A cable intelligent monitoring and evaluation method and system

Abstract

The present application relates to the technical field of resource management, and more particularly to a cable intelligent monitoring and evaluation method and system. The method comprises the following steps: S1: obtaining a cable monitoring sequence, extracting humidity data and dust data based on the cable monitoring sequence; determining a starting segmentation point of the cable monitoring sequence based on the humidity data and the dust data; determining an ending segmentation point based on the starting segmentation point; S2: segmenting the cable monitoring sequence based on the starting segmentation point and the ending segmentation point to obtain a segmentation result; and traversing the cable monitoring sequence based on the segmentation result to obtain a traversal result. The present application dynamically matches the cleaning cycle, spare parts replacement and manpower scheduling through humidity and dust data, reduces redundant investment, quantifies the economic impact of wet dust damage, accurately controls the cost, optimizes the inventory and procurement logic, reduces the supply chain cost, dynamically adjusts the strategy to reduce downtime, prolongs the service life of the cable, and guarantees the asset return.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of resource management, and particularly relates to a cable intelligent monitoring and evaluation method and system. BACKGROUND

[0002] With the expansion of industrial facility scale and the increase of environmental complexity, cable health management faces the dual challenges of high maintenance cost and low resource utilization efficiency. The traditional maintenance mode relies on fixed period inspection and manual experience judgment, which leads to prominent maintenance resource mismatching problems, such as redundant allocation of cleaning manpower, overstock or shortage of spare parts, and economic loss (up to tens of thousands of yuan per hour) caused by unplanned shutdown. Although existing monitoring technologies can collect environmental data such as humidity and dust, they lack systematic integration and commercial analysis capabilities for multi-source data, and cannot convert environmental risks into dynamic maintenance decision basis. In addition, the traditional maintenance plan lacks flexibility and cannot dynamically adjust the cleaning period, spare parts procurement or human resource scheduling according to real-time damage risk, resulting in high maintenance cost and limited enterprise asset management efficiency. Therefore, there is an urgent need for a cable health management system that integrates environmental perception, damage prediction and business intelligence to ensure equipment reliability while achieving precise allocation of maintenance resources and cost-controllable operation goals. SUMMARY

[0003] In order to overcome the shortcomings of high consumption and low efficiency of cable maintenance, the present application provides a cable intelligent monitoring and evaluation method and system.

[0004] The technical implementation scheme of the present application is: a cable intelligent monitoring and evaluation method, comprising the following steps:

[0005] S1: acquiring a cable monitoring sequence, extracting humidity data and dust data based on the cable monitoring sequence, determining a starting segmentation point of the cable monitoring sequence based on the humidity data and the dust data, and determining an ending segmentation point based on the starting segmentation point;

[0006] S2: segmenting the cable monitoring sequence based on the starting segmentation point and the ending segmentation point to obtain a segmentation result, and traversing the cable monitoring sequence based on the segmentation result to obtain a traversal result;

[0007] S3: adjusting the cable monitoring mode based on the traversal result to obtain an adjusted cable monitoring mode, and evaluating the cable spare parts based on the adjusted cable monitoring mode to obtain an evaluation result;

[0008] S4: determining a cable maintenance strategy based on the evaluation result.

[0009] Preferably, the step of acquiring the cable monitoring sequence and extracting the humidity data and the dust data based on the cable monitoring sequence comprises:

[0010] The cable monitoring sequence comprises dust data, humidity data, process data caused by the dust data and humidity data, and final cable damage data;

[0011] The dust data, humidity data, process data, and cable damage data are arranged in chronological order to form the cable monitoring sequence;

[0012] Based on the cable monitoring sequence, the humidity data and the dust data in the cable monitoring sequence are extracted.

[0013] Preferably, the determination of the starting segmentation point of the cable monitoring sequence based on the humidity data and the dust data comprises:

[0014] If the humidity data and the dust data in the cable monitoring sequence simultaneously show abnormalities, the response time and the influence intensity of the humidity data and the dust data are standardized to obtain a standardized processing result, and the humidity data and the dust data are defined as first humidity data and first dust data;

[0015] Based on the standardized processing result, the first humidity data and the first dust data are compared, if the first humidity data is greater than the first dust data, the humidity data is taken as the starting segmentation point; if the first humidity data is less than the first dust data, the dust data is taken as the starting segmentation point;

[0016] If the humidity data and the dust data in the cable monitoring sequence do not show abnormalities, the humidity data or the dust data is taken as the starting segmentation point of the cable monitoring sequence.

[0017] Preferably, the determination of the ending segmentation point based on the starting segmentation point comprises:

[0018] Based on the starting segmentation point, the cable monitoring sequence is traversed, and the cable damage data closest to the starting segmentation point in time is taken as the ending segmentation point.

[0019] Preferably, the segmentation of the cable monitoring sequence based on the starting segmentation point and the ending segmentation point to obtain a segmentation result comprises:

[0020] Based on the starting segmentation point and the ending segmentation point, the cable monitoring sequence is segmented by the starting segmentation point, the ending segmentation point, and the process data in the starting segmentation point and the ending segmentation point to obtain a segmentation result.

[0021] Preferably, the traversal of the cable monitoring sequence based on the segmentation result to obtain a traversal result comprises:

[0022] based on the segmentation result, traversing the cable monitoring sequence, if the humidity data or the dust data appears in the sequence starting from the dust data or the humidity data as the starting segmentation point during the traversal process, marking the humidity data or the dust data as adjustment data;

[0023] traversing the cable monitoring sequence, recording the starting segmentation point and the ending segmentation point in each sequence, if the starting segmentation point in the second sequence is different from the starting segmentation point in the first sequence, traversing at the same speed; if the starting segmentation point is the same as the starting segmentation point in the first sequence, traversing at a speed greater than the traversal speed of the first sequence; if the starting segmentation point is the same and the ending segmentation point is different in the second sequence, traversing the third sequence at a speed less than the traversal speed of the first sequence;

[0024] traversing the cable monitoring sequence, recording the time interval of adjacent data and the data processing rate in each sequence, and defining the time interval as the traversal time and the data processing rate as the traversal speed.

[0025] Preferably, based on the traversal result, adjusting the cable monitoring method to obtain an adjusted cable monitoring method, comprising:

[0026] based on the traversal result, extracting the traversal time, the traversal speed and the adjustment data;

[0027] standardizing the traversal time and the traversal speed to obtain standardized traversal time and traversal speed, constructing a traversal factor with the standardized traversal time as the numerator and the standardized traversal speed as the denominator;

[0028] extracting the number of adjustment data and the number of segmented sequences, taking the number of adjustment data as the numerator and the number of segmented sequences as the denominator to construct an adjustment factor; if adjustment data appears in the sequence and the next sequence starts from the adjustment data as the starting segmentation point, the numerator of the adjustment factor increases by 1, if the next sequence does not start from the adjustment data as the starting segmentation point, the numerator of the adjustment factor decreases by 1;

[0029] during the traversal process of the cable monitoring sequence, if the traversal factor shows a downward trend and the adjustment factor shows an upward trend, switching to the adjusted monitoring method;

[0030] based on the adjusted monitoring method, extracting the data acquisition frequency, the traversal time, the traversal speed and the spare parts preparation time, calculating the total response time of the spare parts priority through the total response time formula;

[0031] If the traversal factor is in an upward trend and the adjustment factor is in a downward trend, the original monitoring mode is maintained.

[0032] Preferably, based on the adjusted cable monitoring mode, the cable spare parts are evaluated to obtain an evaluation result, including:

[0033] The total response time is calculated as follows,

[0034]

[0035] wherein, is the total response time, is the data acquisition frequency, is the frequency efficiency coefficient, is the normalized traversal time, is the normalized traversal speed, is the spare part time weight coefficient, is the spare part preparation time;

[0036] Based on the adjusted cable monitoring mode, the traversal factor of each sequence segment is extracted, and the TRT value of each cable segment is calculated by combining the total response time formula. By analyzing the change trend of the TRT value and the traversal factor, the loss risk level of the cable spare parts is quantified, and an evaluation result is generated.

[0037] Preferably, based on the evaluation result, a cable maintenance strategy is determined, including:

[0038] Based on the evaluation result, the absolute value of the change degree of the traversal factor before and after the cable monitoring mode adjustment is determined to determine the cable maintenance strategy.

[0039] Preferably, a cable intelligent monitoring evaluation system comprises:

[0040] An environmental data acquisition and standardization module: real-time acquisition of cable humidity and dust data, integration according to time sequence; standardization processing of abnormal data, quantification of humidity and dust response intensity; abnormal state judgment based on threshold value, dynamic marking of starting segmentation point;

[0041] A sequence segmentation and dynamic analysis module: segmentation of the monitoring sequence according to the starting and ending segmentation points; dynamic adjustment of speed during traversal of the segmented sequence; marking of humidity or dust data as adjustment data, recording of traversal time and speed change;

[0042] A spare part demand evaluation module: construction of a traversal factor based on the normalized traversal time and traversal speed, quantification of dynamic changes of monitoring efficiency; construction of an adjustment factor through the ratio of the number of adjustment data to the number of segmented sequences, combination of the adjustment data to trigger subsequent segmentation point dynamic correction of the numerator, and reflection of the persistence and cross-influence intensity of environmental interference;

[0043] Resource dynamic scheduling module: according to the spare parts demand evaluation result, dynamically match the cleaning period, spare parts replacement plan and manpower scheduling priority; push early warning information and generate purchase order, optimize inventory allocation.

[0044] Beneficial effects: dynamic matching of maintenance resources: based on real-time analysis of humidity and dust data, dynamically adjust the cleaning period, spare parts replacement plan and manpower scheduling priority, reduce redundant investment and avoid waste of funds. Cost control data: through the collaborative analysis of traversal factors and adjustment factors, quantify the economic impact of wet dust on cable operation status, provide accurate cost basis for enterprises, and develop high-performance cost-effective strategies. Inventory and supply chain optimization: according to cable environmental risk trend data, generate spare parts procurement demand and optimize inventory allocation, avoid overstocking due to emergency procurement, and reduce supply chain comprehensive cost. Automatic decision support: integrate environmental data analysis results and enterprise management processes to automatically generate maintenance work orders, resource allocation suggestions and budget plans, reduce human error and improve management efficiency. Controllable asset risk: through dynamic maintenance strategy adjustment, significantly reduce unplanned downtime frequency, extend cable life and ensure asset investment return. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 The flowchart of the cable intelligent monitoring and evaluation method of the present application;

[0046] Figure 2 The structural diagram of the cable intelligent monitoring and evaluation system of the present application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0048] Embodiment 1: a cable intelligent monitoring and evaluation method, as shown in Figure 1 The method comprises the following steps:

[0049] S1: obtaining a cable monitoring sequence, extracting humidity data and dust data based on the cable monitoring sequence, determining a starting segmentation point of the cable monitoring sequence based on the humidity data and the dust data, and determining an ending segmentation point based on the starting segmentation point;

[0050] S2: based on the starting segmentation point and the ending segmentation point, segmenting the cable monitoring sequence to obtain a segmentation result; based on the segmentation result, traversing the cable monitoring sequence to obtain a traversal result;

[0051] S3: Based on the traversal result, adjust the cable monitoring mode to obtain an adjusted cable monitoring mode; based on the adjusted cable monitoring mode, evaluate the cable spare parts to obtain an evaluation result;

[0052] It should be noted that based on the traversal result (traversal factor and adjustment factor), the real-time trend of monitoring efficiency and environmental interference is analyzed. If the traversal factor decreases and the adjustment factor increases (for example, the traversal factor of the first segment decreases from 4 to 2, and the adjustment factor increases from 0.4 to 0.8), the high-frequency monitoring mode (such as the sampling rate of the salt mist sensor is increased to once per second) is switched to more accurately evaluate the spare parts demand of the high-interference segment.

[0053] S4: Based on the evaluation result, determine the cable maintenance strategy.

[0054] Obtain a cable monitoring sequence, and based on the cable monitoring sequence, extract humidity data and dust data, including:

[0055] The cable monitoring sequence includes dust data, humidity data, process data caused by dust data and humidity data, and final cable damage data;

[0056] The dust data, humidity data, process data and cable damage data are arranged in time sequence in order to form a cable monitoring sequence;

[0057] Based on the cable monitoring sequence, extract the humidity data and dust data in the cable monitoring sequence.

[0058] It should be noted that by integrating dust, humidity, and damage process and result data caused by dust and humidity, a time sequence is constructed, which can trace the causal relationship between environmental factors and cable damage; arranging and extracting key environmental parameters (humidity, dust) according to time provides a structured analysis basis for subsequent dynamic segmentation and damage prediction.

[0059] Data acquisition method: humidity / dust data: real-time collection through environmental sensors deployed on the surface of the cable; process data: record the operating parameters of the cable in the wet dust environment (such as temperature rise rate, partial discharge intensity); final damage data: obtained based on periodic detection or online diagnosis technology (such as insulation resistance test, infrared thermal imaging).

[0060] For example, the cable of a coastal chemical plant is exposed to high humidity and salt spray dust environment for a long time. Through the humidity sensor and dust monitor, real-time collection of environmental data (such as humidity up to 95%, dust concentration exceeding 200 μg / m³). At the same time, record the temperature rise data (such as the temperature of a certain section of cable rises from 40℃ to 65℃) and partial discharge intensity (from 5pC to 50pC) in the running of the cable as process data. After three months, infrared detection found that there was a hot spot on the surface of the cable, and the insulation resistance decreased to below 10MΩ, which was determined as the final damage data. Arrange all the data in chronological order to form a monitoring sequence: such as "Day 1: humidity 92%, dust 180 μg / m³→ temperature rise 3℃→ discharge 10pC; Day 30: humidity 96%, dust 220 μg / m³→ temperature rise 10℃→ discharge 30pC… Day 90: insulation resistance 8MΩ". By extracting humidity and dust data, it is found that the period of high humidity and dust superposition (such as 5 consecutive days of humidity>90%) is highly related to temperature rise out of control and insulation deterioration, so as to accurately locate the risk window and guide the maintenance team to clean or reinforce the insulation layer in advance to avoid failure.

[0061] Based on the humidity data and the dust data, a starting segmentation point of a cable monitoring sequence is determined, including:

[0062] If the humidity data and the dust data in the cable monitoring sequence show abnormalities at the same time, the response time and the influence intensity of the humidity data and the dust data are standardized to obtain a standardized processing result, and the humidity data and the dust data are defined as first humidity data and first dust data;

[0063] Based on the standardized processing result, the first humidity data and the first dust data are compared. If the first humidity data is greater than the first dust data, the humidity data is taken as the starting segmentation point. If the first humidity data is less than the first dust data, the dust data is taken as the starting segmentation point.

[0064] If the humidity data and the dust data in the cable monitoring sequence do not show abnormalities, the humidity data or the dust data is taken as the starting segmentation point of the cable monitoring sequence.

[0065] It should be noted that if the humidity score after standardization is greater than the dust score, the humidity data is selected as the starting segmentation point; otherwise, the dust data is selected; when the humidity and dust data are abnormal at the same time in the cable monitoring sequence, it is necessary to determine which factor dominates the damage starting point to accurately locate the risk source. Through standardization, the "response time" (abnormal appearance speed) and "impact strength" (abnormal degree) of humidity and dust are converted into comparable values, eliminating dimensional differences. For example, if the humidity abnormal response is faster and the intensity is higher, the normalized humidity score is larger, and the humidity is determined as the dominant factor and as the segmentation point; otherwise, the dust data is used as the reference. If both are not abnormal at the same time, the parameter with separate abnormality is directly selected as the segmentation point.

[0066] For example, a cable in a coastal chemical plant is subjected to high humidity (more than 95%) and salt spray corrosion (chloride ion concentration more than 500 μg / m³) at the same time in the typhoon season. Monitoring found that both data were abnormal: the humidity rose from 80% to 98% in 1 hour (fast response time), and the salt spray concentration rose from 200 μg / m³ to 600 μg / m³ in 3 hours (higher intensity). After standardization, the humidity response time score is 0.9, and the intensity score is 0.7; the salt spray response time score is 0.3, and the intensity score is 0.8. The comprehensive score of humidity is (0.9+0.7)=1.6, and the comprehensive score of salt spray is (0.3+0.8)=1.1. Since the humidity total score is higher, the humidity is determined as the dominant factor, and the humidity abnormal starting point (1st hour) is selected as the starting segmentation point.

[0067] Based on the starting segmentation point, the end segmentation point is determined, including:

[0068] Based on the starting segmentation point, the cable monitoring sequence is traversed, and the cable damage data closest to the starting segmentation point in time is taken as the end segmentation point.

[0069] It should be noted that the monitoring sequence is traversed from the starting segmentation point (environmental abnormal starting point) to the nearest cable damage data (such as insulation degradation, temperature rise peak), which is taken as the end segmentation point, to define the complete time window of the damage event.

[0070] For example, the humidity abnormal starting point (1st hour) is taken as the starting segmentation point, and the data is traversed backward. At the 5th hour, the cable insulation resistance is found to drop from 100 MΩ to 20 MΩ (damage data), which is taken as the end segmentation point. Thus, the damage period is determined to be the 1st to 5th hour, during which the associated data (such as the salt spray concentration increasing from 600 μg / m³ to 800 μg / m³, the temperature rise reaching 80°C) is analyzed to analyze the corrosion acceleration effect under the synergistic action of humidity and salt, and to guide targeted cleaning or replacement of the damaged section of the cable.

[0071] Based on the starting segmentation point and the end segmentation point, the cable monitoring sequence is segmented to obtain a segmentation result, including:

[0072] Based on the starting segmentation point and the ending segmentation point, traversing the cable monitoring sequence, segmenting the cable monitoring sequence with the starting segmentation point, the ending segmentation point and the process data in the starting segmentation point and the ending segmentation point, and obtaining a segmentation result.

[0073] It should be noted that the monitoring data (such as humidity, dust, temperature rise process parameters) between the starting segmentation point (environmental abnormality starting point) and the ending segmentation point (damage occurrence point) are divided into independent segments to form a complete "abnormality-damage" event chain. After segmentation, the correlation between the environmental parameters and the cable state in this period can be focused on for analysis to identify the key path of damage evolution.

[0074] The starting segmentation point is the time when the humidity rapidly rises to 98% during the typhoon season (1st hour), and the ending segmentation point is the time when the insulation resistance drops to 20MΩ (5th hour). The segmentation range covers all data within the 1st to 5th hour: humidity remains >95%, salt mist concentration rises from 600μg / m³ to 800μg / m³, and cable temperature rises from 50°C to 80°C. Through segmentation, the 4-hour window is locked as a high-risk period, and analysis shows that the superposition of humidity and salt mist causes the corrosion rate of the insulation layer to accelerate, and the temperature rise accelerates aging, thereby specifically suggesting increasing the corrosion-resistant coating or shortening the cleaning cycle of this section of cable.

[0075] Based on the segmentation result, the cable monitoring sequence is traversed to obtain a traversal result, including:

[0076] Based on the segmentation result, the cable monitoring sequence is traversed, and if, in the sequence with dust data as the starting segmentation point or with humidity data as the starting segmentation point, humidity data or dust data appears, the humidity data or the dust data is marked as adjustment data;

[0077] In the traversal of the cable monitoring sequence, the starting segmentation point and the ending segmentation point in each sequence are recorded. If the starting segmentation point in the second sequence is different from the starting segmentation point in the first sequence, the traversal is performed at the same speed; if the starting segmentation point is the same as the starting segmentation point in the first sequence, the traversal is performed at a speed greater than the traversal speed of the first sequence; if the starting segmentation point is the same and the ending segmentation point is different in the second sequence, the third sequence is traversed at a speed less than the traversal speed of the first sequence.

[0078] In the traversal of the cable monitoring sequence, the time interval and data processing rate of adjacent data in each sequence are recorded, and the time interval is defined as the traversal time and the data processing rate is defined as the traversal speed.

[0079] It should be noted that the analysis efficiency and accuracy of the cable monitoring sequence are improved through dynamic marking and speed adjustment. The core logic is as follows: marking adjustment data: in the segment that has been segmented by humidity or dust as the starting point, if another environmental parameter anomaly appears again (such as a sharp increase in dust in the humidity segment), it is marked as “adjustment data”, indicating the risk of potential cross-influence. Speed dynamic adjustment: adjacent segment split points are different: environmental dominant factor changes (such as humidity→dust), the original speed needs to be maintained to avoid missing new patterns; the split points are the same: the same type of environmental risk continues (such as continuous humidity anomaly), the speed is increased to improve efficiency; the end points are different but the starting points are the same: the same type of environment causes different damage results (such as humidity leading to insulation degradation or temperature rise), the speed is reduced to refine the analysis. Record time and speed: provide a basis for subsequent calculation of traversal factor (time / speed ratio) to quantify monitoring response efficiency.

[0080] For example, a certain cable segment 1 has humidity anomaly (1st hour, humidity 98%) as the starting segmentation point, and the end point is the decrease of insulation resistance (5th hour). When traversing this segment, the salt mist concentration suddenly increases to 800 μg / m³ at the 3rd hour, which is marked as adjustment data.

[0081] Segment 2 has salt mist anomaly as the starting segmentation point (different from segment 1), and is traversed at the same speed, and the end point is temperature rise 80°C (7th hour).

[0082] Segment 3 again has salt mist as the starting segmentation point (same as segment 2), and is traversed at an increased speed, and the end point is partial discharge (8th hour).

[0083] Segment 4 still has salt mist as the starting segmentation point, but the end point changes to insulation breakdown (10th hour, different from segment 3), so the speed is reduced, and the peak value of salt mist concentration and breakdown voltage data of this segment are recorded. During the traversal process, the time consumed by each segment (such as 4 hours for segment 1) and the speed (such as 1.5 times for segment 3) are recorded. Finally, the traversal efficiency is evaluated through the traversal factor to guide the optimization of the algorithm.

[0084] Based on the traversal result, the cable monitoring mode is adjusted to obtain an adjusted cable monitoring mode, comprising:

[0085] Based on the traversal result, the traversal time, the traversal speed and the adjustment data are extracted;

[0086] The traversal time and the traversal speed are standardized to obtain standardized traversal time and traversal speed. The traversal factor is constructed with the standardized traversal time as the numerator and the standardized traversal speed as the denominator.

[0087] The number of adjustment data and the number of segmented sequences are extracted, the number of adjustment data is taken as the numerator, and the number of segmented sequences is taken as the denominator to construct an adjustment factor; if adjustment data appears in the sequence, and the next sequence appears with the adjustment data as the starting segmentation point, the numerator of the adjustment factor is increased by 1, and if the next sequence does not appear with the adjustment data as the starting segmentation point, the numerator of the adjustment factor is reduced by 1;

[0088] During the traversal of the cable monitoring sequence, if the traversal factor shows a downward trend and the adjustment factor shows an upward trend, switch to the adjusted monitoring mode;

[0089] Based on the adjusted monitoring mode, the data acquisition frequency, traversal time, traversal speed and spare part preparation time are extracted, and the total response time of the spare part priority is calculated through the total response time formula;

[0090] If the traversal factor shows an upward trend and the adjustment factor shows a downward trend, the original monitoring mode is maintained.

[0091] It should be noted that the switching to the adjusted monitoring mode includes, for example, if the traversal factor decreases and the adjustment factor increases, the sampling frequency of the humidity sensor is increased to once per minute, and real-time synchronous analysis of dust data is enabled.

[0092] By quantifying the monitoring efficiency (traversal factor) and the environmental disturbance intensity (adjustment factor), the monitoring strategy is dynamically optimized:

[0093] Traversal factor: standardize the traversal time and speed (e.g. time is reduced to [0, 1], speed is expanded to [1, 10]), calculate the "time / speed" ratio. The ratio decreases, indicating that the data volume processed per unit speed increases, and the efficiency improves; otherwise, the efficiency decreases.

[0094] Adjustment factor: measure the environmental mutation frequency with "adjustment data number / segment number". If the adjustment data is adopted as a segmentation point by the subsequent segmentation (molecule + 1), it indicates that the environmental disturbance is persistent, and the monitoring sensitivity needs to be improved; otherwise (molecule - 1) the disturbance is isolated, and the monitoring intensity can be reduced.

[0095] Strategy adjustment logic:

[0096] Traversal factor ↓ + adjustment factor ↑: efficiency is improved but environmental disturbance is intensified, switch to high-frequency monitoring or detailed analysis mode;

[0097] Traversal factor ↑ + adjustment factor ↓: efficiency is reduced and disturbance is reduced, maintain the current mode to save resources.

[0098] For example, the traversal time of a certain cable segment 1 is 4 hours, the speed is 1 (reference value), the normalized time is 0.8, the speed is 0.2, and the traversal factor = 0.8 / 0.2 = 4. Two salt spray concentration surges are found in segment 1 (adjusted data), the total segment number is 5, and the adjustment factor = 2 / 5 = 0.4.

[0099] The traversal time of segment 2 is 3 hours, the speed is increased to 1.5 times, the normalized time is 0.6, the speed is 0.3, and the traversal factor = 2 (decreasing trend). Three new adjustment data are added and the next segment is divided by salt spray, and the adjustment factor = (2+3) / (5+1) = 5 / 6 ≈ 0.83 (increasing trend). At this time, the high-frequency monitoring mode is switched, and the sampling rate of the salt spray sensor is increased.

[0100] The traversal factor of segment 3 is increased to 3 (efficiency decreases), the adjustment factor is decreased to 0.5 (interference decreases), and the original mode is restored to avoid resource waste. Through dynamic balance of efficiency and risk, precise monitoring and resource optimization are achieved in the salt spray frequent chemical plant environment.

[0101] Based on the adjusted cable monitoring mode, the cable spare parts are evaluated to obtain an evaluation result, including:

[0102] The total response time formula is as follows,

[0103]

[0104] Wherein, is the total response time, is the data acquisition frequency, is the frequency efficiency coefficient, is the normalized traversal time, is the normalized traversal speed, is the spare part time weight coefficient, is the spare part preparation time;

[0105] Based on the adjusted cable monitoring mode, the traversal factor of each segment sequence is extracted, and the TRT value of each cable segment is calculated by combining the total response time formula. By analyzing the change trend of TRT value and traversal factor, the loss risk level of cable spare parts is quantified, and the evaluation result is generated.

[0106] It should be noted that the first term of the total response time formula reflects the nonlinear influence of monitoring density on efficiency through the data acquisition frequency (f). , unit: times / hour, the number of times the sensor collects data in a unit of time). The single data acquisition time is (unit: hours), which decreases with the increase of frequency. The exponential term ​(Unit: 1 / time) is the frequency efficiency coefficient, which controls the effect of frequency on the attenuation rate; when Increase (high-frequency monitoring), single data collection time Decrease, at the same time As the exponential term decreases, its decay slows, and the total contribution value tends to stabilize; if Too low (low frequency monitoring), single data collection time is prolonged and The increase in frequency leads to a rapid decay of the exponential term and a significant decrease in the contribution value of the first term, reflecting the "efficiency loss caused by low-frequency monitoring." and Through logarithmic functions The correlation reflects the diminishing marginal returns of monitoring and analysis efficiency. When When the value is less than the preset threshold, the logarithmic growth is significant; when the value is greater than the preset threshold, the growth rate slows down, which is consistent with the law of diminishing returns in resource optimization. per unit hour, through coefficient Weighted ( >0), the adjustable spare parts time as a percentage of the total response time reflects supply chain priority. This refers to the cycle from generating demand to the availability of spare parts. Dimensionless, if =1, spare parts time is directly added; if <1, reduce its importance; if >1, highlighting the impact of spare parts delays; example of the formula exponent section, using... =0.1 1 / time: If =10 times / hour, then the time for a single data collection is =0.1 hours, the first term is: 1 / 10*e -0.1 / 10 ≈0.1*0.990≈0.099; if =2 times / hour (low frequency), then the time for a single sampling is 1 / 2 = 0.5 hours. The first term is: 0.5 * e -0.1 / 2 ≈0.5*0.951≈0.475; Conclusion: High-frequency monitoring ( Increasing the value significantly reduces the first contribution, reflecting the logic of "high-frequency acquisition improves efficiency"; the logarithmic function of the formula's logarithmic part. exist When the value is less than a preset threshold, the growth is approximately linear. The growth rate slows down when it exceeds a preset threshold. For example, if... =2, then log(3)≈1.1; if this value increases to 10, then log(11)≈2.4, the growth rate decreases significantly; the formula for spare parts passes the coefficient. Adjust weights flexibly, for example, when the supply chain is unstable. =2, emphasizing shortening The importance of;

[0107] The total response time (TRT) formula maps the relevance of the following three dynamic factors to resource scheduling: the data acquisition frequency term (f) ): quantifies the efficiency improvement of high-frequency monitoring, avoiding response delays caused by excessively long single acquisition times; the monitoring and analysis efficiency term (a ): reflects the diminishing returns law of segmented data analysis by constraining the marginal effect of ; the spare parts supply chain term (s ): highlights the impact of supply chain bottlenecks on maintenance delays by weighting the spare parts preparation time; based on the TRT value, high-delay cable segments (e.g., TRT > threshold) can be identified, and spare part replacement or cleaning tasks can be prioritized to reduce the risk of unplanned downtime;

[0108] The total response time (TRT) dynamically quantifies the maintenance priority of a cable segment by integrating data acquisition efficiency (f ), monitoring and analysis efficiency (a ), and spare parts supply chain delays (s ). The traversal factor (a ) reflects the efficiency of segmented data analysis:

[0109] A decrease in the traversal factor (e.g., from 4 to 2): indicates an improvement in monitoring efficiency, but may overlook details due to accelerated analysis, so it needs to be combined with the TRT value to determine whether to trigger spare part pre-inspection;

[0110] A dramatic fluctuation in the traversal factor (e.g., from 2 to 5): suggests frequent environmental disturbances, and the logarithmic term (a ) in the TRT formula can be used to identify high-risk segments and prioritize spare parts resources.

[0111] Example: The traversal factor of cable segment 1 decreases from 4 to 2, and its TRT value is 12 hours (mainly affected by the term ); due to salt spray interference, the traversal factor of segment 2 increases to 5, and the TRT value increases to 24 hours (with significant contribution from the logarithmic term). Segment 2 is determined to be high priority, and spare part replacement is triggered immediately; the TRT value of segment 1 is below the threshold, and only the patrol cycle needs to be extended.

[0112] Based on the evaluation results, determine the cable maintenance strategy, including:

[0113] Based on the evaluation results, determine the cable maintenance strategy by adjusting the absolute value of the change in the traversal factor before and after the cable monitoring method.

[0114] It needs to be explained that the absolute value of the traversal factor change before and after the calculation is adjusted, and the urgency of the maintenance strategy adjustment is quantified. The greater the absolute value, the more significant the impact of the monitoring mode switching on efficiency or risk coverage, and more active maintenance measures (such as shortening the cleaning cycle) need to be taken; the absolute value is small, and the original strategy is maintained.

[0115] Example: After adjustment, the traversal factor of a certain section changes from 3 to 1 (absolute value 2), it is determined that the cable in this section has a significant efficiency improvement but insufficient potential risk exposure, triggering high-frequency detection and early procurement of spare parts; the absolute value of another section changes is 0.5, then only optimize the cleaning plan, do not adjust the spare parts inventory.

[0116] Embodiment 2: On the basis of embodiment 1, a cable intelligent monitoring evaluation system, as shown in Figure 2 , comprises:

[0117] The environmental data acquisition and standardization module: real-time acquisition of cable humidity and dust data, integration according to time sequence; standardization processing of abnormal data, quantification of humidity and dust response strength; based on threshold value to judge abnormal state, dynamic marking of starting segmentation point;

[0118] The sequence segmentation and dynamic analysis module: according to the starting and ending segmentation points, the monitoring sequence is segmented; when traversing the segmented sequence, the speed is dynamically adjusted; mark the humidity or dust data as adjustment data, record the traversal time and speed change;

[0119] The spare parts demand evaluation module: based on the traversal time and traversal speed after standardization processing, the traversal factor is constructed, and the dynamic change of monitoring efficiency is quantified; through the ratio of the number of adjustment data to the number of segmented sequence, the adjustment factor is constructed, combined with the adjustment data in the sequence to trigger the dynamic correction of the denominator item, reflecting the persistence and cross-influence strength of environmental interference;

[0120] Resource dynamic scheduling module: according to the spare parts demand evaluation result, dynamically match the cleaning cycle, spare parts replacement plan and manpower scheduling priority; push the early warning information and generate the procurement order, optimize the inventory allocation.

[0121] The above only describes the preferred embodiments of the present application and does not limit the present application. Any modification, equivalent replacement, improvement, etc. within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for intelligent monitoring and evaluation of a cable, characterized in that The method comprises the following steps: S1: obtaining a cable monitoring sequence, extracting humidity data and dust data based on the cable monitoring sequence, and determining a starting segmentation point of the cable monitoring sequence based on the humidity data and the dust data; determining an ending segmentation point based on the starting segmentation point, which comprises traversing the cable monitoring sequence based on the starting segmentation point and taking cable damage data closest in time to the starting segmentation point as the ending segmentation point; S2: segmenting the cable monitoring sequence based on the starting segmentation point and the ending segmentation point to obtain a segmentation result, and traversing the cable monitoring sequence based on the segmentation result to obtain a traversal result; S3: adjusting the cable monitoring mode based on the traversal result to obtain an adjusted cable monitoring mode, which comprises: extracting traversal time, traversal speed and adjustment data based on the traversal result; standardizing the traversal time and the traversal speed to obtain standardized traversal time and traversal speed, constructing a traversal factor with the standardized traversal time as the numerator and the standardized traversal speed as the denominator; extracting the number of adjustment data and the number of segmentation sequences, constructing an adjustment factor with the number of adjustment data as the numerator and the number of segmentation sequences as the denominator; if adjustment data appears in a sequence and the next sequence appears with the adjustment data as the starting segmentation point, the numerator of the adjustment factor is increased by 1, and if the next sequence does not appear with the adjustment data as the starting segmentation point, the numerator of the adjustment factor is decreased by 1; if the traversal factor shows a downward trend and the adjustment factor shows an upward trend during the traversal process of the cable monitoring sequence, switching to the adjusted monitoring mode; extracting data acquisition frequency, traversal time, traversal speed and spare part preparation time based on the adjusted monitoring mode, and calculating the total response time of the spare part priority through a total response time formula; if the traversal factor shows an upward trend and the adjustment factor shows a downward trend, maintaining the original monitoring mode; evaluating the cable spare parts based on the adjusted cable monitoring mode to obtain an evaluation result; S4: determining a cable maintenance strategy based on the evaluation result.

2. The method of claim 1, wherein the method further comprises, The method comprises the following steps: The cable monitoring sequence comprises dust data, humidity data, process data caused by the dust data and the humidity data, and final cable damage data; arranging the dust data, the humidity data, the process data and the cable damage data in time sequence to form the cable monitoring sequence; extracting the humidity data and the dust data in the cable monitoring sequence based on the cable monitoring sequence.

3. The method of claim 1, wherein the method further comprises, The method comprises the following steps: If the humidity data and the dust data in the cable monitoring sequence show abnormalities at the same time, the response time and the influence intensity of the humidity data and the dust data are standardized to obtain a standardized processing result, and the humidity data and the dust data are defined as first humidity data and first dust data; Based on the standardized processing result, the first humidity data and the first dust data are compared, if the first humidity data is greater than the first dust data, the humidity data is taken as the starting segmentation point, if the first humidity data is less than the first dust data, the dust data is taken as the starting segmentation point; If the humidity data and the dust data in the cable monitoring sequence do not show abnormalities, the humidity data or the dust data is taken as the starting segmentation point of the cable monitoring sequence.

4. The method of claim 1, wherein the method further comprises, Based on the starting segmentation point and the end segmentation point, the cable monitoring sequence is segmented to obtain a segmentation result, including: Based on the starting segmentation point and the end segmentation point, the cable monitoring sequence is traversed, and the cable monitoring sequence is segmented based on the starting segmentation point, the end segmentation point and the process data in the starting segmentation point and the end segmentation point to obtain a segmentation result.

5. The method of claim 1, wherein the method further comprises, Based on the segmentation result, the cable monitoring sequence is traversed to obtain a traversal result, including: Based on the segmentation result, the cable monitoring sequence is traversed, if the humidity data or the dust data appears in the sequence with the dust data as the starting segmentation point or the humidity data as the starting segmentation point in the traversal process, the humidity data or the dust data is marked as adjustment data; In the traversal of the cable monitoring sequence, the starting segmentation point and the end segmentation point in each sequence are recorded, if the starting segmentation point in the second sequence is different from the starting segmentation point in the first sequence, the traversal is performed at the same speed, if the starting segmentation point is the same as the starting segmentation point in the first sequence, the traversal is performed at a speed greater than the traversal speed of the first sequence, if the starting segmentation point in the second sequence is the same and the end segmentation point is different, the traversal of the third sequence is performed at a speed less than the traversal speed of the first sequence; In the traversal of the cable monitoring sequence, the time interval and the data processing rate of adjacent data in each sequence are recorded, the time interval is defined as the traversal time, and the data processing rate is defined as the traversal speed.

6. The method of claim 1, wherein the method further comprises, Based on the adjusted cable monitoring mode, the cable spare parts are evaluated to obtain an evaluation result, including: The total response time formula is as follows, wherein, is the total response time, is the data acquisition frequency, is the frequency efficiency coefficient, is the normalized traversal time, is the normalized traversal speed, is the spare parts time weight coefficient, is the spare parts preparation time; Based on the adjusted cable monitoring mode, the traversal factor of each sequence is extracted, the TRT value of each cable segment is calculated by combining the total response time formula, and the loss risk level of the cable spare parts is quantified by analyzing the change trend of the TRT value and the traversal factor to generate the evaluation result.

7. The method of claim 1, wherein the method further comprises, Based on the evaluation result, the cable maintenance strategy is determined, including: Based on the evaluation result, the absolute value of the change degree of the traversal factor before and after the cable monitoring mode is adjusted to determine the cable maintenance strategy.

8. A cable intelligent monitoring and evaluation system for implementing the cable intelligent monitoring and evaluation method of any one of claims 1-7, characterized in that, Including: The environmental data acquisition and standardization module: real-time acquisition of cable humidity and dust data, time sequence integration; Standardize the abnormal data, quantify the response intensity of humidity and dust; judge the abnormal state based on threshold, dynamically mark the starting segmentation point; Sequence segmentation and dynamic analysis module: segment the monitoring sequence according to the starting and ending segmentation points; dynamically adjust the speed when traversing the segmented sequence; mark the humidity or dust data as adjustment data, record the traversal time and speed change; Spare parts demand evaluation module: based on the traversal time and traversal speed after standardization, construct the traversal factor to quantify the dynamic change of monitoring efficiency; through the ratio of the number of adjustment data to the number of segmented sequences, construct the adjustment factor, combine the adjustment data in the sequence to trigger the dynamic correction of the molecular term of the subsequent segmentation point, reflect the persistence and cross-influence intensity of environmental interference; Resource dynamic scheduling module: dynamically match the cleaning cycle, spare parts replacement plan and manpower scheduling priority according to the spare parts demand evaluation result; Push the early warning information and generate the purchase order, optimize the inventory allocation.

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