A power distribution asset operation quality assessment and management system

Abstract

This invention relates to the field of power distribution asset management technology and discloses a power distribution asset operation quality assessment and management system, including a data standardization processing module, an asset status modeling module, a weight adaptive adjustment module, and a standardized quality evaluation module. The data standardization processing module acquires asset operation parameter data and maintenance work order data. The asset status modeling module calculates the time-domain excess integral of parameters deviating from the health threshold range and uses this proportion as an allocation factor to allocate the standard resource consumption equivalent to each indicator, determining the standardized cost allocation value. The weight adaptive adjustment module updates the weights based on the cost allocation value feedback deviation. The standardized quality evaluation module generates an evaluation index and outputs hierarchical management instructions. This invention establishes a standardized resource consumption attribution allocation mechanism, eliminates the time lag deviation between asset deterioration stress and discrete maintenance costs, and guides operation and maintenance resources to be accurately allocated to high-sensitive risk nodes.

Description

Technical Field

[0001] This invention relates to the field of power distribution asset management technology, and specifically to a power distribution asset operation quality assessment and management system. Background Technology

[0002] The current assessment of the operational quality of distribution transformers, switchgear, and cable distribution boxes typically uses online monitoring systems to acquire physical operational parameters such as voltage, current, and temperature rise. The evaluation method uses mathematical models to convert heterogeneous operational data into quantitative scores, achieving standardized benchmarking of asset status. The deterioration of physical assets manifests as a cumulative stress process in the time domain, with its operational fluctuations exhibiting high-frequency and continuous characteristics. However, data related to asset management, such as maintenance work order costs, spare parts replacement costs, and scheduling resource expenditures, are stored in the enterprise resource planning system, exhibiting low-frequency, discrete, and lagging characteristics compared to physical events. Due to the structural misalignment between physical operational signals and management cost data in terms of time scale and data logic, the assessment system struggles to accurately calculate the true driving weight of individual physical parameters on the input of operation and maintenance resources.

[0003] Besides the limitations of hardware monitoring, software control methods have shortcomings in attributing cost drivers under dynamic operating conditions. Chinese invention patent application CN119742766A discloses a distribution network reliability assessment system and method that predicts fault probability by monitoring environmental temperature and humidity deviations and combining them with historical fault records. This technology is based on static standard environmental probability mapping and fails to address the logical gap between accumulated physical damage and delayed management feedback. Weight allocation is based on empirical presets and cannot identify the driving effect of environmental sensitivity on management costs. The lack of attribution paths causes the evaluation results to deviate from the true cost drivers of assets, lacking adaptive correction capabilities under complex environmental changes and failing to support lean management throughout the entire lifecycle of distribution assets. To solve the above data misalignment problem, the industry typically attempts to increase sampling frequency or introduce predictive models. However, simple linear mapping cannot reveal the accumulated damage caused by multiple physical stress couplings, nor can it trace sudden discrete cost events to specific time-domain fluctuation ranges. This lack of attribution paths between physical monitoring and management feedback often causes the evaluation weight system to deviate from the true cost drivers of assets, lacking adaptive correction capabilities in the face of complex environmental changes.

[0004] Therefore, how to construct a standardized evaluation logic based on the equivalent of management resource consumption, realize the adaptive reconstruction of evaluation weights under dynamic working conditions, and benchmark across asset types has become the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention proposes a power distribution asset operation quality assessment and management system, comprising: The data standardization processing module is used to acquire physical operation parameter data and maintenance work order data of power distribution assets. The maintenance work order data records the standard resource consumption equivalent. The asset status modeling module is used to extract the time-domain segments that deviate from the preset health interval boundaries of each evaluation index based on physical operation parameter data, calculate the integral value of the time-domain segments with respect to time to obtain the time-domain over-limit integral amount, and use the proportion of the time-domain over-limit integral amount of a single parameter in the total integral amount of all-dimensional indicators as the cost allocation factor to allocate the standard resource consumption equivalent to each evaluation index in order to determine the standardized operation and maintenance cost allocation value driven by a single parameter. The weight adaptive adjustment module is used to set the initial weights of each evaluation indicator based on the preset environmental sensitivity coefficient, and to normalize and update the weights of each evaluation indicator using an exponential adjustment function based on the feedback deviation of the standardized operation and maintenance cost allocation value relative to the preset cost benchmark. The standardized quality evaluation module is used to generate an assessment index that characterizes the risk of uncontrolled asset operation and maintenance costs based on updated weights and real-time collected physical operation parameter data. By comparing the assessment index with preset management standard thresholds, it outputs hierarchical management instructions for the corresponding asset maintenance priorities.

[0006] Preferably, the asset status modeling module performs the following logical steps when determining the standardized operation and maintenance cost allocation value driven by each evaluation indicator: Step S21, for the traceability time window before any maintenance work order is generated, extract the sampling sequence of each evaluation indicator in the physical operation parameter data and remove sampling outliers; Step S22, identify the over-limit amplitude in the sampling sequence that exceeds the boundary of the healthy interval, and perform an integral operation on the over-limit amplitude over time to obtain the time-domain over-limit integral quantity used to objectively characterize the accumulated physical damage stress; Step S23, calculate the weight ratio of the time-domain over-limit integral quantity of a single parameter in the total integral quantity of all-dimensional indicators, and decompose the total standard resource consumption equivalent corresponding to the maintenance work order according to the weight ratio, thereby mapping the discrete cost data to the continuous physical parameter fluctuation process.

[0007] Preferably, the weight adaptive adjustment module includes an environmental impact analysis unit, which is used to monitor the environmental operating parameters of the area where the power distribution asset is located, and determine the operating condition acceleration coefficient of each evaluation index based on the environmental operating parameters, and use the operating condition acceleration coefficient to adjust the environmental sensitivity coefficient to update the initial weight.

[0008] Preferably, the standardized quality evaluation module includes a risk conversion unit, which is used to map the evaluation index into a probability value of uncontrolled operation and maintenance costs, so as to guide operation and maintenance resources to be deployed to highly sensitive risk nodes.

[0009] Preferably, the data standardization processing module includes a standardization benchmarking unit, which is used to transform the data characteristics of heterogeneous power distribution assets into evaluation parameters of a unified dimension.

[0010] Preferably, the asset state space modeling module is also used to generate corrected weights by calculating the dispersion of physical operation parameter data on the time axis, and to use the corrected weights to perform weighted compensation for the time-domain over-limit integral.

[0011] Preferably, the weight adaptive adjustment module includes a feedback correction unit, which receives the evaluation index output by the standardized quality evaluation module and adjusts the weight of each evaluation indicator exponentially according to the deviation between actual resource consumption and predicted consumption within a preset audit period.

[0012] Preferably, when the standardized quality evaluation module outputs hierarchical management instructions, it divides the power distribution assets into four management levels according to the numerical level of the evaluation index, and matches the corresponding standardized operation and maintenance procedures for each management level.

[0013] Preferably, it also includes a resource allocation optimization module, which is connected to the standardized quality evaluation module, and is used to automatically generate a maintenance plan list and spare parts allocation suggestions according to hierarchical management instructions, so as to suppress redundant investment in standardized services.

[0014] The beneficial effects of this invention are: 1. In the management of power distribution asset operation quality assessment, this invention aims to achieve standardized and commensurate benchmarking of heterogeneous power distribution assets across management dimensions. Since power distribution networks encompass a vast array of heterogeneous equipment, including transformers and switchgear, the physical monitoring parameters of each device exhibit significant differences in time-domain frequency and dimensional attributes. This makes it difficult for traditional systems to establish a unified evaluation benchmark. This invention constructs a cost driver allocation mechanism based on time-domain limit integral quantities, transforming high-frequency continuous physical operation signals into standard resource consumption equivalents based on historical maintenance work orders. This maps the complex electrical degradation process into financially supported management costs. This mechanism eliminates the incomparability of heterogeneous assets at the physical representation level, enabling horizontal benchmarking of assets of different types and regions under a unified management resource scale, thus solving the long-standing problem of inaccurate evaluation scales in standardized assessment systems.

[0015] 2. Constructing a standardized weight adaptive reconstruction path with self-evolution capabilities: Traditional evaluation systems often rely on static experience to preset weights, which cannot adapt to the migration of fault mechanisms caused by sudden environmental changes or equipment aging. This invention relies on the indicator importance mapping operator to capture the resource sensitivity of meteorological and environmental parameters to each operating indicator, and combines the maintenance feedback deviation generated by actual work orders to exponentially adjust and normalize the evaluation weights. This closed-loop correction mechanism enables the system to autonomously remove the interference of static experience, accurately identify the core indicators driving the consumption of management resources under the current operating conditions, ensure that the standardized evaluation results are always highly consistent with the actual deterioration cost of assets, and improve the decision robustness of the system under nonlinear evolution conditions.

[0016] 3. Achieving precise matching between power grid operation and maintenance resource input and asset management needs: The standardized operation quality index generated by this invention directly represents the probability distribution of asset operation and maintenance cost runaway, transforming the abstract electrical health into a quantifiable management risk indicator. By comparing this index with preset management standard thresholds, the system's output hierarchical management decision suggestions can guide operation and maintenance resources to be precisely deployed to risk nodes with high management cost sensitivity, avoiding resource misallocation caused by uneven weight distribution in traditional preventive maintenance models. While ensuring the consistency of power grid operation, it reduces unnecessary standardized service redundancy expenditures and improves the lean level of distribution asset life cycle management. Attached Figure Description

[0017] 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.

[0018] Figure 1 This is the architecture and data flow diagram of the power distribution asset operation quality assessment system of the present invention; Figure 2 This is the closed-loop control logic diagram for the status evolution and operation and maintenance feedback of power distribution assets in this invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] A power distribution asset operation quality assessment and management system, comprising: The data standardization processing module is used to acquire physical operation parameter data and maintenance work order data of power distribution assets. The maintenance work order data records the standard resource consumption equivalent. The asset status modeling module is used to extract the time-domain segments that deviate from the preset health interval boundaries of each evaluation index based on physical operation parameter data, calculate the integral value of the time-domain segments with respect to time to obtain the time-domain over-limit integral amount, and use the proportion of the time-domain over-limit integral amount of a single parameter in the total integral amount of all-dimensional indicators as the cost allocation factor to allocate the standard resource consumption equivalent to each evaluation index in order to determine the standardized operation and maintenance cost allocation value driven by a single parameter. The weight adaptive adjustment module is used to set the initial weights of each evaluation indicator based on the preset environmental sensitivity coefficient, and to normalize and update the weights of each evaluation indicator using an exponential adjustment function based on the feedback deviation of the standardized operation and maintenance cost allocation value relative to the preset cost benchmark. The standardized quality evaluation module is used to generate an assessment index that characterizes the risk of uncontrolled asset operation and maintenance costs based on updated weights and real-time collected physical operation parameter data. By comparing the assessment index with preset management standard thresholds, it outputs hierarchical management instructions for the corresponding asset maintenance priorities.

[0021] Preferably, the asset status modeling module performs the following logical steps when determining the standardized operation and maintenance cost allocation value driven by each evaluation indicator: Step S21, for the traceability time window before any maintenance work order is generated, extract the sampling sequence of each evaluation indicator in the physical operation parameter data and remove sampling outliers; Step S22, identify the over-limit amplitude in the sampling sequence that exceeds the boundary of the healthy interval, and perform an integral operation on the over-limit amplitude over time to obtain the time-domain over-limit integral quantity used to objectively characterize the accumulated physical damage stress; Step S23, calculate the weight ratio of the time-domain over-limit integral quantity of a single parameter in the total integral quantity of all-dimensional indicators, and decompose the total standard resource consumption equivalent corresponding to the maintenance work order according to the weight ratio, thereby mapping the discrete cost data to the continuous physical parameter fluctuation process.

[0022] Preferably, the weight adaptive adjustment module includes an environmental impact analysis unit, which is used to monitor the environmental operating parameters of the area where the power distribution asset is located, and determine the operating condition acceleration coefficient of each evaluation index based on the environmental operating parameters, and use the operating condition acceleration coefficient to adjust the environmental sensitivity coefficient to update the initial weight.

[0023] Preferably, the standardized quality evaluation module includes a risk conversion unit, which is used to map the evaluation index into a probability value of uncontrolled operation and maintenance costs, so as to guide operation and maintenance resources to be deployed to highly sensitive risk nodes.

[0024] Preferably, the data standardization processing module includes a standardization benchmarking unit, which is used to transform the data characteristics of heterogeneous power distribution assets into evaluation parameters of a unified dimension.

[0025] Preferably, the asset state space modeling module is also used to generate corrected weights by calculating the dispersion of physical operation parameter data on the time axis, and to use the corrected weights to perform weighted compensation for the time-domain over-limit integral.

[0026] Preferably, the weight adaptive adjustment module includes a feedback correction unit, which receives the evaluation index output by the standardized quality evaluation module and adjusts the weight of each evaluation indicator exponentially according to the deviation between actual resource consumption and predicted consumption within a preset audit period.

[0027] Preferably, when the standardized quality evaluation module outputs hierarchical management instructions, it divides the power distribution assets into four management levels according to the numerical level of the evaluation index, and matches the corresponding standardized operation and maintenance procedures for each management level.

[0028] Preferably, the asset status modeling module follows the following quantitative accounting rules when calculating the standardized operation and maintenance cost allocation value driven by a single parameter: ,in, The standardized operation and maintenance cost allocation value assigned to the i-th evaluation indicator. This corresponds to the total standard resource consumption equivalent in the maintenance work order. Let be the time-domain limit-crossing integral of the i-th evaluation indicator within a specific retrospective time window, and n be the total number of evaluation indicators participating in the evaluation.

[0029] Preferably, it also includes a resource allocation optimization module, which is connected to the standardized quality evaluation module, and is used to automatically generate a maintenance plan list and spare parts allocation suggestions according to hierarchical management instructions, so as to suppress redundant investment in standardized services.

[0030] Example 1: In the scenario of continuous operation of a regional power grid containing heterogeneous power distribution assets such as multiple types of distribution transformers and switchgear, the power distribution asset operation quality assessment and management system needs to address the migration of equipment degradation mechanisms caused by alternating extreme environmental changes. Traditional assessment systems use static preset weights, which cannot objectively trace the fluctuations of high-frequency continuous physical parameters to low-frequency discrete delayed maintenance costs, leading to misallocation of operation and maintenance resources and uncontrolled management costs. This example relies on the architecture of the power distribution asset operation quality assessment and management system. The data standardization processing module obtains the physical operation parameter data of the power distribution assets and maintenance work order data containing the equivalent of standard resource consumption, and initiates a standardized accounting process based on cost drivers to resolve the structural misalignment between the time domain of heterogeneous physical signals and the time domain of management settlement.

[0031] The asset status modeling module extracts the sampling sequences of each evaluation index from the physical operation parameter data within a traceability time window set before any maintenance work order is generated. It then removes outliers and identifies out-of-limit amplitudes exceeding the preset health interval boundaries. These out-of-limit amplitudes are integrated along the time axis to calculate the time-domain out-of-limit integral quantity, which objectively represents the cumulative physical damage stress. Based on the linear cumulative damage theory, heterogeneous physical parameters with different dimensions are strictly prohibited from direct algebraic addition. The module retrieves the statistical integral of each evaluation index when it reaches the functional failure critical point from the local historical degradation sample library. The average value is set as the baseline damage tolerance. The real-time acquired time-domain excess limit integral is divided by the corresponding baseline damage tolerance to calculate and output the dimensionless equivalent damage factor. Based on this, the system calculates the proportion of the time-domain excess limit integral of a single parameter in the total integral of all-dimensional indicators. This proportion is determined as the cost allocation factor. According to this cost allocation factor, the total standard resource consumption equivalent in the corresponding maintenance work order is decomposed to determine the standardized operation and maintenance cost allocation value driven by the single parameter. The quantitative accounting rule for the standardized operation and maintenance cost allocation value allocated to the i-th evaluation indicator satisfies the formula: ,in, The standardized operation and maintenance cost allocation value assigned to the i-th evaluation indicator. This corresponds to the total standard resource consumption equivalent in the maintenance work order. Let be the time-domain limit-breaking integral of the i-th evaluation indicator within a specific retrospective time window, and n be the total number of evaluation indicators participating in the assessment. This quantitative accounting procedure transforms the complex electrical degradation process into a definite financial allocation ledger, establishing a management benchmark for power distribution asset assessment.

[0032] The weight adaptive adjustment module adjusts the preset environmental sensitivity coefficient based on the environmental operating condition parameters of the distribution asset area monitored by the environmental impact analysis unit, combined with the operating condition acceleration coefficient of each evaluation indicator, and sets the initial weight of each evaluation indicator. The system extracts the standardized operation and maintenance cost allocation value generated in the previous steps, compares it with the preset cost benchmark to calculate the feedback deviation, and uses the system's embedded exponential adjustment function to normalize and update the weight of each evaluation indicator. The specific update logic satisfies the formula: ,in, For the updated number The weight of each evaluation indicator Let i be the weight of the i-th evaluation indicator at the current time. Let j be the weight of the j-th evaluation indicator at the current time. and The environmental sensitivity coefficients for each evaluation indicator represent the management and maintenance costs driven by the cumulative deviation per unit. E represents the maintenance feedback deviation. This step establishes the attribution path between upstream physical stress and downstream management feedback, enabling the standardized evaluation weights to autonomously remove static interference and complete adaptive reconstruction under dynamic operating conditions. It should be clearly pointed out that the variable symbols used to represent the environmental sensitivity coefficients in this formula are... Its essence is not an independently generated new variable, but rather the time-domain over-limit integral output from the aforementioned accounting steps. The dimensionless feature operator is mapped by performing an inner product operation with the offline calibrated system constants; the system ensures the physical dimension integral accumulation in the underlying data flow logic by establishing a definite logical mapping relationship. It can directly and continuously serve as the driving force for index regulation, inheriting and transforming into the environmental sensitivity coefficient. The standardized quality evaluation module acquires real-time physical operation parameter data and aggregates and weights the aforementioned parameter set according to updated weights to generate an assessment index characterizing the risk of uncontrolled asset operation and maintenance costs. This assessment index acts as a standardized management benchmark mapping the probability of uncontrolled operation and maintenance costs. Based on a logistic regression classification model, the assessment index with continuous unbounded attributes is extracted and input into the Sigmoid activation function module. Through exponential smoothing and compression operations, it is transformed and locked within the open interval of 0 to 1, outputting a standardized probability value of uncontrolled operation and maintenance costs. This logistic regression classification model was trained using historical operation files of the same model of equipment before system deployment. Its input feature vector is directly constructed by concatenating the standardized operation and maintenance cost allocation value output by the asset status modeling module with the environmental condition parameter matrix. The corresponding classification labels are extracted from enterprise-level... The database records the binary data of whether the equipment in the ledger has triggered an overspending of maintenance costs (overspending is marked as 1, and not overspending is marked as 0). The gradient descent algorithm is used to iteratively optimize until the loss function converges, thereby establishing a statistically based mapping weight between the assessment index and the actual out-of-control state. The system compares the assessment index with the preset management standard threshold, divides the power distribution assets into 4 management levels according to the numerical level of the assessment index, matches the corresponding standardized operation and maintenance procedures for each management level, and outputs hierarchical management instructions for the corresponding asset maintenance priority. After receiving the instructions, the resource allocation optimization module generates a maintenance plan list and spare parts allocation suggestions, guiding the operation and maintenance resources to be accurately deployed to risk nodes with high management cost sensitivity. Under a unified management resource scale, horizontal benchmarking of heterogeneous assets across regions and suppression of redundant service costs are achieved.

[0033] Example 2: In a continuous power grid test scenario involving 150 distribution transformers and 80 switchgear cabinets, a distributed data acquisition device deployed at the equipment terminal collects physical operation parameter data. This data acquisition device has a sampling frequency of no less than 10kHz and a measurement error boundary set within 0.2%. In the test environment, broadband white Gaussian noise with a signal-to-noise ratio of 15dB and power frequency harmonic interference with an amplitude of 5% are introduced to simulate real industrial electromagnetic disturbance. The data standardization processing module obtains the above-mentioned physical operation parameter data and maintenance work order data from the enterprise resource planning middleware. The asset status modeling module extracts the sampling sequence before the maintenance work order is generated. The span of the traceability time window is set by a technical trade-off between capturing the entire cycle of cumulative equipment damage and suppressing the redundant overhead of system data processing. The system establishes judgment rules based on the thermal time constant of physical equipment and the average latency of historical faults, and selects 90 consecutive days with no changes in network topology and covering the complete quarterly temperature cycle as the determined traceability time window.

[0034] Under input conditions with 15dB Gaussian white noise, the transient peak amplitudes in the physical operation parameter data undergo high-frequency distortion. The conventional discrete peak value judgment logic intercepts 312 out-of-limit signals. The asset status modeling module extracts the out-of-limit time-domain segments of each evaluation index that deviate from the preset healthy range boundary, calculates the integral value of the out-of-limit time-domain segment with respect to time, and outputs the time-domain out-of-limit integral. This integral operation relies on the time-domain surface accumulation mechanism to suppress high-frequency noise spikes superimposed on the fundamental frequency. The time-domain out-of-limit integral of a single parameter is stabilized at 45.2V·h. The system thereby removes 87% of the non-destructive transient fluctuations in the original data sequence. The system calculates the cost allocation factor based on the proportion of the time-domain out-of-limit integral of the single parameter in the total integral of all-dimensional indicators. Based on the cost allocation factor, the standard resource consumption equivalent of 5000 monetary units in the maintenance work order data is decomposed to each evaluation index, and the standardized operation and maintenance cost allocation value driven by the single parameter is output.

[0035] To verify the gradient response law and boundary convergence characteristics of the dynamic evaluation system, a control group and multiple experimental groups with problem intensity gradients were set up in the test environment. The control group maintained the statically set weight configuration, while the experimental groups activated the weight adaptive adjustment module, set the environmental sensitivity coefficient, and compared the standardized operation and maintenance cost allocation value with the preset cost benchmark output feedback deviation. In the first environmental gradient, the daily average temperature difference was maintained at a low-order disturbance level of 5℃, and the difference between the standardized operation and maintenance cost allocation values ​​output by the experimental group and the control group was less than 3%. Switching to the second environmental gradient, the daily average temperature difference increased to 15℃. The fixed weight of the control group caused its resource prediction deviation rate to rise to 28%. The experimental group used an exponential adjustment function combined with feedback deviation to update the weights of each evaluation indicator, increasing the weight proportion of environmentally sensitive evaluation indicators. The standardized quality evaluation module generated an evaluation index based on the updated weight aggregation parameters. The fitting deviation rate of this evaluation index with the actual maintenance work order consumption converged to 4.1%. The environmental stress was pushed up to the third environmental gradient with a daily average temperature difference of 30℃. The updated evaluation index mapped the asset's deterioration cost increase state caused by temperature rise, further applying a stress exceeding 40℃. Under the boundary condition of daily average temperature difference of ℃, the test data curve shows a nonlinear inflection point characteristic. The growth slope of the evaluation index decreases as the temperature difference expands and enters a horizontal plateau region. The aging rate of the insulating medium reaches a physical saturation state after exceeding its glass transition temperature. Continuous application of thermal stress no longer generates linearly increasing management cost stress. The physical saturation state here means that in the evaluation model, when the accumulated thermal stress causes the insulating material to exceed the glass transition temperature, the equipment has essentially lost its economic repair value. The system determines that its deterioration cost state is locked at the fixed replacement cost limit value of the whole machine replacement. Therefore, the additional thermal stress exceeding this temperature threshold is no longer calculated as an increasing daily maintenance amortization cost in the financial allocation logic, but is directly converted into the final state data of fixed asset impairment, thereby triggering the highest level of asset scrapping and overall replacement warning. This nonlinear inflection point establishes the upper limit boundary of the internal factor of the index adjustment function to prevent abnormal data from causing the evaluation index to overflow. The system outputs hierarchical management instructions and issues a material allocation list based on the evaluation index, guiding operation and maintenance management resources to bypass the false alarm nodes caused by noise and to be deployed to power distribution asset units with high environmental sensitivity.

[0036] Example 3: This example combines Figures 1 to 2 A description of a power distribution asset operation quality assessment and management system, such as... Figure 1 As shown, the data standardization processing module obtains physical operation parameter data and includes standard resource consumption equivalents. The maintenance work order data provides the basic data input for the system. After receiving the data, the asset status modeling module extracts the time domain segment that exceeds the limit and calculates the time domain limit integral. This process allows for the determination of standardized operation and maintenance cost allocation values ​​driven by individual parameters. The weight adaptive adjustment module sets the initial weights based on environmental sensitivity. And based on the standardized operation and maintenance cost allocation value The feedback deviation E relative to the preset cost benchmark is normalized and updated using an exponential adjustment function to obtain the updated weights. The final standardized quality evaluation module is based on the updated weights. It aggregates real-time collected physical operation parameter data to generate an assessment index that characterizes the risk of uncontrolled asset operation and maintenance costs. By comparing the assessment index with preset management standard thresholds, it outputs hierarchical management instructions for the corresponding asset maintenance priorities.

[0037] like Figure 2 As shown, the system initially operates under normal asset conditions and its physical parameters are within safe limits. When the detected value of the operating parameter exceeds the limit, a state switch is triggered, and the system enters a physical damage stress accumulation state. In this state, the time-domain limit-exceeding integral is calculated. Once the accumulated physical damage stress has formed, a standardized operation and maintenance cost allocation process is executed, causing the system to enter a discrete operation and maintenance cost allocation state. At this time, the system combines the standard resource consumption equivalent recorded in the maintenance work order data. Determine the standardized operation and maintenance cost allocation value driven by a single parameter. When the standardized operation and maintenance cost allocation value Once determined, the system uses dynamic weight aggregation to generate an assessment index and enters the assessment index generation state. By generating a cost runaway risk assessment index and comparing it with the asset operation and maintenance cost runaway risk threshold after the assessment index is generated, the system enters the standardized maintenance response state to determine the maintenance priority and hierarchical procedures for the corresponding assets. Finally, by executing the standardized operation and maintenance procedures, the assets are restored to normal and returned to the normal operation state.

[0038] Example 4: In the operation of a coastal high-salt-fog power distribution network, continuous infiltration of ambient humidity alters the surface insulation ground state of distribution assets, causing baseline drift of physical operating parameter data along the static health interval boundary. This leads to calculation deviations in the time-domain over-limit integral, and the environmental sensitivity coefficient relied upon for weight calculation lacks statistical measurement procedures based on the physical process of equipment degradation. The data standardization processing module initiates a dynamic health baseline self-calibration algorithm to separate aging baseline drift from degradation abrupt characteristics. The data standardization processing module selects three consecutive months of historical data containing the fault-free operation state of a specific distribution transformer as a sliding calibration window. The system extracts the physical operating parameter data within this sliding calibration window, suppresses discrete noise through a moving average filtering algorithm, generates a smoothed baseline sequence, calculates the local variance of the smoothed baseline sequence, and determines the smoothed baseline sequence based on the current average value of the smoothed baseline sequence. The value is added to the product of the local variance and the preset confidence factor to generate a dynamic upper limit boundary. The false alarm rate index recorded in the historical maintenance work order data is read, and the confidence factor is calculated and updated based on the false alarm rate index. In the specific calculation, the system sets a constant of 3, which includes a predetermined margin, as the benchmark confidence factor. When the read false alarm rate index exceeds the lower limit of the system's allowed safety check band, the system dynamically reduces the product result of the confidence factor by multiplying the benchmark confidence factor by a correction coefficient composed of (1 minus the current percentage value of the false alarm rate index), thereby tightening the judgment threshold of the subsequent health interval boundary. The dynamic upper limit boundary is periodically reconstructed over time. The system sets the difference between the physical operation parameter data and the dynamic upper limit boundary as the over-limit amplitude. The asset status modeling module calculates the time integral for the over-limit amplitude that is greater than zero and outputs the time domain over-limit integral amount that filters out baseline drift interference.

[0039] To calibrate the environmental sensitivity coefficient, the weight adaptive adjustment module loads historical maintenance data of the same type of power distribution asset over the past 5 years. The system reads the standard resource consumption equivalent from the records and imports it along with environmental operating parameters on the synchronous time axis into a multinomial regression model. The weight adaptive adjustment module solves for the elastic partial derivative of the standard resource consumption equivalent with respect to individual environmental operating parameters, extracts the convergent mean of this elastic partial derivative over the steady-state operating range, and assigns this convergent mean to the corresponding evaluation index, the environmental sensitivity coefficient. The mathematical model of the environmental sensitivity coefficient satisfies: ,in, Let T be the environmental sensitivity coefficient of the i-th evaluation index, and T be the time span of the historical maintenance data. The standard resource consumption equivalent accumulated at time t. Let t be the environmental operating condition parameters. The above parameter calibration procedure converts the environmental sensitivity coefficient into a quantitative measurement value based on historical maintenance data. The system outputs the time-domain limit-breaking integral quantity determined according to the dynamic upper limit boundary, integrates the environmental sensitivity coefficient calculated based on the elastic partial derivative model, drives the exponential adjustment function to update the evaluation index weights, and the standardized quality evaluation module generates an evaluation index based on the updated weights and aggregates parameters. This index maps the accelerated decay effect of high salt spray environment on specific insulation indicators. The system outputs hierarchical management instructions to guide the maintenance team to prioritize cleaning and desalinating distribution transformers in the critical insulation state and applying anti-corrosion coatings. This controls the dissipation of management resources during the static parameter setting period and establishes a quantitative correspondence between the physical deterioration state of equipment and the standardized operation and maintenance resource input.

[0040] Example 5: When the system faces the initial access of a newly established distribution network management domain and the commissioning of all heterogeneous distribution assets, the data standardization processing module initiates the on-site deployment pre-calibration procedure, extracts the static ledger attributes and historical extreme values ​​of the environment in the target area within a specified period, and combines them with the steady-state physical operation parameter data collected during the short-term trial operation phase to construct an initial baseline under a specific service environment. The system calculates the root mean square error of the steady-state physical operation parameter data, calculates the product of the root mean square error and the set calibration constant, and adds the product to the initial baseline to generate the initial health interval boundary of the current network topology. The boundary parameter is input into the asset status modeling module to compensate for the baseline physical characteristic offset caused by cross-regional deployment and determine the measurement benchmark of the initial physical state parameters of heterogeneous equipment.

[0041] The system retrieves the standard resource consumption equivalent of similar assets within the target distribution network area during historical maintenance cycles. The asset status modeling module slices the standard resource consumption equivalent by time dimension and removes abnormal expenditure records exceeding the set consumption limit. The discrete mean of the filtered data is extracted and set as the initial preset cost benchmark. The weight adaptive adjustment module reads the initial preset cost benchmark and the standardized operation and maintenance cost allocation value generated during the short-term trial operation, calculates the initial deviation between the two, and adjusts the scaling factor inside the index adjustment function based on the initial deviation. Once the scaling factor remains within the set fluctuation threshold for three consecutive calculation cycles, the system locks the initial weights of each evaluation indicator and switches to dynamic evaluation mode. The standardized quality evaluation module generates an evaluation index based on the locked weights and physical operation parameter data and outputs an initial hierarchical management instruction. Based on this instruction, a material allocation list is generated, and the operation and maintenance management terminal outputs the targeted allocation and scheduling information of maintenance resources in the newly connected area according to the list.

[0042] Example 6: In a scenario involving the grid connection and commissioning of heterogeneous power distribution assets with multiple batches of unknown basic electrical properties, the discreteness of the physical state of the inherent insulation materials between batches causes the health interval boundary relied upon by the asset state modeling module to lose its universality. Furthermore, the damping coefficient used by the weight adaptive adjustment module to smooth out weight abrupt changes lacks quantitative calibration. The data standardization processing module initiates an offline parameter matrix generation process. The system reads the steady-state physical operation parameter data of the sampled assets from the same batch, constructs a multi-dimensional feature matrix composed of current distortion rate and relative temperature rise rate, and uses principal component analysis to extract the first principal component eigenvalue of this multi-dimensional feature matrix. The product of the first principal component eigenvalue and the normal operating baseline value is set as... For the health interval boundary of the corresponding batch of distribution assets, in the specific execution process, the first principal component eigenvalue is extracted and preprocessed into a dimensionless coefficient representing the expansion factor of the batch data variance. This coefficient is multiplied by the normal operation benchmark value with clear physical dimensions obtained in the factory rated test of such distribution assets. In essence, it uses statistical dimensionality reduction to generate a dynamic tolerance bandwidth around the nominal benchmark. This bandwidth is proportionally scaled down to the initial physical benchmark through the dimensionless coefficient, thereby ensuring accurate electrical measurement attributes and engineering feasibility based on the finally determined health interval boundary. The system inputs the calibrated health interval boundary into the asset status modeling module to establish the physical judgment scale for the time domain segment of each evaluation indicator exceeding the limit.

[0043] For the weight update process, the adaptive weight adjustment module retrieves a historical mapping sample set of standard assets with lifecycle degradation labels. The system locates the discrete time coordinates where the standardized operation and maintenance cost allocation value in the sample set experiences a step change. It calculates the absolute rate of change of the feedback deviation relative to the time span at this discrete time coordinate and inputs this absolute rate of change into an iterative approximation function to search for a convergent algebraic value that makes the continuous second derivative of the weight sequence of each evaluation index approach zero. Before performing the derivative calculation, the system's built-in data processing pipeline pre-calls a cubic spline interpolation algorithm to smoothly reconstruct the weight evolution points distributed according to the discrete time coordinates. This interpolation process fits the discrete sequence into a smooth curve that includes a time independent variable and has a globally continuous second-order differentiable property. The process of finding the continuous second derivative approaching zero is essentially locating the linear extension segment with the most stable curvature change on the fitted curve, thereby eliminating the interference of short-term transient fluctuations. The system assigns this convergent algebraic value to the exponential adjustment function as a damping coefficient. The asset status modeling module and the weight adaptive adjustment module process the real-time collected physical operation parameter data based on the offline calibrated health interval boundaries and damping coefficients. The standardized quality evaluation module generates an evaluation index and outputs hierarchical management instructions based on the processing results. The operation and maintenance resource allocation path of heterogeneous power distribution assets is based on the engineering data sequence calibrated across batches.

[0044] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A power distribution asset operation quality assessment and management system, characterized in that, include: The data standardization processing module is used to acquire physical operation parameter data and maintenance work order data of power distribution assets. The maintenance work order data records the standard resource consumption equivalent. The asset status modeling module is used to extract the time-domain segments that deviate from the preset health interval boundaries of each evaluation index based on physical operation parameter data, calculate the integral value of the time-domain segments with respect to time to obtain the time-domain over-limit integral amount, and use the proportion of the time-domain over-limit integral amount of a single parameter in the total integral amount of all-dimensional indicators as the cost allocation factor to allocate the standard resource consumption equivalent to each evaluation index in order to determine the standardized operation and maintenance cost allocation value driven by a single parameter. The weight adaptive adjustment module is used to set the initial weights of each evaluation indicator based on the preset environmental sensitivity coefficient, and to normalize and update the weights of each evaluation indicator using an exponential adjustment function based on the feedback deviation of the standardized operation and maintenance cost allocation value relative to the preset cost benchmark. The standardized quality evaluation module is used to generate an assessment index that characterizes the risk of uncontrolled asset operation and maintenance costs based on updated weights and real-time collected physical operation parameter data. By comparing the assessment index with preset management standard thresholds, it outputs hierarchical management instructions for the corresponding asset maintenance priorities.

2. The power distribution asset operation quality assessment and management system according to claim 1, characterized in that, When determining the standardized operation and maintenance cost allocation value driven by each evaluation indicator, the asset status modeling module performs the following logical steps: Step S21, for the traceability time window before any maintenance work order is generated, extract the sampling sequence of each evaluation indicator in the physical operation parameter data and remove sampling outliers; Step S22, identify the over-limit amplitude in the sampling sequence that exceeds the boundary of the healthy interval, and perform an integral operation on the over-limit amplitude over time to obtain the time-domain over-limit integral quantity used to objectively characterize the accumulated physical damage stress; Step S23: Calculate the weight ratio of the time-domain limit-breaking integral of a single parameter in the total integral of all-dimensional indicators, and decompose the total standard resource consumption equivalent corresponding to the maintenance work order according to the weight ratio.

3. The power distribution asset operation quality assessment and management system according to claim 1, characterized in that, The weight adaptive adjustment module includes an environmental impact analysis unit, which monitors the environmental operating parameters of the area where the power distribution assets are located, determines the operating condition acceleration coefficient of each evaluation index based on the environmental operating parameters, and uses the operating condition acceleration coefficient to adjust the environmental sensitivity coefficient to update the initial weights.

4. The power distribution asset operation quality assessment and management system according to claim 1, characterized in that, The standardized quality evaluation module includes a risk conversion unit, which maps the evaluation index to the probability value of uncontrolled operation and maintenance costs, so as to guide operation and maintenance resources to be deployed to highly sensitive risk nodes.

5. The power distribution asset operation quality assessment and management system according to claim 1, characterized in that, The data standardization processing module includes a standardization benchmarking unit, which is used to transform the data characteristics of heterogeneous power distribution assets into evaluation parameters with unified dimensions.

6. The power distribution asset operation quality assessment and management system according to claim 1, characterized in that, The asset status modeling module is also used to generate corrected weights by calculating the dispersion of physical operation parameter data on the time axis, and to use the corrected weights to perform weighted compensation for time-domain over-limit integral quantities.

7. The power distribution asset operation quality assessment and management system according to claim 1, characterized in that, The weight adaptive adjustment module includes a feedback correction unit, which receives the evaluation index output by the standardized quality evaluation module and adjusts the weight of each evaluation indicator exponentially according to the deviation between actual resource consumption and predicted consumption within the preset audit period.

8. The power distribution asset operation quality assessment and management system according to claim 1, characterized in that, When outputting hierarchical management instructions, the standardized quality evaluation module divides power distribution assets into four management levels according to the numerical level of the evaluation index, and matches corresponding standardized operation and maintenance procedures for each management level.

9. The power distribution asset operation quality assessment and management system according to claim 1, characterized in that, It also includes a resource allocation optimization module, which is connected to the standardized quality evaluation module. This module is used to automatically generate maintenance plan lists and spare parts allocation suggestions based on hierarchical management instructions, in order to suppress redundant investment in standardized services.

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