Communication resource reliability assessment method based on 5 +5 double-source index system

Abstract

The invention relates to a communication resource reliability assessment method based on a '5 + 5 double-source index system'. The method comprises the following steps: step 1, collecting original operation data and operation resume data in parallel; step 2, carrying out standardized preprocessing on the data acquired in the step 1; step 3, performing dynamic weight distribution based on the data preprocessed in the step 2; and step 4, performing communication resource reliability evaluation based on a dynamic weight distribution result in the step 3. According to the invention, full-dimension and high-precision reliability evaluation of resources such as communication equipment, optical cables, links and the like is realized.

Description

Technical Field

[0001] This invention belongs to the field of information and communication technology, and relates to a method for evaluating the reliability of communication resources, particularly a method for evaluating the reliability of communication resources based on a "5+5 dual-source index system". Background Technology

[0002] With the rapid iteration of technologies such as 5G, cloud computing, and the Industrial Internet, the scale of communication networks continues to expand, and the quantity and data exchange volume of resources such as communication equipment, transmission optical cables and derived communication channel links, and service bearer units are experiencing explosive growth. These resources have become critical infrastructure supporting social information transmission. In this context, the reliability of communication resources directly determines the quality of service (QoS) and business continuity of the network. For example, core businesses such as financial transactions and telemedicine have extremely high requirements for the latency, packet loss rate, and long-term stability of communication resources. Even millisecond-level latency fluctuations or occasional failures can cause significant economic losses or security risks. Therefore, the need for refined and comprehensive reliability assessment of communication resources is becoming increasingly urgent.

[0003] Current assessments of resource reliability in the communications field primarily rely on two traditional algorithm systems, whose technical approaches and application scenarios have significant limitations: Single-dimensional indicator evaluation methods were the mainstream solutions in the early stages of the industry. Their core principle is to judge resource reliability based on a single real-time operational indicator, such as qualitative scoring based on a single condition like "whether bandwidth utilization is below 80%", "whether transmission latency is less than 50ms", or "whether packet loss rate is less than 1%". Some optimization solutions increase the number of indicators, but still focus on real-time operational data, such as simultaneously collecting three indicators: bandwidth, latency, and packet loss rate, and outputting results based on the logic of "all met is qualified, any unmet is unqualified".

[0004] To compensate for the limitations of single-dimensional evaluation, fixed-weight multi-indicator evaluation methods have evolved into multi-indicator weighted algorithms. These algorithms typically select 3-4 real-time operational indicators, pre-set fixed weights, and calculate a comprehensive score through "indicator normalization + summation of fixed weights." Data collection still primarily relies on real-time data, with only a few solutions incorporating short-term historical data such as "number of failures in the past 24 hours," but a systematic historical data processing mechanism has not yet been established.

[0005] The two types of algorithms mentioned above are currently widely used in small and medium-sized communication networks or simple business scenarios. Their core advantages are simple implementation and low computational cost. However, with the diversification of communication resource types and the increasing complexity of business needs, their technical shortcomings are gradually becoming apparent.

[0006] Existing traditional algorithms have significant technical shortcomings in terms of comprehensiveness, accuracy, and adaptability in reliability assessment, and cannot meet the assessment needs of today's complex communication networks. Specifically, these shortcomings are manifested in the following four aspects: The data dimensions are limited, and long-term reliability assessments are lacking. Existing technologies generally rely on a single data source—"real-time operational data"—and fail to incorporate "historical operational history data" into the evaluation system. For example, algorithms only collect real-time indicators such as current bandwidth and latency, ignoring crucial information such as historical fault records, performance degradation trends, and maintenance cycles of communication resources. The direct problem caused by this deficiency is that the evaluation results only reflect the "instantaneous state" of the resources and cannot predict their long-term stability.

[0007] The indicator processing mechanism is crude, resulting in significant calculation errors. Existing technologies lack differentiated preprocessing mechanisms for collected indicators, generally employing a crude approach of "uniform normalization + simple filtering." Specific problems include: lack of adaptation logic designed for indicator types: for example, using the same normalization formula for positive indicators like "bandwidth utilization" and negative indicators like "transmission latency," leading to a mismatch between indicator weights and actual impact; simplistic outlier handling: most algorithms only handle abnormal data through "threshold filtering," failing to consider the statistical laws of data fluctuations (such as the 3σ principle), easily resulting in the deletion of normal fluctuation data or the omission of latent abnormal data (e.g., a packet loss rate of 0.8%, although below the 1% threshold, fluctuates by 3 times compared to the historical average); and failure to eliminate differences in units and magnitudes: for example, "latency (ms level)" and "packet loss rate (%)" are directly involved in the calculation, and the difference in magnitude causes the scoring results to be dominated by a single indicator, failing to truly reflect the comprehensive impact of multiple indicators.

[0008] The rigid weight allocation mechanism suffers from poor adaptability to business scenarios. Existing multi-index algorithms all employ a "preset fixed weight" mechanism, meaning that once the weight values ​​are set, they cannot be dynamically adjusted according to the business scenario. For example, weights designed for "real-time video transmission" are directly applied to "enterprise core data transmission," resulting in scoring results that are out of touch with actual business needs. Specific defects manifest as follows: different businesses have fundamentally different reliability requirements for communication resources—real-time video is sensitive to "jitter and latency," core data transmission is sensitive to "packet loss rate and long-term stability," and IoT sensor data transmission is sensitive to "bandwidth utilization and device availability." The existing fixed weight mechanism cannot adapt to these differences, leading to "distorted" scores for the same resource in different scenarios. For example, a resource might score "excellent" in a video scenario, but due to a high packet loss rate, its actual reliability in a core data transmission scenario might only be "acceptable." Existing algorithms cannot distinguish between these differences.

[0009] In summary, existing communication resource reliability assessment algorithms suffer from three core flaws: "single data dimension, coarse indicator processing, and rigid weighting mechanism." These flaws result in assessments that are "one-sided, have large errors, and poor adaptability," failing to meet the demands of today's complex communication networks for "refined, multi-dimensional, and highly adaptable" reliability assessments. Therefore, there is an urgent need to develop a multi-data-dimensional, high-precision, and highly adaptable communication resource reliability scoring algorithm to address the shortcomings of existing technologies and support efficient operation and maintenance and optimized resource scheduling of communication networks. Summary of the Invention

[0010] To address the shortcomings of existing technologies, this invention proposes a communication resource reliability assessment method based on a "5+5 dual-source index system." By systematically collecting "raw operational data" and "operational history data," and combining differentiated preprocessing, dynamic weight allocation, and weighted fusion calculation, it achieves a comprehensive and high-precision reliability assessment of resources such as communication equipment, optical cables, and links. The core objective is to provide a scientific and practical scoring basis for communication network operation and maintenance decisions and resource scheduling.

[0011] The above-mentioned objective of this invention is achieved through the following technical solution: A communication resource reliability assessment method based on a "5+5 dual-source index system" includes the following steps: Step 1: Collect raw runtime data and runtime history data in parallel; Step 2: Perform standardization preprocessing on the data collected in Step 1; Step 3: Based on the data preprocessed in Step 2, perform dynamic weight allocation; Step 4: Based on the dynamic weight allocation results in Step 3, conduct a reliability assessment of communication resources.

[0012] Furthermore, the original operating data and operating history data in step 1 include: Raw operational data: Bandwidth utilization (B), transmission delay (D), packet loss rate (L), jitter (J), instantaneous bit error rate (E); Operational history data: historical failure count (F), mean time to recovery (R), maintenance interval (M), long-term availability (... Historical performance degradation rate (P) Furthermore, the specific steps of step 2 include: (1) Outlier filtering: Raw data: The “3σ rule” is used – the mean (μ) and standard deviation (σ) of the data for the last N periods (default 10) are calculated, and outliers exceeding “[μ-3σ,μ+3σ]” are replaced with μ; Resume data: Missing values ​​are filled with the median of the last K days (default 7), and outliers exceeding the general maximum value are replaced with 80% of the maximum value.

[0013] (2) Indicator classification normalization: Positive indicators (B, M, ): ; Negative indicators (D, L, J, E, F, R, P):

[0014] Furthermore, the specific method for step 3 is as follows: Select the weighting mode based on the assessment requirements: Select "Preset Experience Weights": Allocate total weights and internal weights according to general configurations, or adjust the proportions according to scenario requirements; Select "Dynamic Entropy Weight": Calculate the indicator weights according to the formula above to ensure that the weights match the actual fluctuation patterns of the data.

[0015] Furthermore, the specific method for step 4 is as follows: (1) Calculation of sub-item scores: Raw running data item scoring: ( These are the normalized values ​​of the original data. (Weights within the original data). Performance history data item scoring: ( These are the normalized values ​​of the original data. (Weights within the original data). (2) Calculation of comprehensive score:

[0016] (3) Based on the comprehensive scoring results, conduct a reliability assessment of communication resources.

[0017] The advantages and beneficial effects of this invention are as follows: This invention addresses three major shortcomings of existing technologies from a qualitative perspective through a three-step core processing flow of "dual-source data acquisition → differentiated preprocessing → dynamic weight allocation," achieving comprehensive, refined, and scenario-specific adaptation for communication resource reliability assessment. The specific correspondence is as follows: 1. Addressing the deficiency of "single data dimension": Dual-source data collection to supplement long-term evaluation dimensions. Existing technologies only collect real-time data, which cannot reflect the long-term stability of resources. This invention solves this problem by "parallel acquisition of dual-source data": on the one hand, it collects "raw operating data" to capture the real-time operating status of resources; on the other hand, it adds the collection of "operating history data" to systematically integrate the historical fault records, maintenance cycles, and performance degradation trends of resources.

[0018] This step upgrades the assessment from a "single real-time dimension" to a "real-time + long-term" dual dimension, which can identify resource aging and hidden failure risks in advance, filling the gap in existing technologies that cannot assess the health of resources throughout their entire life cycle.

[0019] 2. Addressing the deficiency of "coarse indicator processing": Differentiated preprocessing reduces calculation errors. Existing technologies employ "uniform normalization + threshold filtering," failing to differentiate between indicator types (positive / negative) and neglecting data fluctuation patterns, leading to large scoring errors and imbalanced indicator influence. This invention addresses this issue through "differentiated preprocessing steps": First, outliers are filtered using the "3σ principle" on the raw operational data, accurately identifying hidden fluctuations. Second, different normalization logics are designed for "positive indicators" and "negative indicators" to avoid scoring dominance caused by differences in indicator magnitude. Third, a "trend correction" step is added to historical data to prevent extreme historical data from interfering with evaluation results. This process upgrades indicator processing from "extensive" to "refined," ensuring the effectiveness and fairness of each indicator and laying the foundation for subsequent scoring accuracy.

[0020] 3. Addressing the "rigid weighting mechanism" deficiency: Dynamic weight allocation steps adapt to multiple business needs. Existing technologies use "fixed weights," which cannot match the differentiated reliability requirements of different businesses, resulting in scores that are "out of touch with business needs." This invention solves this problem through a "two-layer dynamic weight allocation process": the first layer adjusts the "total weight ratio of raw data and historical data" according to the business scenario; the second layer adjusts the internal weights within each data category based on the importance of the indicators. This process upgrades the weights from "fixed and unchanging" to "scenario-based dynamic adaptation," allowing the scores of the same resource in different business scenarios to accurately reflect actual needs and avoiding the distortion caused by the "one-size-fits-all" scoring method of existing technologies. Attached Figure Description

[0021] Figure 1 This is a flowchart of the processing of the present invention. Detailed Implementation

[0022] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings: A communication resource reliability assessment method based on a "5+5 dual-source index system", such as... Figure 1 As shown, it includes the following steps: Step 1: Collect raw runtime data and runtime history data in parallel; The original operation data and operation history data in step 1 include: Raw operational data: Bandwidth utilization (B), transmission delay (D), packet loss rate (L), jitter (J), instantaneous bit error rate (E); Operational history data: historical failure count (F), mean time to recovery (R), maintenance interval (M), long-term availability (... Historical performance degradation rate (P) The calculation method for runtime history data does not have to be unique and fixed. Instead, it follows the design logic of "default general rules + flexible adjustment based on scenarios"—the core computing framework remains unified to ensure consistent evaluation benchmarks, but specific parameters, statistical ranges, and special scenario processing methods can be flexibly adapted according to resource types, business needs, and differences in operation and maintenance systems. This is highly consistent with the core goal of the technical solution to "solve the problem of rigidity in traditional algorithms".

[0023] Step 2: Perform standardization preprocessing on the data collected in Step 1; The specific steps of step 2 include: (1) Outlier filtering: Raw data: The “3σ rule” is used – the mean (μ) and standard deviation (σ) of the data for the last N periods (default 10) are calculated, and outliers exceeding “[μ-3σ,μ+3σ]” are replaced with μ; Resume data: Missing values ​​are filled with the median of the last K days (default 7), and outliers exceeding the general maximum value are replaced with 80% of the maximum value.

[0024] (2) Indicator classification normalization: Positive indicators (B, M, ): ; Negative indicators (D, L, J, E, F, R, P):

[0025] Step 3: Based on the data preprocessed in Step 2, perform dynamic weight allocation; The specific method for step 3 is as follows: Select the weighting mode based on the assessment requirements: Select "Preset Experience Weights": Allocate total weights and internal weights according to general configurations, or adjust the proportions according to scenario requirements; Select "Dynamic Entropy Weight": Calculate the indicator weights according to the formula to ensure that the weights match the actual fluctuation patterns of the data.

[0026] Step 4: Based on the dynamic weight allocation results in Step 3, conduct a reliability assessment of communication resources; The specific method for step 4 is as follows: (1) Calculation of sub-item scores: Raw running data item scoring: ( These are the normalized values ​​of the original data. (Weights within the original data). Performance history data item scoring: ( These are the normalized values ​​of the original data. (Weights within the original data). (2) Calculation of comprehensive score:

[0027] (3) Based on the comprehensive scoring results, conduct a reliability assessment of communication resources.

[0028] The working principle of this invention is: (I) Core Indicator System Design: "5+5 Dual-Source Indicators" This invention is the first to construct a dual-source indicator system of "raw operational data + operational history data". Each type of data includes 5 core indicators, covering key evaluation dimensions such as "real-time performance, long-term health, and failure risk", which are specifically defined as follows:

[0029] (II) Core Processing Flow The core processing flow of this invention follows the logic of "dual-source data acquisition → differentiated preprocessing → dynamic weight allocation → weighted fusion scoring", as follows: Dual-source data acquisition: parallel acquisition of raw runtime data and runtime history data; Differential preprocessing: The raw data is filtered for outliers using the "3σ principle", and the historical data is filled with missing values ​​using the "median replacement method". At the same time, different normalization formulas are used according to the indicator type (positive / negative). Dynamic weight allocation: Adjusted in two layers, "total weight" and "internal weight", to adapt to different business scenarios; Weighted fusion scoring: First, calculate the individual scores of the two-source data, and then combine them with the total weight to obtain the comprehensive score (0-100 points). Example 1: (I) General Implementation Prerequisites and Parameter Presets Before initiating the assessment, general parameters need to be preset based on the single resource type to be assessed. The parameter range should cover common industry scenarios to ensure the universality and accuracy of the assessment.

[0030] 1. Basic parameters of dual-source indicators Raw operational data: Bandwidth utilization (B), transmission delay (D), packet loss rate (L), jitter (J), instantaneous bit error rate (E); Operational history data: historical failure count (F), mean time to recovery (R), maintenance interval (M), long-term availability (... ), historical performance degradation rate (P).

[0031] 2. General weight configuration method Mode 1: Preset experience weights The default total weight distribution is 50% for raw data and 50% for resume data. This can be adjusted according to specific needs – increase the weight of raw data if real-time requirements are high, and increase the weight of resume data if stability requirements are high. Internal weight allocation:

[0032] Mode 2: Dynamic Entropy Weight Suitable for scenarios with high reliability requirements, the weights are calculated using the "information entropy" of historical data (the greater the data difference, the higher the weight). The core formula is: Information entropy of the j-th indicator: ( (where n is the proportion of the normalized value of the j-th indicator in the i-th sample). The weight of the j-th indicator: (m=5, meaning each data category contains 5 indicators).

[0033] (II) General Implementation Steps Using any single communication resource (such as a certain type of equipment or a certain section of optical cable) as the evaluation object, the implementation steps are standardized to 4 steps, and only the resource type needs to be adjusted during the parameter preset stage: Step 1: General Acquisition of Dual-Source Data Collect dual-source data from the resources to be evaluated through standardized interfaces to ensure data timeliness and completeness: Raw operational data collection: Data is extracted from resource hardware interfaces or communication protocol stacks. The default collection period is 30 seconds / time. The collected data includes bandwidth utilization (B), transmission latency (D), packet loss rate (L), jitter (J), and instantaneous bit error rate (E). The data format is uniformly "indicator name: value + unit".

[0034] Operational history data collection: Data is extracted from the operation and maintenance management system or historical log database. The default collection cycle is once per day (adjustable). The collected data includes historical failure count (F), average failure recovery time (R), maintenance interval (M), and long-term availability (Long-term availability). Historical performance degradation rate (P), with the data format uniformly set to "Indicator Name: Value + Unit".

[0035] Step 2: Standardization Preprocessing of Dual-Source Data Eliminate data noise and unit differences to ensure that indicators are fairly included in the calculation: (1) Outlier filtering: Raw data: The “3σ rule” is used – the mean (μ) and standard deviation (σ) of the data for the last N periods (default 10) are calculated, and outliers exceeding “[μ-3σ,μ+3σ]” are replaced with μ; Resume data: Missing values ​​are filled with the median of the last K days (default 7), and outliers exceeding the general maximum value are replaced with 80% of the maximum value.

[0036] (2) Indicator classification normalization: Positive indicators (B, M, ): ; Negative indicators (D, L, J, E, F, R, P):

[0037] Step 3: General Weight Allocation Select the weighting mode based on the assessment requirements: Select "Preset Experience Weights": Allocate total weights and internal weights according to general configurations, or adjust the proportions according to scenario requirements; Select "Dynamic Entropy Weight": Calculate the indicator weights according to the formula above to ensure that the weights match the actual fluctuation patterns of the data.

[0038] Step 4: General Score Calculation Resource reliability scores are calculated using a two-step method: "item-based scoring → comprehensive scoring". Sub-item scoring calculation: Raw running data item scoring: ( These are the normalized values ​​of the original data. (Weights within the original data). Performance history data item scoring: ( These are the normalized values ​​of the original data. (Weights within the original data). Overall score calculation:

[0039] (III) General Implementation Verification and Adaptation Instructions 1. Verification Logic By designing for "configurable parameters, standardized processes, and universal formulas," any single resource evaluation scenario can be adapted by adjusting the "universal parameter range" and "weight allocation" without modifying the core process, ensuring that the evaluation results match the actual reliability of the resource.

[0040] 2. General Rules for Interpreting Results The overall score corresponds to a four-level reliability rating, directly guiding operational decisions:

[0041] The innovation of this invention lies in: This invention proposes a communication resource reliability scoring algorithm based on a "5+5 dual-source index system." The core of this algorithm is to achieve comprehensive evaluation by collecting two types of data: first, raw operational data including bandwidth utilization, transmission latency, packet loss rate, jitter, and instantaneous bit error rate; second, operational history data including historical failure count, mean time to recovery, maintenance interval, long-term availability, and historical performance degradation rate. This algorithm significantly improves the comprehensiveness and accuracy of the scoring through an innovative process of preprocessing the dual-source data separately, dynamically assigning weights, and performing weighted fusion calculations. It addresses the one-sidedness of traditional algorithms that "emphasize real-time performance while neglecting historical data," and reduces index errors through differentiated normalization processing, ensuring the completeness and accuracy of the reliability calculation results.

[0042] This invention has a wide range of applications and can be directly adapted to core areas such as communication equipment performance evaluation, optical cable health monitoring, communication service reliability classification, and channel link stability detection. It can provide key data support for communication network resource scheduling, fault early warning, and operation and maintenance optimization, and has significant practical value and market application prospects.

[0043] 1. Existing communication resource scoring algorithms mostly adopt an evaluation model of "single-source data + fixed indicators," generally lacking a systematic integration of historical operational data, and the indicator system does not cover key dimensions such as "instantaneous bit error rate" and "historical performance degradation rate." This invention, based on the characteristics of transmission resources, pioneers a "5+5 dual-source indicator system," deeply integrating raw operational data with operational history data. Through innovative indicator selection and data processing logic, it achieves a multi-dimensional assessment of communication resource reliability. Compared with existing technologies, the dual-source data fusion scheme and differentiated indicator system of this invention are entirely new designs. The comprehensiveness and accuracy of the scoring results are significantly superior to existing algorithms, fully meeting the "novelty" requirements of patent law.

[0044] 2. The inventiveness of this invention lies in two major technological breakthroughs: First, it breaks through the limitations of traditional algorithms' "single data dimension" by innovatively incorporating key indicators such as "historical performance degradation rate" and "instantaneous bit error rate" into the evaluation system. Through a dual-dimensional evaluation of "real-time status + long-term trend," it solves the technical pain point that traditional algorithms cannot reflect the long-term reliability of resources. Second, it designs a calculation process of "separate preprocessing of dual-source data + dynamic weight allocation"—using 3σ outlier processing for the original data and trend correction and missing value completion for the historical data. The weight allocation can be dynamically adapted according to the business scenario, rather than the existing technology's "fixed weight + simple summation." This solution is not a simple combination of existing technologies, but rather achieves a technological leap in communication resource reliability assessment through substantial process innovation and indicator optimization, meeting the "inventiveness" requirements of the Patent Law.

[0045] 3. The reliability scoring algorithm provided by this invention has clear practical value: it has high basic data utilization, can directly connect to existing data sources such as communication equipment interfaces, network log systems, and operation and maintenance management platforms, without the need for additional data collection architecture; it is highly implementable, and can be directly integrated into communication network management systems, providing an operational basis for resource scheduling (such as prioritizing the allocation of high-reliability resources for core services) and operation and maintenance decisions (such as formulating maintenance plans based on historical failure counts), effectively solving the current practical problems in communication resource reliability assessment, and generating significant economic benefits (reduced operation and maintenance costs) and social benefits (improved communication network stability). The algorithm has high technical maturity and can be directly applied to industrial production scenarios.

[0046] In summary, the communication resource reliability scoring algorithm based on the "5+5 dual-source index system" proposed in this invention has significant innovations in terms of technical solution, evaluation dimensions, and practical value.

[0047] It should be emphasized that the embodiments described in this invention are illustrative rather than limiting. Therefore, this invention includes, but is not limited to, the embodiments described in the specific implementation. Any other implementations derived by those skilled in the art based on the technical solutions of this invention are also within the scope of protection of this invention.

Claims

1. A communication resource reliability assessment method based on a "5+5 dual-source index system", characterized in that: Includes the following steps: Step 1: Collect raw runtime data and runtime history data in parallel; Step 2: Perform standardization preprocessing on the data collected in Step 1; Step 3: Based on the data preprocessed in Step 2, perform dynamic weight allocation; Step 4: Based on the dynamic weight allocation results in Step 3, conduct a reliability assessment of communication resources.

2. The communication resource reliability assessment method based on the "5+5 dual-source index system" according to claim 1, characterized in that: The original operation data and operation history data in step 1 include: Raw operational data: Bandwidth utilization (B), transmission delay (D), packet loss rate (L), jitter (J), instantaneous bit error rate (E); Operational history data: historical failure count (F), mean time to recovery (R), maintenance interval (M), long-term availability (... ), historical performance degradation rate (P).

3. The communication resource reliability assessment method based on the "5+5 dual-source index system" according to claim 1, characterized in that: The specific steps of step 2 include: (1) Outlier filtering: Raw data: The "3σ rule" is used - the mean (μ) and standard deviation (σ) of the data for the last N periods (default 10) are calculated, and outliers exceeding "[μ-3σ,μ+3σ]" are replaced with μ; Resume data: Missing values ​​are filled with the median of the last K days (default 7), and outliers exceeding the general maximum value are replaced with 80% of the maximum value; (2) Indicator classification normalization: Positive indicators (B, M, ): ; Negative indicators (D, L, J, E, F, R, P): .

4. The communication resource reliability assessment method based on the "5+5 dual-source index system" according to claim 1, characterized in that: The specific method for step 3 is as follows: Select the weighting mode based on the assessment requirements: Select "Preset Experience Weights": Allocate total weights and internal weights according to general configuration, or adjust the proportions according to scenario requirements; Select "Dynamic Entropy Weight": Calculate the indicator weights according to the formula above to ensure that the weights match the actual fluctuation patterns of the data.

5. The communication resource reliability assessment method based on the "5+5 dual-source index system" according to claim 1, characterized in that: The specific method for step 4 is as follows: (1) Calculation of sub-item scores: Raw running data item scoring: ( These are the normalized values ​​of the original data. (Weights within the original data). Performance history data item scoring: ( These are the normalized values ​​of the original data. (Weights within the original data). (2) Calculation of comprehensive score: (3) Based on the comprehensive scoring results, conduct a reliability assessment of communication resources.

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