A regional asset efficiency analysis method based on multi-index data auditing

Abstract

The application provides a regional asset efficiency analysis method based on multi-index data auditing, and belongs to the field of data processing. The application collects resource data including space and equipment in a region, extracts features such as space-resource relationship and resource distribution after preprocessing. Through the construction of a multi-dimensional auditing index system, dynamic weight optimization and efficiency quantization model, the life cycle efficiency evaluation and optimization of regional assets are realized.

Description

Technical Field

[0001] This invention relates to the fields of data processing, asset performance evaluation, and regional resource management, and in particular to a regional asset performance analysis method based on multi-indicator data auditing. Background Technology

[0002] In the field of communication resource management, rational analysis of regional asset efficiency is crucial for comprehensively analyzing the network resource structure within a region, promoting the improvement of network resource efficiency, and ensuring the rational and efficient utilization of network resources. However, operators currently face many pain points in asset efficiency management. First, there is a lack of comprehensive benefit assessment models. With the continuous growth of network scale, data centers suffer from uneven load, varying sizes, and significant cost disparities, making it difficult to comprehensively analyze and measure data center efficiency through manual methods alone. Second, there is a lack of multi-dimensional evaluation methods for data centers. Currently, the existing network lacks data support for integrating resources across all services in data centers, managing multi-state assets, and considering various cost types. There is also a lack of systematic evaluation methods for the rationality of the number of data centers, the rationality of their size, resource data quality, network service quality, and the structure of various professional networks. Summary of the Invention

[0003] To address the aforementioned technical challenges, this invention provides a regional asset performance analysis method based on multi-indicator data auditing. This invention is significantly innovative, firstly in its ability to fuse multi-source heterogeneous data, overcoming the limitations of single data dimensions by horizontally integrating multiple systems and combining business rules with data-driven analysis. Secondly, it exhibits dynamic adaptability, automatically adjusting weights and model parameters according to changes in external factors and the environment, supporting long-term tracking and adapting to the continuous growth of communication networks and the constantly evolving evaluation standards.

[0004] The technical solution of this invention is: A regional asset performance analysis method based on multi-indicator data audit, characterized in that, By collecting resource data, including space and equipment within the region, and extracting the relationship between space and resources and the distribution of resources after preprocessing, the region's assets can be evaluated and optimized throughout their entire life cycle by constructing a multi-dimensional audit indicator system, dynamic weight optimization, and efficiency quantification model.

[0005] Furthermore, An improved TOPSIS model is introduced to construct a standardized decision matrix. Mahalanobis distance is used to replace traditional Euclidean distance to eliminate the influence of correlation between indicators. Grey relational analysis is combined to quantify the closeness of assets to ideal performance. The subjective and objective weights are integrated and dynamically optimized through the combined weight method, and regional asset performance scores and rankings are output.

[0006] Furthermore, Starting with data collection, relevant data for regional asset efficiency assessment are gathered, including cost data, spatial resource data, equipment resource information, equipment fault alarm information, utilization rate information, and resource distribution information. In the data preprocessing stage, the collected data is cleaned to remove erroneous data. Then, noise reduction is performed to eliminate interference factors, and data of different formats are standardized.

[0007] Furthermore, After data collection and standardization, the process moves to the indicator audit and weight allocation stage. First, it is necessary to determine the regional asset efficiency assessment indicators, namely the thresholds and verification logic set for each indicator. Second, a combined dynamic weight calculation is adopted, and subjective and objective weights are integrated through weighted harmonic average to enhance the model's adaptability. Finally, the asset efficiency index is calculated, a weighted standardized matrix is ​​constructed, and the ideal and negative ideal solutions are confirmed. Mahalanobis distance calculation and grey relational analysis are introduced to comprehensively measure and score the regional asset efficiency.

[0008] Furthermore, It includes modules for multi-source data integration, data preprocessing, construction of a multi-dimensional indicator system, dynamic audit rules, combined weight allocation, efficiency index calculation, and intelligent optimization decision-making.

[0009] The multi-dimensional indicator system includes: quantitative dimensions such as the rationality of the number of data centers (actual / theoretical) and the rationality of the data center area (actual / theoretical); quality dimensions such as the number of transmission equipment failures, the number of wireless equipment failures, the number of home broadband equipment failures, environmental alarm indicators, and transmission hidden danger indicators (co-routing risks); structural dimensions such as the convergence ratio of wireless equipment, the convergence ratio of transmission equipment, the rationality of power configuration, and the rationality of temperature control; and efficiency dimensions such as the utilization rate of transmission ports, the utilization rate of home broadband ports, the utilization rate of equipment, the proportion of high-efficiency sites, and the number of inefficient and ineffective assets.

[0010] The dynamic audit rules are configured with differentiated verification logic for different specialties. For the transmission specialty, rules such as 0% equipment utilization, network decommissioning status, failure to collect northbound network management data for 7 consecutive days, network access time greater than 8 years, and failure rate greater than 6% are used to determine inefficiency. For the wireless specialty, rules such as zero 5G traffic for 7 consecutive days and 5G utilization less than 3GB are used to determine inefficiency. For the home broadband specialty, rules such as 0% PON port utilization and less than 10% PON port utilization are used to determine inefficiency.

[0011] The intelligent dynamic optimization strategy includes root cause analysis and multi-objective simulation, supporting multi-level and multi-professional performance evaluation and ranking.

[0012] The intelligent optimization decision-making module includes root cause analysis and multi-objective simulation functions. Root cause analysis decomposes inefficient areas / data centers into multi-dimensional indicators, identifies major shortcomings (such as quality issues, structural issues, and efficiency issues), and provides targeted improvement suggestions. Multi-objective simulation simulates the impact of different optimization schemes (such as cleaning up inefficient assets, adjusting network structure, and optimizing resource allocation) on overall efficiency, allowing decision-makers to select the optimal solution. Early warning push automatically identifies assets that are not scheduled for disposal and overdue assets, and can push early warning notifications to responsible persons on a daily basis, supporting closed-loop management of the entire process.

[0013] The beneficial effects of this invention are The ability to integrate multi-source heterogeneous data breaks down data silos, horizontally connects multiple systems, and achieves comprehensive integration of financial, operational, business, spatial, and resource data, providing a global perspective and a complete data foundation for comprehensive evaluation.

[0014] The multi-dimensional evaluation system constructs a hierarchical indicator system from four dimensions: quantity, quality, structure, and efficiency. It covers key elements such as data center rationality, equipment failure, network structure, resource utilization, and cost-effectiveness, and realizes a comprehensive and multi-faceted quantitative evaluation of regional asset efficiency, which is more scientific and reasonable than the traditional single-dimensional evaluation method.

[0015] The dynamic weight optimization mechanism adopts a combined weight method that integrates subjective weights from AHP and objective weights from entropy weight method. It reflects both business priorities and management orientation, as well as the actual distribution characteristics of data. The weight parameters can be automatically adjusted with changes in the environment to adapt to the differentiated needs of different regions and periods, significantly improving the adaptability of the model.

[0016] The algorithm model is scientifically advanced, employing Mahalanobis distance to eliminate the influence of correlation between indicators and combining grey relational analysis to comprehensively evaluate asset efficiency, making it more accurate and reliable than traditional methods. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the workflow of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but 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.

[0019] The main objective of this invention is to provide an efficient, accurate, and scalable method for analyzing and evaluating the effectiveness of regional assets, thereby solving the multi-dimensional evaluation challenges faced by telecommunications operators in network resource management. Specifically: First, multi-source heterogeneous integration. Horizontally connect multiple systems (cost system, wireless workbench, transmission workbench, integrated resource pipeline system, fault management system, OMC network management system, etc.) to obtain multi-source heterogeneous data (cost data, traffic data, regional professional resource data, quality data, etc.), break through single data dimensions, integrate business rules and data-driven analysis, and achieve horizontal and vertical data integration.

[0020] Secondly, it offers dynamic adaptability. Weights and model parameters automatically adjust with environmental changes, supporting long-term tracking. As 5G networks continue to be built, 4G / 2G networks are gradually phased out, and new technologies evolve, evaluation standards are constantly being updated dynamically. Traditional manual configuration methods require significant time and effort. This invention achieves automatic weight optimization and adaptive adjustment of model parameters, enabling flexible configuration and rapid processing of massive amounts of data. It adapts to the differentiated needs of different regions (provinces / cities / districts / branches / data centers) and different specialties (transmission / wireless / core / home broadband), significantly saving labor and time costs.

[0021] This invention introduces an improved TOPSIS model to construct a standardized decision matrix, uses Mahalanobis distance to replace traditional Euclidean distance to eliminate the influence of correlation between indicators, combines grey relational analysis to quantify the closeness of assets to ideal performance, and achieves the integration and dynamic optimization of subjective and objective weights through a combined weighting method (analytic hierarchy process AHP + entropy weighting method) to output regional asset performance scores and rankings.

[0022] This invention includes the following steps: Multi-source heterogeneous data acquisition and fusion The horizontal integration of data from multiple systems includes regional asset financial data (costs, revenues), operation and maintenance data (failure rates, maintenance records), IoT data (energy consumption, traffic), spatial data (GIS location, distribution density), and spatial and resource relationship data.

[0023] Data preprocessing and standardization A unified asset database is constructed through data cleaning (missing value imputation, outlier correction) and standardization (normalization, discretization). The collected data undergoes comprehensive cleaning. Remove obviously erroneous data, such as data with abnormal positions or negative lengths; Denoising processing eliminates interference factors in the data and filters out low-quality data; Standardize the original data using the range method.

[0024] Where max(x) j ) and min(x j ) are the maximum and minimum values ​​of the j-th indicator, respectively; Constructing a standardized decision matrix

[0025] Construction of Multi-Source Heterogeneous Audit System Audit dimensions: A hierarchical indicator system is constructed from four dimensions: quantity, network quality, network structure, and efficiency. Quantity dimension Reasonableness of the number of data centers (actual number of data centers / theoretical number of data centers) and reasonableness of the data center area (actual data center area / theoretical data center area). Quality Dimensions Number of transmission equipment failures, number of wireless equipment failures, number of broadband equipment failures, number of environmental alarms, and transmission potential hazard indicators. Structural Dimensions Wireless device convergence ratio (number of wireless devices / data center capacity), transmission device convergence ratio (number of transmission devices / data center capacity), power configuration rationality, temperature control rationality Benefit Dimension Transmission port utilization, broadband port utilization, equipment utilization, percentage of sites with high energy consumption, high failure rate, high cost, and high load (number of sites / total number of sites), and number of inefficient and ineffective assets (assets with utilization rate <10% or 0). Audit rules: Rules are set by setting thresholds (e.g., marking an anomaly if the utilization rate is below 30%) and logical verification (e.g., spatial distribution analysis of resources, identification of risks associated with the same route).

[0026] Dynamic weight allocation and indicator fusion The combined weighting method is adopted. The initial weights are determined by experts through the analytic hierarchy process (AHP). The data dispersion is calculated and dynamically adjusted based on the entropy weighting method. The subjective and objective weights are integrated by weighted harmonic average to enhance the adaptability of the model.

[0027] Entropy weight method for calculating objective weights Calculate the entropy value e of the j-th index. j :

[0028] Calculate weights :

[0029] Combined weight fusion By integrating subjective and objective weights using a weighted harmonic average (where the subjective weight accounts for α and the objective weight accounts for 1-α):

[0030] Constructing a weighted standardization matrix

[0031] Determine the ideal solution and the negative ideal solution Ideal solution (positive) V+

[0032] Negative ideal solution V-

[0033] Improved distance calculation (Mahaviron distance) Calculate the covariance matrix ∑

[0034] in This is the vector of the mean values ​​of each indicator.

[0035] Calculate Mahalanobis distance Distance from asset i to the ideal solution :

[0036] Distance from asset i to the ideal solution :

[0037] Grey relational analysis Calculate the correlation coefficient: Correlation coefficient between asset i and the ideal solution :

[0038] Similarly, calculate the correlation coefficient with the negative ideal solution. ,in The resolution coefficient (usually taken as 0.5).

[0039] Normalized correlation coefficient

[0040] Comprehensive Proximity Calculation The final performance index is generated by combining distance correlation.

[0041]

[0042] in, This is the weighting coefficient for distance and relevance (usually taken as 0.5).

[0043] Asset efficiency ranking according to Sort the values ​​from largest to smallest.

[0044] This invention begins with data acquisition, extensively collecting data related to regional asset effectiveness assessment, including cost data, spatial resource data, equipment resource information, equipment fault alarm information, utilization rate information, and resource distribution information. The data preprocessing stage cleans the collected data to remove obviously erroneous data, such as abnormal locations, negligible alarms, and negative area values. Noise reduction processing is then performed to eliminate interference factors, and data of different formats are standardized to ensure data consistency and processability.

[0045] After data collection and standardization, the process moves to the indicator audit and weight allocation stage. First, it's necessary to determine the regional asset efficiency assessment indicators, including the thresholds and verification logic for each indicator. Second, a combined dynamic weight calculation is used, integrating subjective and objective weights through a weighted harmonic average to enhance the model's adaptability. Finally, the asset efficiency index is calculated, a weighted standardized matrix is ​​constructed, and ideal and negative ideal solutions are identified. Mahalanobis distance calculation and grey relational analysis are then employed to comprehensively measure and score the regional asset efficiency.

[0046] The above description is merely a preferred embodiment of the present invention and is used only to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A regional asset performance analysis method based on multi-indicator data auditing, characterized in that, By collecting resource data and preprocessing it, we can extract spatial and resource relationships and resource distribution. By constructing a multi-dimensional audit indicator system, dynamic weight optimization, and efficiency quantification model, we can achieve full life-cycle efficiency assessment and optimization of regional assets.

2. The method according to claim 1, characterized in that, An improved TOPSIS model is introduced to construct a standardized decision matrix. Mahalanobis distance is used to replace traditional Euclidean distance to eliminate the influence of correlation between indicators. Grey relational analysis is combined to quantify the closeness of assets to ideal performance. The subjective and objective weights are integrated and dynamically optimized through the combined weight method, and regional asset performance scores and rankings are output.

3. The method according to claim 1, characterized in that, Starting with data collection, relevant data for regional asset efficiency assessment are gathered, including cost data, spatial resource data, equipment resource information, equipment fault alarm information, utilization rate information, and resource distribution information. The data preprocessing stage involves cleaning the collected data to remove erroneous data; then denoising is performed to eliminate interference factors, and data of different formats are standardized.

4. The method according to claim 3, characterized in that, After data collection and standardization, the process moves to the indicator audit and weight allocation stage. First, it is necessary to determine the regional asset efficiency assessment indicators, namely the thresholds and verification logic set for each indicator. Second, a combined dynamic weight calculation is adopted, and subjective and objective weights are integrated through weighted harmonic average to enhance the model's adaptability. Finally, the asset efficiency index is calculated, a weighted standardized matrix is ​​constructed, and the ideal and negative ideal solutions are confirmed. Mahalanobis distance calculation and grey relational analysis are introduced to comprehensively measure and score the regional asset efficiency.

5. The method according to claim 4, characterized in that, Construct a multi-dimensional indicator system, including the following dimensions: quantity dimension (reasonableness of the number of data centers and the area of ​​data centers); quality dimension (number of transmission equipment failures, number of wireless equipment failures, number of home broadband equipment failures, environmental alarm indicators, and transmission hidden danger indicators); structure dimension (convergence ratio of wireless equipment, convergence ratio of transmission equipment, rationality of power configuration, and rationality of temperature control); and efficiency dimension (utilization rate of transmission ports, utilization rate of home broadband ports, equipment utilization rate, proportion of high-efficiency sites, high-voltage sites, high-voltage sites, and number of inefficient and ineffective assets).

6. The method according to claim 4, characterized in that, Dynamic audit rules are set up, and differentiated audit logic is configured for different specialties. For the transmission specialty, the following criteria are used to determine invalidity: equipment utilization rate of 0%, network decommissioning status, failure to collect northbound network management data for 7 consecutive days, network access time of more than 8 years and failure rate of more than 6%. For the wireless specialty, the following criteria are used to determine invalidity: zero 5G traffic for 7 consecutive days and 5G utilization rate of less than 3GB. For the home broadband specialty, the following criteria are used to determine invalidity: PON port utilization rate of 0% and PON port utilization rate of less than 10%.

7. The method according to claim 6, characterized in that, The system features intelligent dynamic optimization strategies, including root cause analysis and multi-objective simulation, supporting multi-level and multi-professional performance evaluation and ranking.

8. The method according to claim 4, characterized in that, The system features an intelligent optimization decision-making module with root cause analysis and multi-objective simulation capabilities. Root cause analysis decomposes inefficient areas / data centers into multi-dimensional indicators, identifies key weaknesses, and provides targeted improvement suggestions. Multi-objective simulation simulates the impact of different optimization schemes on overall performance, allowing decision-makers to select the optimal solution. Early warning push automatically identifies assets that are not scheduled for disposal and overdue assets, pushing early warning notifications on a daily basis, supporting closed-loop management throughout the entire process.

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