refers to the potential for random fluctuations or unpredictable changes in parameters such as temperature, humidity, and pollution in the actual operating environment. The confidence interval provides a probabilistic range encompassing the true remaining lifetime, quantifying the reliability of the assessment results and enabling decision-makers to better understand and manage risks. For example, Monte Carlo simulation can be used. By randomly sampling future changes in environmental parameters (based on historical data or predicted distributions) and running the environment-equipment coupled aging model multiple times, a series of possible remaining lifetime values ​​are obtained, and the confidence interval for the remaining lifetime is then statistically calculated. Alternatively, fuzzy logic or interval analysis methods can be used to represent the uncertainty of environmental parameters as fuzzy numbers or interval numbers. Through fuzzy inference or interval operations, a fuzzy estimate or interval estimate of the remaining lifetime is obtained, thereby determining its confidence interval.

[0101] Outputting the remaining lifetime and the confidence interval provides users with more comprehensive and reliable equipment status information. The remaining lifetime value provides the best estimate, while the confidence interval reveals the range of uncertainty in that estimate, helping users weigh risks and benefits when formulating maintenance strategies. For example, the estimated remaining lifetime and the upper and lower limits of the confidence interval can be displayed numerically and graphically (e.g., bar charts, probability density plots) through a user interface (such as a visualization dashboard in a digital twin platform). Simultaneously, the estimated remaining lifetime and confidence interval data can also be provided to other systems (such as asset management systems and maintenance scheduling systems) via API interfaces or data reports for further analysis and decision-making.

[0102] By introducing a remaining useful life (RWW) assessment mechanism and addressing environmental uncertainties, this application effectively solves the problem of low accuracy in RWW assessment. Specifically, RWW is calculated based on an environment-equipment coupled aging model. This model's ability to model the interaction between the environment and equipment ensures that the RWW calculation fully integrates the nonlinear effects of environmental stress on aging, thus avoiding the bias caused by relying on fixed models in traditional methods. RWW is dynamically updated based on material performance degradation and aging rate. By adjusting the RWW value in real time, it adapts to changes in material state and rate during equipment aging, improving the real-time performance and accuracy of the assessment. The confidence interval for RWW is calculated considering the uncertainties of environmental changes, quantifying the risks brought by environmental fluctuations, providing a possible range of RWW, and enhancing the reliability and robustness of the assessment results. Outputting the RWW and confidence interval allows users to obtain more comprehensive equipment status information, facilitating the formulation of scientific maintenance decisions. This design upgrades the remaining life assessment from "single-point prediction" to "dynamic prediction with risk boundaries," ensuring the consistency between life calculation and the environmental coupling model, and enabling the explicit expression of uncertainty through confidence intervals. This makes equipment condition prediction more closely match the complex working conditions on site, significantly enhancing the reliability and decision support capabilities of the early warning system.

[0103] In some of the solutions mentioned above in this application, a service life adjustment factor is proposed to dynamically adjust the early warning threshold. However, in this process, how to accurately quantify the value of the adjustment factor according to the specific service stage of the equipment in order to optimize the accuracy of the early warning and reduce the risk of false alarms and missed alarms has become a key problem that needs to be solved.

[0104] In response, this application further proposes a service life adjustment factor. Determined based on the stage of service: During the new operational period Take a value between 0.90 and 0.95; during the stable operation period, Take 1.0; during the aging period, Take a value between 0.85 and 0.90.

[0105] Specifically, service life adjustment factor This is a multiplicative coefficient used to dynamically adjust the warning threshold. Its core function is to reflect the inherent aging characteristics and risk sensitivity of the cable branch box at different service stages. Through this factor, the warning system can differentiate and correct the initial warning threshold according to the actual "age" and health status of the equipment, thereby ensuring that the warning threshold closely matches the life cycle status of the cable branch box and avoiding inaccurate warnings caused by different degrees of equipment aging.

[0106] In practical applications, a mapping table can be pre-stored in the system, associating different service stages (e.g., new commissioning period, stable operation period, aging period) with the corresponding service life adjustment factor value range. When the system identifies a cable branch box in a certain service stage, it queries the table and applies the corresponding adjustment factor. Alternatively, this can be implemented using a rule-based expert system. This system determines the service stage of the cable branch box based on its service life and outputs the corresponding service life adjustment factor according to preset logical rules.

[0107] Service life adjustment factor during the new commissioning period Use a value between 0.90 and 0.95. In the initial stage of commissioning a new cable branch box, although the overall performance of the equipment is good, there may be manufacturing defects, installation problems, or instability factors during the initial break-in period. Setting the threshold to less than 1 means the warning threshold will be more stringent than the initial threshold. This stringency aims to improve the sensitivity of the warning system, enabling the timely detection of potential early-stage faults during the initial operation of the equipment. For example, in the first 1-2 years after equipment commissioning, the system will automatically apply an adjustment factor of 0.90 to 0.95. This helps provide stronger protection before equipment performance is fully stable or potential defects are fully exposed; even slight abnormal fluctuations may trigger warnings, allowing for the timely detection and resolution of potential manufacturing defects or installation problems. Alternatively, by analyzing historical data, if initial fluctuations in specific parameters (such as partial discharge or temperature rise) are found in newly commissioned equipment, using an adjustment factor of 0.90 to 0.95 will make the warning system more sensitive to these initial fluctuations, prompting maintenance personnel to conduct more thorough inspections and adjustments, thus avoiding the missed detection of early faults.

[0108] During stable operation, service life adjustment factor Use 1.0. After the cable branch box enters a stable operating phase, the equipment performance tends to stabilize, and the aging rate is relatively gradual. Setting it to 1.0 means the warning threshold will remain consistent with the initial threshold. For example, when the cable distribution box is between 2 and 15 years old, the system will... The setting is 1.0. This reflects that the equipment is in normal operation within its design life at this stage, requiring neither overly sensitive nor overly lenient early warning strategies, thus ensuring the effectiveness of early warnings while reducing unnecessary false alarms. Alternatively, after the equipment has undergone initial break-in and there are no major failure records, the system will restore the adjustment factor to 1.0. This indicates that the equipment has entered its expected performance range, and the early warning system can make judgments based on standard thresholds to maintain the stability and reliability of early warnings.

[0109] During the aging period, the service life adjustment factor Use a value between 0.85 and 0.90. When cable branch boxes enter their aging period, their material properties begin to degrade significantly, increasing the risk of failure. Setting the threshold to a smaller range means the warning threshold will be further tightened. This tightening is designed to adapt to the reality of declining equipment performance and effectively improve the ability to identify faults in aging equipment. For example, when a cable branch box has been in service for more than 15 years, the system will automatically apply an adjustment factor of 0.85 to 0.90. This stricter threshold can better adapt to the reality of declining equipment performance; even slight performance fluctuations may indicate potential fault risks, thereby effectively improving the ability to identify faults in aging equipment and preventing missed detections due to excessively high thresholds. Alternatively, the system will activate this adjustment factor when the cumulative operating time of the equipment reaches more than 80% of its design life. This helps to provide more refined risk management as the equipment enters the end of its life cycle. For example, for problems such as insulation aging and increased contact resistance, a stricter threshold can prompt maintenance personnel to plan maintenance or replacement in advance, avoiding sudden failures.

[0110] The above technical solution clearly defines the service life adjustment factor for cable branch boxes at different service stages. The specific values ​​address the technical issues of inaccurate quantification of adjustment factors and mismatch with the actual aging stages of equipment during threshold adjustment, thus hindering the balance between false alarm and missed alarm rates. Specifically, the adjustment factors are segmented into new commissioning, stable operation, and aging periods, ensuring that the adjustment of the warning threshold fully aligns with the performance changes throughout the equipment's lifecycle. This achieves precise quantification of the adjustment factors and eliminates the arbitrariness and biases introduced by experience-based settings. During the new commissioning period, an adjustment factor of 0.90 to 0.95 is used, making the warning threshold more stringent than the initial value. This allows for timely detection of potential manufacturing defects or installation problems in the early stages of equipment operation, preventing early fault misses. During the stable operation period, a standard adjustment factor of 1.0 is used to maintain the threshold at a reasonable level, ensuring warning sensitivity while reducing unnecessary false alarms. During the aging period, an adjustment factor of 0.85 to 0.90 is used to further tighten the threshold, adapting to the accelerated aging and performance degradation of the equipment, effectively improving risk identification capabilities and preventing serious fault misses due to overly lenient thresholds. This phased, quantitative adjustment method with clearly defined numerical ranges provides a clear engineering basis for threshold adjustments, ensuring reasonable early warning sensitivity throughout the equipment's entire lifecycle and fundamentally improving early warning accuracy. This segmented value design fully matches the typical aging characteristics of cable branch boxes, upgrading dynamic threshold adjustment from qualitative optimization to a implementable and reproducible quantitative execution plan. This significantly enhances the stability and practicality of the early warning system throughout its lifecycle, providing clear and executable threshold judgment standards for on-site operation and maintenance.

[0111] To verify the effectiveness of the technical solution in this application, 50 cable distribution boxes in a coastal area were selected for a two-year actual operation test. The test subjects were divided into two groups: Control group: 25 units, using the traditional fixed threshold early warning method. Experimental group: 25 units, using the dynamic threshold early warning method proposed in this application. Table 1: Comparison Data of Early Warning Performance

[0112] Early warning accuracy 72.3% 91.5% +26.6% False alarm rate 18.7% 5.2% -72.2% underreporting rate 9.0% 3.3% -63.3% Average warning lead time 2.3 days 5.7 days +147.8% Equipment availability 96.8% 98.5% +1.8% Table 2: Examples of ESI Calculation under Different Environmental Conditions

[0113] Clean the inland 25 60 0.1 0.05 0.28 Coastal salt spray 30 85 0.8 0.3 0.61 Industrial pollution 28 70 0.2 0.9 0.52 Extreme Environment 35 95 1.2 1.5 0.78 Table 3: Examples of Application of Adjustment Factors at Different Service Stages

[0114] CB-001 1 year New commissioning period 0.92 0.35 0.95 100°C 87.4°C CB-015 8 years Stable operation period 1.00 0.42 0.93 100°C 93.0°C CB-023 18 years Aging period 0.88 0.55 0.90 100°C 79.2°C The experimental data above show that the technical solution of this application is significantly better than traditional methods in terms of key indicators such as early warning accuracy, false alarm rate, and false alarm rate, verifying the effectiveness of the quantitative modeling of environmental coupling effects and the dynamic threshold adjustment mechanism.

[0115] Example 2

[0116] Traditional digital twin early warning systems for cable branch boxes primarily focus on the equipment's own operating parameters, failing to fully integrate the accelerating effect of environmental stress on equipment degradation, resulting in significant deviations in risk prediction under complex operating conditions. Furthermore, the commonly used fixed early warning thresholds cannot adapt to changes in the equipment's condition throughout its entire lifecycle, easily leading to false alarms and missed alarms. In addition, the single data source and lack of external information integration result in insufficient timeliness and accuracy of early warnings, and low precision in remaining life assessment.

[0117] In response, this application proposes an electronic device including at least one processor and a memory. The memory stores instructions, which, when executed by the at least one processor, cause the processor to perform the aforementioned digital twin early warning method for cable branch boxes that integrates environmental coupling effects. The core innovation of this embodiment lies in combining the calculation of environmental stress index with the construction of an environment-equipment coupled aging model, and introducing a dynamic early warning threshold adjustment mechanism based on service life and environmental stress index. This quantifies the accelerating effect of the environment on equipment degradation and adapts to changes in equipment status throughout its entire life cycle, thereby reducing false alarm and false negative rates and improving the accuracy of remaining life assessment.

[0118] In some of the embodiments described above in this application, an environmental stress index calculation and early warning threshold adjustment mechanism is proposed. However, during its implementation, it is necessary to ensure that the hardware device can efficiently perform multi-source data fusion and dynamic model calculation. Specifically, at least one processor is used to provide real-time computing capabilities to execute the core logic in the cable branch box digital twin early warning method, including the processing of operational data, environmental data, and external data. A memory is used to store the instruction set of the early warning method, which defines the complete process of data acquisition, environmental stress index calculation, environmental-equipment coupled aging model construction, and dynamic adjustment of the early warning threshold, ensuring the deployability and persistence of the method.

[0119] Furthermore, when an instruction is executed by at least one processor, the processor first acquires operational data, environmental data, and external data from the cable distribution box. Operational data is periodically collected by sensors deployed inside the cable distribution box; for example, temperature sensors monitor internal temperature changes in real time, and current sensors record load current fluctuations. Environmental data is acquired through surrounding environmental monitoring devices; for example, temperature and humidity sensors collect air temperature and humidity parameters. External data is accessed in real time from meteorological satellite systems or power grid dispatching platforms; for example, weather forecast information is used to predict the impact of extreme weather. This data is synchronously transmitted to a memory as the basis for subsequent analysis.

[0120] Based on this, the processor calculates the environmental stress index using environmental data. Specifically, the environmental stress index is obtained by comprehensively quantifying parameters such as ambient temperature, humidity, and air pollutant concentration. For example, a weighted summation algorithm is used to multiply each environmental parameter by its preset weight and then sum them up to obtain a numerical index characterizing the overall impact of the environment on the equipment. This index directly reflects the accelerating effect of environmental stresses such as salt spray and chemical corrosion on the deterioration of cable branch boxes, providing key input for subsequent model construction.

[0121] Furthermore, the processor constructs an environment-equipment coupled aging model based on the environmental stress index. This model describes the coupling effect between the environmental stress index and the equipment aging rate through mathematical relationships. For example, it uses nonlinear functions to fit historical data and establishes a mapping relationship between the environmental stress index and the aging rate of insulation materials. After calculating the current environmental stress index, the model can dynamically output the corresponding aging acceleration factor, thereby quantifying the differences in aging rates under different environmental conditions (such as high-salt-fog environments on islands and clean inland environments), significantly improving the accuracy of remaining life assessment.

[0122] Meanwhile, the processor dynamically adjusts the early warning threshold based on the service life of the cable distribution box and the environmental stress index. Specifically, the service life serves as an identifier for the equipment's life cycle stage, dividing the equipment into initial, mid-term, and final operation phases, with each phase having a preset basic early warning threshold adjustment factor. The environmental stress index is used to correct this factor in real time; for example, when the environmental stress index exceeds the threshold, the adjustment factor is automatically reduced to tighten the early warning threshold. Thus, the initial early warning threshold, after dual dynamic correction, forms a threshold adapted to the current state of the equipment, effectively avoiding the problems of high false alarm rates in the early stages of service and high false alarm rates in the late stages of service.

[0123] Finally, the processor compares the real-time monitored operating data with the dynamically adjusted warning thresholds and outputs warning information based on the prediction results of the environment-equipment coupled aging model. When operating parameters exceed the adjusted thresholds or the model predicts an abnormal aging rate, the system displays a warning sign on the monitoring interface or sends a warning notification to maintenance personnel via the communication module. Through the above technical solution, this application achieves deep coupling analysis of environmental stress and equipment status, integrates multi-source data to improve the timeliness of warnings, significantly reduces the false alarm and missed alarm rates through the dynamic threshold mechanism, and provides accurate remaining life basis for spare parts scheduling, thereby comprehensively improving the reliability and intelligence level of the cable branch box digital twin warning system.

[0124] Example 3

[0125] This invention provides a computer-readable storage medium.

[0126] The computer-readable storage medium provided in this embodiment of the invention stores a computer program, which, when executed by a processor, can implement the steps of the above-described digital twin early warning method for cable branch boxes that incorporates environmental coupling effects: Acquire operational, environmental, and external data for the cable branch box; Calculate the environmental stress index based on the environmental data; An environment-equipment coupled aging model is constructed based on the environmental stress index. The early warning threshold is dynamically adjusted based on the service life of the cable branch box and the environmental stress index. The warning results are output based on the environment-equipment coupled aging model and the adjusted warning threshold.

[0127] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0128] For a description of the computer-readable storage medium provided in the embodiments of the present invention, please refer to the above method embodiments; the present invention will not be described in detail here.

[0129] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0130] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0131] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0132] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A digital twin early warning method for cable branch boxes that integrates environmental coupling effects, characterized in that, The method includes: Acquire operational, environmental, and external data for the cable distribution box; Calculate the environmental stress index based on the environmental data; An environment-equipment coupled aging model is constructed based on the environmental stress index. The early warning threshold is dynamically adjusted based on the service life of the cable branch box and the environmental stress index. The warning results are output based on the environment-equipment coupled aging model and the adjusted warning threshold.

2. The digital twin early warning method for cable branch boxes based on the fusion of environmental coupling effects as described in claim 1, characterized in that, The calculation of the environmental stress index based on the environmental data includes: The environmental data is normalized to obtain normalized environmental parameters, wherein the normalization process uses the following formula: in, To normalize environmental parameters, These are the current environmental parameter values. This represents the minimum value of the environmental parameters. This represents the maximum value of the environmental parameter. The environmental stress index is obtained by weighted summation of the normalized environmental parameters based on preset weights, wherein the preset weights are determined by the analytic hierarchy process based on the significance of the influence of each environmental parameter on the aging of the cable branch box.

3. The digital twin early warning method for cable branch boxes based on the fusion of environmental coupling effects as described in claim 1, characterized in that, The construction of the environment-equipment coupled aging model based on the environmental stress index includes: A mapping relationship is established between the environmental stress index and the aging rate of the cable branch box material, wherein the material aging rate is calculated in the following manner: in, As of the current aging rate, As the baseline aging rate, For environmental acceleration factor, Environmental stress index; The material performance degradation is calculated based on the mapping relationship and the historical integral of the environmental stress index.

4. The digital twin early warning method for cable branch boxes based on the fusion of environmental coupling effects according to claim 1, characterized in that, The dynamic adjustment of the early warning threshold based on the service life of the cable branch box and the environmental stress index includes: The service stage of the cable branch box is determined based on its service life, and the service stage includes the new commissioning period, the stable operation period, and the aging period. The threshold adjustment coefficient is determined based on the service stage and the environmental stress index, wherein the adjusted warning threshold is calculated using the following formula: in, for The warning threshold is adjusted in real time. The initial warning threshold, This is a service life adjustment factor. Environmental stress adjustment factor; The adjusted warning threshold is calculated based on the initial warning threshold and the threshold adjustment coefficient.

5. The digital twin early warning method for cable branch boxes based on the fusion of environmental coupling effects according to claim 1, characterized in that, The external data includes weather forecast data and power grid dispatch data; The method further includes: The operational data, environmental data, and external data are time-aligned, spatially registered, and weighted and fused to obtain fused state data. The weighted fusion is based on the reliability of the data source and the accuracy of the sensor.

6. The digital twin early warning method for cable branch boxes based on the fusion of environmental coupling effects according to claim 4, characterized in that, The method also includes a threshold self-learning mechanism: The threshold adjustment coefficient is automatically optimized based on historical early warning accuracy feedback. The threshold self-learning mechanism aims to reduce the false positive rate and false negative rate, and is achieved by minimizing the following objective function: in, The objective function value, For false alarm rate, The false negative rate, and These are the weighting coefficients.

7. The digital twin early warning method for cable branch boxes based on the fusion of environmental coupling effects according to claim 1, characterized in that, The method further includes: The remaining life of the cable branch box is calculated based on the environment-equipment coupled aging model, and the remaining life is dynamically updated based on the material performance degradation and aging rate. Considering the uncertainty of environmental changes, calculate the confidence interval of the remaining lifetime, and output the remaining lifetime and the confidence interval.

8. The digital twin early warning method for cable branch boxes based on the fusion of environmental coupling effects according to claim 4, characterized in that, The service life adjustment factor Determined based on the aforementioned service stage: During the new commissioning period, Take a value between 0.90 and 0.95; During the stable operation period, Set the value to 1.0; During the aging period, Take a value between 0.85 and 0.

90.

9. An electronic device, comprising: At least one processor; A memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the cable branch box digital twin early warning method for incorporating environmental coupling effects as described in any one of claims 1-8.

10. A non-transitory machine-readable storage medium storing executable instructions that, when executed, cause a machine to perform a digital twin early warning method for cable branch boxes that incorporates environmental coupling effects as described in any one of claims 1-8.

Images