Maintenance dynamic processing method and system for cable installation characteristic records

Abstract

The invention provides a maintenance dynamic processing method and system for cable installation characteristic records, and relates to the field of data processing, and the method comprises the steps: setting the recording precision of a plurality of events according to the priority scores of a plurality of events; optimizing the initial storage proportion of the events by utilizing the average value of the storage resource consumption of the events and the priority scores of the events to obtain an optimized storage proportion of the events; adjusting an event optimization storage proportion according to the predicted abnormal event probability distribution to obtain an event adaptive storage proportion; and generating a dynamic data processing strategy based on the multiple event recording accuracies and the event adaptive storage proportion. The method aims at solving the technical problems that in practical application, abnormal data processing precision is insufficient, storage resource utilization efficiency is low, response is not timely and the like due to the fact that an existing data recording method cannot achieve real-time optimization of recording precision and a storage strategy. The abnormal data processing precision and response efficiency can be effectively improved, and meanwhile the utilization rate of storage resources is increased.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a method and system for dynamic maintenance processing of cable installation feature records. Background Technology

[0002] During cable installation, to ensure project quality and operational safety, it is usually necessary to record and manage any abnormal events that may occur during construction. In existing technologies, abnormal event data recording often employs a fixed template-based storage method. This means that regardless of changes in the risk level, frequency of occurrence, or construction environment of the abnormal event, it is recorded with the same precision standard and allocated fixed storage resources. While this approach achieves basic archiving of abnormal events, it has significant shortcomings in practical applications.

[0003] On the one hand, existing methods fail to collect and record high-precision data based on the importance of high-risk, high-impact anomalies, resulting in information gaps in subsequent problem tracing and accident analysis. On the other hand, some low-risk, frequent but minor anomalies occupy a large amount of storage space, wasting system resources. More importantly, existing anomaly recording methods do not consider dynamic changes in the construction environment (such as construction area, climate conditions, and work methods), making it impossible to intervene and allocate resources in advance based on future anomaly risk trends in the time zone, thus limiting the intelligence and adaptability of data processing. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic maintenance processing method and system for cable installation feature records, addressing the shortcomings of existing data recording methods for abnormal events during cable installation. These methods lack a dynamic adjustment mechanism based on changes in the construction scenario, making it impossible to achieve real-time optimization of recording accuracy and storage strategies. Consequently, they suffer from technical problems in practical applications, such as insufficient accuracy in abnormal data processing, low efficiency in storage resource utilization, and untimely response. The invention includes:

[0005] In a first aspect, the present invention provides a dynamic maintenance processing method for cable installation feature records, comprising: performing event risk assessments based on multiple cable installation anomaly event types to obtain multiple comprehensive risk coefficients; obtaining the event occurrence frequency of the multiple cable installation anomaly event types within a preset historical period, calculating multiple event priority scores in combination with the multiple comprehensive risk coefficients, and setting multiple event recording accuracies; setting an initial event storage ratio based on the event occurrence frequency ratio within the preset historical period, optimizing the initial event storage ratio using the average event storage resource consumption and the multiple event priority scores to obtain an optimized event storage ratio; predicting multiple types of anomalies based on cable installation construction characteristics in a future time zone, outputting the predicted anomaly event probability distribution, adjusting the optimized event storage ratio to obtain an event-adaptive storage ratio; and generating a dynamic data processing strategy based on the multiple event recording accuracies and the event-adaptive storage ratio to perform maintenance processing on the cable installation anomaly data in the future time zone.

[0006] Preferably, the maintenance dynamic processing method for cable installation feature records further includes: configuring an event risk assessment strategy, wherein the event risk assessment strategy includes multiple event risk assessment indicators, and each event risk assessment indicator is identified with an indicator weight, wherein the event risk assessment indicators include at least event severity, abnormal duration and processing complexity; according to the event risk assessment strategy, event risk assessment is performed on multiple cable installation abnormal event types respectively, and multiple comprehensive risk coefficients are output.

[0007] Preferably, the maintenance dynamic processing method for cable installation feature records further includes: statistically analyzing the occurrence frequency of the multiple cable installation abnormal event types within a preset historical time period to obtain multiple event occurrence frequencies; performing weighted calculations on the multiple event occurrence frequencies and the multiple comprehensive risk coefficients to obtain multiple event priority scores, wherein the event priority scores are positively correlated with the event occurrence frequency and the comprehensive risk coefficient; and based on a priority-dimensionality reduction ratio mapping table, matching and obtaining multiple data dimensionality reduction ratios according to the multiple event priority scores, setting them as multiple event recording precisions, wherein the data dimensionality reduction ratios are negatively correlated with the event priority scores.

[0008] Preferably, the maintenance dynamic processing method for cable installation feature records further includes: obtaining the average storage resource consumption of multiple events of the multiple cable installation abnormal event types within a preset historical period; calculating multiple proportional adjustment coefficients based on the average storage resource consumption of the multiple events and multiple event priority scores, wherein the proportional adjustment coefficients are negatively correlated with the average storage resource consumption of events and positively correlated with the event priority scores; correcting the initial storage ratio of the events based on the multiple proportional adjustment coefficients, and outputting the optimized storage ratio of the events.

[0009] Preferably, the maintenance dynamic processing method for cable installation feature records further includes: acquiring cable installation construction features in a future time zone, wherein the cable installation construction features include at least construction scene information, construction environment information, construction method, and worker characteristics; pre-training anomaly analysis plugin, wherein the anomaly analysis plugin includes N anomaly event prediction branches; assessing environmental fluctuation based on the construction environment information and outputting an environmental fluctuation coefficient; setting the ratio of the environmental fluctuation coefficient to the average of historical environmental fluctuation coefficients as a branch adjustment coefficient, multiplying it by the initial number of branches and rounding it to obtain the number of adapted branches K, wherein K is less than or equal to N, and the initial number of branches is 3; randomly selecting K anomaly event prediction branches from the N anomaly event prediction branches, predicting K event probability distributions based on the construction scene information, construction environment information, construction method, and worker characteristics, and calculating the average to obtain the predicted anomaly event probability distribution.

[0010] Preferably, the maintenance dynamic processing method for cable installation feature records further includes: collecting a sample cable installation construction feature set based on historical cable installation feature records, and statistically analyzing the proportion of abnormal events corresponding to multiple cable installation abnormal event types within the historical time zone for each sample cable installation construction feature, setting this as the sample abnormal event probability, constructing a sample abnormal event probability distribution, and obtaining a sample abnormal event probability distribution set, wherein the duration of the historical time zone is the same as the duration of the future time zone; using the sample cable installation construction feature set and the sample abnormal event probability distribution set as training data, and dividing them equally into N parts to obtain N training sets, where N is an integer greater than 10; using the N training sets to train the machine learning model to convergence, obtaining N abnormal event prediction branches, and combining them to obtain an abnormal event analysis plugin.

[0011] Preferably, the maintenance dynamic processing method for cable installation feature records further includes: calculating the probability percentage of each cable installation abnormal event type based on the predicted abnormal event probability distribution to obtain multiple probability percentages; calculating the frequency percentage of each cable installation abnormal event type based on the occurrence frequency of multiple events within a preset historical period to obtain multiple frequency percentages; calculating multiple adjustment coefficients based on the multiple probability percentages and multiple frequency percentages, and adjusting the storage ratio corresponding to multiple cable installation abnormal event types in the event optimized storage ratio to obtain the event-adaptive storage ratio, wherein the adjustment coefficient is the ratio of the probability percentage to the frequency percentage.

[0012] Preferably, the maintenance dynamic processing method for cable installation feature records further includes: if the data storage volume of a single cable installation abnormal event type meets the upper limit of the corresponding event adaptation storage ratio, then a dynamic cleanup mechanism is executed, wherein the dynamic cleanup mechanism calculates the similarity between the stored data of each abnormal event and the stored data of other abnormal events in a single cable installation abnormal event type, sums them to obtain the overall similarity of each abnormal event, and removes the abnormal event with the highest overall similarity.

[0013] Secondly, the present invention also provides a maintenance dynamic processing system for cable installation feature records, used to execute a maintenance dynamic processing method for cable installation feature records as described in the first aspect, comprising: an event risk assessment module, used to perform event risk assessment based on multiple cable installation abnormal event types, and obtain multiple comprehensive risk coefficients; a recording accuracy setting module, used to obtain the event occurrence frequency of the multiple cable installation abnormal event types within a preset historical period, calculate multiple event priority scores in combination with the multiple comprehensive risk coefficients, and set multiple event recording accuracies; a storage ratio optimization module, used to set an initial event storage ratio based on the event occurrence frequency ratio within the preset historical period, optimize the initial event storage ratio using the average event storage resource consumption and the multiple event priority scores, and obtain an optimized event storage ratio; an optimized storage ratio adjustment module, used to predict multiple types of abnormal events based on cable installation construction characteristics in a future time zone, output the predicted abnormal event probability distribution, adjust the optimized event storage ratio, and obtain an event-adaptive storage ratio; and a data maintenance processing module, used to generate a dynamic data processing strategy based on the multiple event recording accuracies and the event-adaptive storage ratio, and perform maintenance processing on the cable installation abnormal data in the future time zone.

[0014] The embodiments of the present invention have the following advantages:

[0015] By conducting event risk assessments based on multiple cable installation anomaly event types, multiple comprehensive risk coefficients are obtained. Next, the frequency of occurrence of each of these cable installation anomaly event types within a preset historical period is obtained, and multiple event priority scores are calculated based on the comprehensive risk coefficients. Multiple event recording accuracies are also set. Then, an initial event storage ratio is set based on the proportion of events occurring within the preset historical period. This initial storage ratio is optimized using the average storage resource consumption of multiple events and the multiple event priority scores to obtain an optimized event storage ratio. Furthermore, multiple types of anomalies are predicted based on cable installation construction characteristics in future time zones, and the predicted anomaly event probability distribution is output. The optimized event storage ratio is then adjusted to obtain an event-adaptive storage ratio. Finally, a dynamic data processing strategy is generated based on the multiple event recording accuracies and the event-adaptive storage ratio to maintain and process the cable installation anomaly data in the future time zone. In other words, by introducing multi-dimensional parameters such as abnormal event risk assessment, priority scoring, historical occurrence frequency analysis, and future construction scenario prediction, a dynamically adjustable data processing mechanism is constructed. This mechanism can optimize recording accuracy and storage strategy in real time based on the risk level, probability of occurrence, and changes in the construction environment of abnormal events during cable installation. This enables the key recording of high-risk events and intelligent compression of low-value events, effectively improving the accuracy and response efficiency of abnormal data processing, while also increasing the utilization rate of storage resources and enhancing data management and adaptability in complex construction environments. Attached Figure Description

[0016] Figure 1 This is a flowchart of the maintenance dynamic processing method for cable installation feature recording according to the present invention;

[0017] Figure 2 This is a schematic diagram of the maintenance dynamic processing system for recording cable installation features according to the present invention.

[0018] Explanation of reference numerals in the attached figures:

[0019] Event risk assessment module 11, recording accuracy setting module 12, storage ratio optimization module 13, optimized storage ratio adjustment module 14, data maintenance and processing module 15. Detailed Implementation

[0020] This invention provides a dynamic processing method and system for recording cable installation characteristics. It addresses the shortcomings of existing methods for recording abnormal events during cable installation, which lack a dynamic adjustment mechanism based on changes in the construction scenario. This results in insufficient accuracy in abnormal data processing, low storage resource utilization, and untimely response in practical applications. By introducing multi-dimensional parameters such as abnormal event risk assessment, priority scoring, historical frequency analysis, and future construction scenario prediction, a dynamically adjusted data processing mechanism is constructed. This mechanism optimizes recording accuracy and storage strategy in real time based on the risk level, probability of occurrence, and changes in the construction environment of abnormal events during cable installation. This enables focused recording of high-risk events and intelligent compression of low-value events, effectively improving the accuracy and response efficiency of abnormal data processing, while also increasing storage resource utilization and enhancing data management and adaptability in complex construction environments.

[0021] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. 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. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0022] Example 1, please refer to the appendix. Figure 1 This invention provides a maintenance dynamic processing method for cable installation feature records, applied to a maintenance dynamic processing system for cable installation feature records, specifically including the following steps:

[0023] S10: Based on multiple cable installation abnormality event types, conduct event risk assessments separately to obtain multiple comprehensive risk coefficients.

[0024] Furthermore, step S10 of the present invention further includes:

[0025] S11: Configure an event risk assessment strategy, wherein the event risk assessment strategy includes multiple event risk assessment indicators, and each event risk assessment indicator is identified with an indicator weight. The event risk assessment indicators include at least the severity of the event, the duration of the anomaly, and the complexity of the processing; S12: According to the event risk assessment strategy, perform event risk assessment on multiple cable installation anomaly event types respectively, and output multiple comprehensive risk coefficients.

[0026] Specifically, firstly, an event risk assessment strategy is configured. This strategy includes multiple event risk assessment indicators, which at least include event severity, duration of anomaly, and handling complexity. Event severity measures the potential impact of an anomaly on cable installation quality, safety, and subsequent operation. Higher severity indicates greater potential harm, and the assessment result should be given higher weight to ensure such events receive focused attention and detailed recording. For example, anomalies causing cable insulation damage or significant construction delays should have a significantly higher severity score than minor appearance defects. Duration of anomaly refers to the time from the occurrence of the anomaly to its elimination or return to normal. Longer duration indicates a more significant impact on the construction process and system stability, and a correspondingly higher risk level. Handling complexity assesses the ease or difficulty of resolving the anomaly, including the required manpower, material resources, technical means, and time costs. Higher complexity indicates greater resource consumption and potentially higher safety risks and construction difficulties, and should therefore account for a larger proportion of the risk assessment to guide the rational allocation of maintenance resources and storage strategies. In addition, each event risk assessment indicator is labeled with an indicator weight, which is used to reflect the relative importance of the indicator in the overall risk assessment. The weight setting can be adjusted according to actual construction needs, historical experience and expert review results to ensure that the risk assessment results meet the actual project management requirements. By multiplying the scores of each indicator by the corresponding weight and then summing them up, the comprehensive risk coefficient of the abnormal event is obtained, which provides a scientific basis for subsequent event prioritization and dynamic data processing.

[0027] Next, multiple cable installation anomaly event types are acquired. This involves identifying and collecting various possible anomalies from actual occurrences or historical records at the construction site. These anomaly event types cover various problems and defects that may be encountered during cable installation, serving as the objects of subsequent risk assessment and data processing. For example, common anomaly event types include poor cable joints, damaged insulation, excessive bending during laying, missing sealing of wall penetrations, and damaged cable sheaths. Then, according to the aforementioned event risk assessment strategy, event risk assessments are conducted for each of the multiple cable installation anomaly event types. First, based on the performance of each anomaly event type on each assessment indicator, a corresponding score is assigned. For example, if the severity of a poor joint event is high, a higher score is assigned to that indicator; if the event has low processing complexity, a lower score is assigned to the corresponding indicator. Next, each indicator score is multiplied by its set weight, and after weighted summation, a comprehensive risk coefficient for that anomaly event type is obtained. This coefficient quantitatively reflects the overall risk level of the event. Multiple comprehensive risk coefficients are output, which will serve as important references for subsequent event priority scoring, recording accuracy settings, and storage resource allocation.

[0028] S20: Obtain the frequency of occurrence of the multiple cable installation abnormal event types within a preset historical period, calculate multiple event priority scores by combining the multiple comprehensive risk coefficients, and set multiple event recording precision.

[0029] Furthermore, step S20 of the present invention also includes:

[0030] S21: Statistically calculate the occurrence frequency of the multiple cable installation abnormality event types within a preset historical time period to obtain multiple event occurrence frequencies; S22: Perform weighted calculation on the multiple event occurrence frequencies and the multiple comprehensive risk coefficients to obtain multiple event priority scores, wherein the event priority score is positively correlated with the event occurrence frequency and the comprehensive risk coefficient; S23: Based on the priority-dimensionality reduction ratio mapping table, obtain multiple data dimensionality reduction ratios according to the multiple event priority scores, and set them as multiple event recording precisions, wherein the data dimensionality reduction ratio is negatively correlated with the event priority score.

[0031] Specifically, firstly, for the identified multiple cable installation anomaly event types, the actual number of occurrences within a preset historical period is counted. This historical period can be the most recent half month, the most recent month, or other reasonable time range. By analyzing construction logs, monitoring system data, or inspection records, the number of occurrences of each anomaly event is calculated. Combined with the total number of events within this period, the occurrence frequency of each anomaly event is obtained. The occurrence frequency reflects the relative probability of an anomaly event occurring during actual construction. The higher the frequency of an anomaly event, the greater its impact on construction and the more attention it needs to receive. This process yields the occurrence frequencies of multiple events.

[0032] Next, a weighted calculation is performed on the occurrence frequency of the multiple events and the multiple comprehensive risk coefficients. That is, the occurrence frequency and comprehensive risk coefficient are each assigned a certain weight, and then the two are summed to obtain a priority score. The event priority score is positively correlated with both the occurrence frequency and the comprehensive risk coefficient. That is, the higher the occurrence frequency of an event, the higher its priority score, indicating that the event is more common in construction and requires priority attention and handling. The higher the comprehensive risk coefficient of an event, the higher its priority score, indicating that the event has greater potential harm and affects construction safety and quality. This score comprehensively reflects the harm of the abnormal event and its actual frequency of occurrence in the construction process, and is a key basis for judging the importance of the event.

[0033] A priority-dimensionality reduction ratio mapping table is constructed. First, the priority scores of various anomalies during past construction processes are summarized, along with corresponding data storage effectiveness, processing efficiency, and data quality feedback. Next, the effectiveness of event data in anomaly diagnosis, tracing, and handling under different dimensionality reduction ratios is evaluated, identifying the critical points of data performance at different levels of precision. Further, combining historical analysis results and project requirements, a mapping relationship between priority scores and dimensionality reduction ratios is established. This ensures that high-priority events have lower dimensionality reduction ratios to guarantee information integrity, while low-priority events allow for greater compression to save resources, resulting in the priority-dimensionality reduction ratio mapping table. Here, the priority score reflects the overall importance of the anomaly event; a higher score indicates a greater event risk, higher frequency of occurrence, and higher processing priority. The data dimensionality reduction ratio represents the degree of compression and simplification of the original data during data collection and storage. A larger ratio means a higher degree of dimensionality reduction and lower data precision; a smaller ratio means more complete data retention and higher precision. There is a negative correlation between the two: a higher priority score corresponds to a smaller dimensionality reduction ratio, meaning high-priority events require higher recording precision and more complete data storage, while low-priority events allow for greater dimensionality reduction and compression to save storage resources.

[0034] Next, based on the priority-dimensionality reduction ratio mapping table, multiple data dimensionality reduction ratios are obtained by matching the multiple event priority scores, and these are set as multiple event recording accuracies. That is, the matching interval is searched in the mapping table according to the score value to obtain the corresponding dimensionality reduction ratio, which serves as the recording accuracy standard for that event. Through dynamic matching based on the priority-dimensionality reduction ratio mapping table, intelligent allocation of abnormal event data recording accuracy can be achieved, ensuring both the integrity and high quality of critical event data, effectively controlling overall storage resource consumption, and improving system response speed and processing efficiency.

[0035] S30: Set the initial storage ratio of events based on the proportion of events occurring within a preset historical period, and optimize the initial storage ratio of events by using the average storage resource consumption of multiple events and the priority scores of the multiple events, to obtain the optimized storage ratio of events.

[0036] Furthermore, step S30 of the present invention also includes:

[0037] S31: Obtain the average storage resource consumption of the multiple cable installation abnormal event types within a preset historical time period; S32: Calculate multiple proportional adjustment coefficients based on the average storage resource consumption of the multiple events and the multiple event priority scores, wherein the proportional adjustment coefficients are negatively correlated with the average storage resource consumption of the events and positively correlated with the event priority scores; S33: Correct the initial storage ratio of the events based on the multiple proportional adjustment coefficients and output the optimized storage ratio of the events.

[0038] Specifically, firstly, the average storage resource consumption of multiple cable installation anomaly event types within a preset historical period is obtained. In the data management process of cable installation anomaly events, different types of anomalies have different data storage resource consumption due to differences in data volume, data type, and recording frequency. By statistically analyzing the actual storage resources consumed by each anomaly event type within the preset historical period, the average storage resource consumption of each event type is calculated. This average reflects the average resource occupancy level of the event type in historical data storage, and is usually measured by indicators such as storage capacity (e.g., MB, GB) or storage duration.

[0039] Next, to rationally allocate storage resources and optimize data storage strategies, a proportional adjustment coefficient needs to be calculated based on the storage resource consumption of events and their importance (reflected by event priority scores). This coefficient is used to dynamically adjust the event storage ratio, ensuring that high-priority events with low resource consumption receive more storage space, while events with high resource consumption are appropriately compressed while maintaining their importance. Then, multiple proportional adjustment coefficients are calculated based on the average storage resource consumption of multiple events and multiple event priority scores. The proportional adjustment coefficient is negatively correlated with the average event storage resource consumption; that is, the higher the resource consumption of an event, the smaller its corresponding proportional adjustment coefficient should be, thereby reducing the storage ratio of that event and avoiding resource waste. The proportional adjustment coefficient is positively correlated with the event priority score; that is, the higher the event priority, the larger its proportional adjustment coefficient, reflecting that the event should be given priority to receive sufficient storage space to ensure information integrity. Through the above calculations, the importance of abnormal events and storage resource limitations can be balanced, effectively preventing high-resource-consuming events from excessively occupying storage space, while ensuring the data quality and integrity of critical high-risk events, thereby improving the overall efficiency of data management and system performance.

[0040] First, based on the percentage of occurrences of each type of cable installation anomaly within a preset historical time period, the corresponding initial storage ratio for each event is determined. Events with higher occurrence frequencies should be allocated more storage resources; therefore, the initial storage ratio is directly proportional to the event's occurrence frequency. This method allocates a basic storage space ratio for each event, serving as the starting point for subsequent optimization adjustments. Next, the initial storage ratio for each event is corrected using multiple ratio adjustment coefficients. This involves multiplying the initial storage ratio for each event by its corresponding ratio adjustment coefficient to obtain the corrected storage ratio. However, in actual calculations, after correcting the initial storage ratios of multiple anomalies using ratio adjustment coefficients, the resulting adjusted event storage ratio parameters typically do not strictly satisfy the condition that their sum is 1 (i.e., the total allocation ratio of overall storage resources). To ensure the rationality and completeness of all event storage ratios, these adjusted ratio parameters need further standardization. This involves normalizing the storage ratio parameter for each event by dividing each ratio value by the sum. After normalization, the sum of the storage ratios for all events is strictly equal to 1, forming a reasonable and standardized set of event-adapted storage ratios, from which the optimized event storage ratio is output. This normalization process ensures the integrity and rationality of the event storage ratio, avoiding resource allocation deviations caused by ratios not equal to 1, and guaranteeing the scientific validity and feasibility of the entire cable installation anomaly event storage scheme. This correction step can dynamically adjust the allocation of event storage resources, taking into account both the importance (priority) of events and storage costs, to achieve a more reasonable resource configuration.

[0041] S40: Based on the characteristics of cable installation and construction in the future time zone, predict multiple types of abnormal events, output the probability distribution of predicted abnormal events, and adjust the optimized storage ratio of the events to obtain the event-adaptive storage ratio.

[0042] Furthermore, step S40 of the present invention further includes:

[0043] S41: Obtain cable installation construction characteristics in a future time zone, wherein the cable installation construction characteristics include at least construction scene information, construction environment information, construction method and operator characteristics.

[0044] Specifically, this involves acquiring cable installation construction characteristics within a future time zone. These characteristics include at least construction scenario information, construction environment information, construction methods, and worker characteristics. The construction scenario refers to the specific physical and engineering environment in which the cable installation takes place, including but not limited to the geographical location of the construction area, the construction location (underground, overhead, tunnel, etc.), the condition of surrounding facilities, and spatial constraints. Different construction scenarios significantly impact the difficulty of cable installation and the types of potential anomalies. For example, construction within tunnels, with its confined space, can lead to more mechanical damage and anomalies. The construction environment involves natural and man-made environmental factors during construction, such as meteorological conditions (temperature, humidity, rainfall, wind speed, etc.), environmental temperature and humidity changes, construction noise levels, and the construction site itself. Environmental factors such as the safety environment can affect the performance of cable materials, the condition of construction equipment, and the operational quality of workers, thus influencing the probability of abnormal events. The construction method refers to the specific technology and process used in cable installation, including laying methods (such as traction laying and manual laying), fixing methods, connection processes, and protective measures. Different construction methods correspond to different operational complexities and potential risks. For example, mechanical traction laying may cause strain abnormalities, while manual laying is more prone to poor joints. The characteristics of the workers refer to the information of personnel involved in cable installation, including their experience level, skill level, headcount, and team structure. The technical proficiency and teamwork ability of the workers directly affect the construction quality and the incidence of abnormal events.

[0045] S42: Pre-trained anomaly analysis plugin, wherein the anomaly analysis plugin includes N anomaly prediction branches.

[0046] Furthermore, step S42 of the present invention further includes:

[0047] S421: Based on historical cable installation characteristic records, collect sample cable installation construction characteristic sets, and statistically analyze the proportion of abnormal events corresponding to multiple cable installation abnormal event types within the historical time zone for each sample cable installation construction characteristic. Set this as the sample abnormal event probability, construct the sample abnormal event probability distribution, and obtain the sample abnormal event probability distribution set. The duration of the historical time zone is the same as the duration of the future time zone. S422: Use the sample cable installation construction characteristic set and the sample abnormal event probability distribution set as training data, and divide them into N equal parts to obtain N training sets, where N is an integer greater than 10. S423: Use the N training sets to train the machine learning model until convergence, obtain N abnormal event prediction branches, and combine them to obtain an abnormal event analysis plugin.

[0048] Specifically, firstly, based on historical cable installation characteristic records, several construction cycles are extracted from the existing historical cable installation records (each cycle's duration is the same as the future predicted time zone, such as 7 days or 1 month). From these, several sample construction characteristic sets similar to the construction conditions in the future time zone are extracted to construct a sample cable installation construction characteristic set. Next, for each set of sample construction characteristics, within its corresponding historical construction cycle, the occurrence frequency of various cable installation anomalies under that characteristic condition is counted. The frequency of each type of anomaly is divided by the total number of anomalies in that sample cycle to obtain its anomaly proportion under that characteristic condition. This proportion reflects the relative probability of a certain type of anomaly under a specific construction characteristic, and is set as the sample anomaly probability. The anomaly type of each sample and its corresponding probability are combined to form a sample anomaly probability distribution, resulting in a sample anomaly probability distribution set.

[0049] Next, the sample cable installation construction feature set and the sample abnormal event probability distribution set are used as training data. To improve the generalization ability of the model training and prevent the model from overfitting to a small amount of data, the training data is divided into N equal subsets, where N is an integer greater than 10. Common subsets include N = 10, 20, and 50, and the specific value can be flexibly set according to the total number of samples and the model complexity. Then, the machine learning model is trained using the N training sets respectively. Common models include BP neural networks, support vector machines, and random forests. Taking BP neural networks as an example, a typical feedforward fully connected neural network consists of an input layer, one or more hidden layers, and an output layer. For the cable installation abnormal event prediction task, the model input is the construction feature vector, and the output is the predicted probability of various abnormal events. During training, firstly, the construction features of the sample are input into the network, passing through the hidden and output layers sequentially to obtain the predicted probability distribution of the abnormal events corresponding to the current sample. Then, the cross-entropy loss function is used to calculate the error between the predicted result and the actual probability distribution of abnormal events in the sample. Next, based on the error result, the gradient information of each neuron in each layer is calculated backward using the chain rule to update the network parameters. Furthermore, gradient descent (such as SGD, Adam, etc.) is used to iteratively adjust the weights and biases. When the validation set loss does not decrease for several consecutive rounds, or reaches the preset maximum number of training rounds (such as 1000 times), the model is considered to have converged, and the training process is terminated. N abnormal event prediction branches are obtained, which are combined to obtain the abnormal event analysis plugin.

[0050] S43: Evaluate the environmental fluctuation based on the construction environment information and output the environmental fluctuation coefficient; S44: Set the ratio of the environmental fluctuation coefficient to the average of the historical environmental fluctuation coefficients as the branch adjustment coefficient, multiply it by the initial number of branches and round it to obtain the number of adapted branches K, where K is less than or equal to N, and the initial number of branches is 3; S45: Randomly select K abnormal event prediction branches from the N abnormal event prediction branches, predict the probability distribution of K events based on the construction scenario information, construction environment information, construction method and worker characteristics, and calculate the mean to obtain the predicted abnormal event probability distribution.

[0051] Specifically, firstly, environmental fluctuation is assessed based on the construction environment information. This information, including temperature variation range (diurnal temperature range, seasonal span), humidity fluctuation, and other parameters, is crucial for cable installation quality and the risk of abnormal events. Next, the environmental stability within the current construction period is evaluated using statistical methods such as standard deviation and rate of change, yielding an environmental fluctuation coefficient. Then, the ratio of this environmental fluctuation coefficient to the historical average environmental fluctuation coefficient is set as the branch adjustment coefficient. This coefficient is multiplied by the initial number of branches and rounded to obtain the number of suitable branches, K, where K is less than or equal to N. The initial number of branches is 3, meaning that the more unstable the environment, the more branches are involved in the prediction, improving the robustness and redundancy of the prediction and enhancing the overall anomaly prediction capability. Conversely, reducing the number of branches saves computational resources when the environment is stable.

[0052] Then, K abnormal event prediction branches are randomly selected from the N abnormal event prediction branches, and the probability distributions of K events are predicted based on the construction scenario information, construction environment information, construction method and worker characteristics; the mean of the K event probability distributions is further calculated to obtain the predicted abnormal event probability distribution.

[0053] Furthermore, step S40 of the present invention further includes:

[0054] S46: Based on the predicted abnormal event probability distribution, calculate the probability percentage of each cable installation abnormal event type to obtain multiple probability percentages; S47: According to the occurrence frequency of multiple events within a preset historical period, calculate the frequency percentage of each cable installation abnormal event type to obtain multiple frequency percentages; S48: Calculate multiple adjustment coefficients based on the multiple probability percentages and multiple frequency percentages, and adjust the storage ratio corresponding to multiple cable installation abnormal event types in the event optimized storage ratio to obtain the event-adaptive storage ratio, wherein the adjustment coefficient is the ratio of the probability percentage to the frequency percentage.

[0055] Specifically, firstly, based on the predicted probability distribution of abnormal events, the probability percentage of each cable installation abnormal event type is calculated. This involves calculating the ratio of the probability of each predicted abnormal event to the sum of the probabilities of multiple predicted abnormal events, resulting in multiple probability percentages. These probability percentages reflect the relative likelihood of different abnormal event types occurring under the current construction environment, construction methods, and worker characteristics. This information guides the refined allocation of subsequent event recording strategies, such as allocating a higher storage ratio to abnormal event types with higher probability percentages, thereby achieving risk-oriented data processing and resource allocation. Secondly, based on the occurrence frequencies of multiple events within a preset historical period, the cumulative occurrence count of each abnormal event type within that historical period is statistically analyzed. The occurrence counts of each type of event are then normalized to the total occurrence count, calculating the frequency percentage of each abnormal event type.

[0056] Further, multiple adjustment coefficients are calculated based on the multiple probability proportions and multiple frequency proportions. Each adjustment coefficient is the ratio of the probability proportion to the frequency proportion of the event. If the adjustment coefficient is greater than 1, it indicates that the predicted probability of the abnormal event under the current construction conditions is higher than the historical norm, suggesting an increased risk and requiring an increase in its storage ratio in the storage strategy to ensure the integrity and traceability of critical data. If the adjustment coefficient is less than 1, it indicates that the current probability of the abnormal event is lower than the historical frequency, with relatively low risk, and its storage ratio can be appropriately reduced to save storage resources. If the adjustment coefficient is approximately equal to 1, it indicates that the current abnormal risk is consistent with historical trends, and no significant adjustment to the storage strategy is needed. Next, the adjustment coefficients for each event are used to multiply and adjust the optimized storage ratios for events previously calculated through risk assessment and resource consumption. This corrects the storage ratio corresponding to each abnormal event type. The adjusted result is the event-adapted storage ratio. To ensure that the sum of all event-adapted storage ratios is 1, the system normalizes the ratios by dividing each ratio value by the sum. This dynamic adjustment mechanism enables the storage strategy for abnormal events to be dynamically adjusted based on current construction risks. This ensures that high-risk events are given priority attention while avoiding the waste of storage resources, thereby improving the intelligence level of data management and the ability to respond to abnormal events at the construction site.

[0057] S50: Generate a dynamic data processing strategy based on the accuracy of the multiple event records and the event adaptation storage ratio, and perform maintenance processing on the cable installation anomaly data in the future time zone.

[0058] Furthermore, step S50 of the present invention further includes:

[0059] S51: If the data storage volume of a single cable installation abnormal event type meets the upper limit of the corresponding event adaptation storage ratio, then a dynamic cleanup mechanism is executed. The dynamic cleanup mechanism calculates the similarity between the stored data of each abnormal event and the stored data of other abnormal events in a single cable installation abnormal event type, sums them to obtain the overall similarity of each abnormal event, and removes the abnormal event with the highest overall similarity.

[0060] Specifically, a dynamic data processing strategy is generated based on the accuracy of the multiple event records and the event-adaptive storage ratio to guide the collection, storage, and management of cable installation anomaly data within the future time zone. This strategy dynamically adjusts the detail and storage ratio of data records according to the importance of different anomaly events and the allocation of storage resources. This allows for the focused recording and preservation of high-risk, high-priority events, while appropriately compressing or simplifying the storage of low-risk events. This optimizes storage resource utilization efficiency, improves the accuracy and response speed of anomaly data processing, and ensures that anomaly information during cable installation is scientifically and rationally maintained and managed dynamically.

[0061] Meanwhile, during data maintenance and processing, the total amount of stored data corresponding to each type of abnormal event is monitored in real time. When the data storage volume of a certain type of abnormal event reaches or exceeds its preset upper limit of the adaptive storage ratio, a dynamic cleanup mechanism is triggered. To avoid blindly deleting important data, the cleanup mechanism selectively removes data based on data similarity analysis. First, for each stored abnormal event data in the abnormal event type, its similarity with other abnormal event data in the same type is calculated. Similarity can be based on various features, such as timestamps, locations, abnormal feature description vectors, sensor reading patterns, etc., using Euclidean distance, cosine similarity, or other suitable similarity measurement methods. Next, for each abnormal event data, its similarity value with all other abnormal event data is accumulated to obtain the overall similarity score of the abnormal event, reflecting the redundancy of the event data in the overall data. Further, based on the overall similarity score, the abnormal event data with the highest overall similarity is identified, that is, the record with the highest repetition with other data and the greatest information redundancy, and this data is selected for removal first. Through the aforementioned dynamic cleanup mechanism, the amount of abnormal event data stored can be intelligently controlled, ensuring the reasonable allocation and efficient use of storage resources, while also guaranteeing the representativeness and integrity of abnormal data, effectively supporting the accurate management and tracking analysis of abnormal cable installation events.

[0062] In summary, the maintenance dynamic processing method for cable installation feature records provided by this invention has the following technical effects:

[0063] By conducting event risk assessments based on multiple cable installation anomaly event types, multiple comprehensive risk coefficients are obtained. Next, the frequency of occurrence of each of these cable installation anomaly event types within a preset historical period is obtained, and multiple event priority scores are calculated based on the comprehensive risk coefficients. Multiple event recording accuracies are also set. Then, an initial event storage ratio is set based on the proportion of events occurring within the preset historical period. This initial storage ratio is optimized using the average storage resource consumption of multiple events and the multiple event priority scores to obtain an optimized event storage ratio. Furthermore, multiple types of anomalies are predicted based on cable installation construction characteristics in future time zones, and the predicted anomaly event probability distribution is output. The optimized event storage ratio is then adjusted to obtain an event-adaptive storage ratio. Finally, a dynamic data processing strategy is generated based on the multiple event recording accuracies and the event-adaptive storage ratio to maintain and process the cable installation anomaly data in the future time zone. In other words, by introducing multi-dimensional parameters such as abnormal event risk assessment, priority scoring, historical occurrence frequency analysis, and future construction scenario prediction, a dynamically adjustable data processing mechanism is constructed. This mechanism can optimize recording accuracy and storage strategy in real time based on the risk level, probability of occurrence, and changes in the construction environment of abnormal events during cable installation. This enables the key recording of high-risk events and intelligent compression of low-value events, effectively improving the accuracy and response efficiency of abnormal data processing, while also increasing the utilization rate of storage resources and enhancing data management and adaptability in complex construction environments.

[0064] Example 2: Based on the same inventive concept as the maintenance dynamic processing method for cable installation feature records in the foregoing examples, this invention also provides a maintenance dynamic processing system for cable installation feature records. Please refer to the appendix. Figure 2The system includes: an event risk assessment module 11, used to assess the event risk based on multiple cable installation anomaly event types and obtain multiple comprehensive risk coefficients; a recording accuracy setting module 12, used to obtain the event occurrence frequency of the multiple cable installation anomaly event types within a preset historical period, calculate multiple event priority scores based on the multiple comprehensive risk coefficients, and set multiple event recording accuracies; a storage ratio optimization module 13, used to set an initial event storage ratio based on the event occurrence frequency ratio within a preset historical period, optimize the initial event storage ratio using the average event storage resource consumption and the multiple event priority scores, and obtain an optimized event storage ratio; an optimized storage ratio adjustment module 14, used to predict multiple types of anomalies based on cable installation construction characteristics in future time zones, output the predicted anomaly event probability distribution, adjust the optimized event storage ratio, and obtain an event-adaptive storage ratio; and a data maintenance processing module 15, used to generate a dynamic data processing strategy based on the multiple event recording accuracies and the event-adaptive storage ratio, and maintain and process the cable installation anomaly data in the future time zone.

[0065] Furthermore, the maintenance dynamic processing system for cable installation feature records is also used to: configure an event risk assessment strategy, wherein the event risk assessment strategy includes multiple event risk assessment indicators, and each event risk assessment indicator is identified with an indicator weight, and the event risk assessment indicators include at least event severity, abnormal duration and processing complexity; according to the event risk assessment strategy, perform event risk assessment on multiple cable installation abnormal event types respectively, and output multiple comprehensive risk coefficients.

[0066] Furthermore, the maintenance dynamic processing system for recording cable installation characteristics is also used for: statistically analyzing the occurrence frequency of the multiple cable installation abnormal event types within a preset historical time period to obtain multiple event occurrence frequencies; performing weighted calculations on the multiple event occurrence frequencies and the multiple comprehensive risk coefficients to obtain multiple event priority scores, wherein the event priority score is positively correlated with the event occurrence frequency and the comprehensive risk coefficient; and based on a priority-dimensionality reduction ratio mapping table, matching and obtaining multiple data dimensionality reduction ratios according to the multiple event priority scores, setting them as multiple event recording precisions, wherein the data dimensionality reduction ratio is negatively correlated with the event priority score.

[0067] Furthermore, the maintenance dynamic processing system for cable installation feature records is also used to: obtain the average storage resource consumption of multiple events of the multiple cable installation abnormal event types within a preset historical period; calculate multiple proportional adjustment coefficients based on the average storage resource consumption of the multiple events and the multiple event priority scores, wherein the proportional adjustment coefficients are negatively correlated with the average storage resource consumption of the events and positively correlated with the event priority scores; correct the initial storage ratio of the events based on the multiple proportional adjustment coefficients, and output the optimized storage ratio of the events.

[0068] Furthermore, the maintenance dynamic processing system for recording cable installation characteristics is also used for: acquiring cable installation construction characteristics in future time zones, wherein the cable installation construction characteristics include at least construction scene information, construction environment information, construction method, and worker characteristics; pre-training anomaly analysis plugin, wherein the anomaly analysis plugin includes N anomaly event prediction branches; evaluating environmental fluctuation based on the construction environment information and outputting an environmental fluctuation coefficient; setting the ratio of the environmental fluctuation coefficient to the average of historical environmental fluctuation coefficients as a branch adjustment coefficient, multiplying it by the initial number of branches and rounding it to obtain the number of adapted branches K, wherein K is less than or equal to N, and the initial number of branches is 3; randomly selecting K anomaly event prediction branches from the N anomaly event prediction branches, predicting K event probability distributions based on the construction scene information, construction environment information, construction method, and worker characteristics, and calculating the average to obtain the predicted anomaly event probability distribution.

[0069] Furthermore, the maintenance dynamic processing system for cable installation feature records is also used for: collecting sample cable installation construction feature sets based on historical cable installation feature records, and statistically analyzing the proportion of abnormal events corresponding to multiple cable installation abnormal event types within the historical time zone for each sample cable installation construction feature, setting this as the sample abnormal event probability, constructing a sample abnormal event probability distribution, and obtaining a sample abnormal event probability distribution set, wherein the duration of the historical time zone is the same as the duration of the future time zone; using the sample cable installation construction feature set and the sample abnormal event probability distribution set as training data, and dividing them equally into N parts to obtain N training sets, where N is an integer greater than 10; using the N training sets to train the machine learning model to convergence, obtaining N abnormal event prediction branches, and combining them to obtain an abnormal event analysis plugin.

[0070] Furthermore, the maintenance dynamic processing system for cable installation feature records is also used for: calculating the probability percentage of each cable installation abnormal event type based on the predicted abnormal event probability distribution, obtaining multiple probability percentages; calculating the frequency percentage of each cable installation abnormal event type based on the occurrence frequency of multiple events within a preset historical period, obtaining multiple frequency percentages; calculating multiple adjustment coefficients based on the multiple probability percentages and multiple frequency percentages, and adjusting the storage ratio corresponding to multiple cable installation abnormal event types in the event optimized storage ratio to obtain the event-adaptive storage ratio, wherein the adjustment coefficient is the ratio of the probability percentage to the frequency percentage.

[0071] Furthermore, the maintenance dynamic processing system for cable installation feature records is also used to: if the data storage volume of a single cable installation abnormal event type meets the upper limit of the corresponding event adaptation storage ratio, then execute a dynamic cleanup mechanism, wherein the dynamic cleanup mechanism is to calculate the similarity between the stored data of each abnormal event and the stored data of other abnormal events in a single cable installation abnormal event type, sum them to obtain the overall similarity of each abnormal event, and remove the abnormal event with the highest overall similarity.

[0072] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. The maintenance dynamic processing method and specific examples of cable installation feature records in the foregoing embodiment one are also applicable to the maintenance dynamic processing system of cable installation feature records in this embodiment. Through the foregoing detailed description of the maintenance dynamic processing method of cable installation feature records, those skilled in the art can clearly understand the maintenance dynamic processing system of cable installation feature records in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and relevant parts can be referred to in the method section.

[0073] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0074] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A maintenance dynamic processing method for cable installation characteristic records, characterized in that, The methods include: Based on multiple types of abnormal cable installation events, risk assessments were conducted for each event to obtain multiple comprehensive risk coefficients. The frequency of occurrence of the multiple cable installation abnormality event types within a preset historical period is obtained, and multiple event priority scores are calculated by combining the multiple comprehensive risk coefficients, and multiple event recording precisions are set. The initial storage ratio of events is set according to the proportion of events occurring within a preset historical period. The initial storage ratio of events is optimized by using the average storage resource consumption of multiple events and the priority scores of the multiple events to obtain the optimized storage ratio of events. Based on the characteristics of cable installation and construction in the future time zone, multiple types of abnormal events are predicted, the probability distribution of predicted abnormal events is output, and the optimized storage ratio of the events is adjusted to obtain the event-adaptive storage ratio. Based on the accuracy of the multiple event records and the event-adaptive storage ratio, a dynamic data processing strategy is generated to maintain and process the abnormal cable installation data in the future time zone.

2. The maintenance dynamic processing method for cable installation characteristic records according to claim 1, characterized in that, Based on multiple types of cable installation anomalies, risk assessments were conducted for each event, resulting in multiple comprehensive risk coefficients, including: Configure an event risk assessment strategy, wherein the event risk assessment strategy includes multiple event risk assessment indicators, and each event risk assessment indicator is identified with an indicator weight. The event risk assessment indicators include at least the event severity, the duration of the anomaly, and the processing complexity. According to the event risk assessment strategy, event risk assessments are performed on multiple cable installation abnormality event types, and multiple comprehensive risk coefficients are output.

3. The maintenance dynamic processing method for cable installation characteristic records according to claim 1, characterized in that, The frequency of occurrence of the multiple cable installation anomaly events within a preset historical time period is obtained, and multiple event priority scores are calculated by combining the multiple comprehensive risk coefficients. Multiple event recording precisions are also set, including: The occurrence frequency of the multiple cable installation abnormality event types is statistically analyzed within a preset historical time period to obtain the occurrence frequency of multiple events. The occurrence frequency of the multiple events and the multiple comprehensive risk coefficients are weighted and calculated to obtain multiple event priority scores, wherein the event priority scores are positively correlated with the occurrence frequency of events and the comprehensive risk coefficients; Based on the priority-dimensionality reduction ratio mapping table, multiple data dimensionality reduction ratios are obtained by matching the multiple event priority scores, and set as the multiple event record precisions. Among them, the data dimensionality reduction ratio and the event priority score are negatively correlated.

4. The maintenance dynamic processing method for cable installation characteristic records according to claim 1, characterized in that, Optimize the initial storage ratio of the events by using the average storage resource consumption of multiple events and the priority scores of the multiple events, and obtain the optimized storage ratio of the events, including: Obtain the average storage resource consumption of multiple events of the multiple cable installation abnormal event types within a preset historical time period; Multiple proportional adjustment coefficients are calculated based on the average event storage resource consumption and the event priority scores. The proportional adjustment coefficients are negatively correlated with the average event storage resource consumption and positively correlated with the event priority scores. The initial storage ratio of the event is corrected based on the multiple ratio adjustment coefficients, and the optimized storage ratio of the event is output.

5. The maintenance dynamic processing method for cable installation characteristic records according to claim 1, characterized in that, Based on the characteristics of cable installation and construction in the future time zone, multiple types of abnormal events are predicted, and the probability distribution of predicted abnormal events is output, including: The cable installation construction characteristics within a future time zone are obtained, wherein the cable installation construction characteristics include at least construction scene information, construction environment information, construction method and operator characteristics; A pre-trained anomaly analysis plugin, wherein the anomaly analysis plugin includes N anomaly prediction branches; Based on the construction environment information, an environmental fluctuation assessment is performed, and an environmental fluctuation coefficient is output. The ratio of the environmental fluctuation coefficient to the average historical environmental fluctuation coefficient is set as the branch adjustment coefficient. Multiplying it by the initial number of branches and rounding it off, we get the number of adapted branches K, where K is less than or equal to N and the initial number of branches is 3. K abnormal event prediction branches are randomly selected from the N abnormal event prediction branches. Based on the construction scenario information, construction environment information, construction method and worker characteristics, the probability distribution of K events is predicted. The mean is then calculated to obtain the predicted abnormal event probability distribution.

6. The maintenance dynamic processing method for cable installation characteristic records according to claim 5, characterized in that, The pre-trained anomaly analysis plugin includes: Based on historical cable installation characteristic records, a sample cable installation construction characteristic set is collected, and the proportion of abnormal events corresponding to multiple cable installation abnormal event types in the historical time zone for each sample cable installation construction characteristic is calculated and set as the sample abnormal event probability. A sample abnormal event probability distribution is constructed to obtain a sample abnormal event probability distribution set, wherein the duration of the historical time zone is the same as the duration of the future time zone. The sample cable installation construction feature set and the sample abnormal event probability distribution set are used as training data and divided into N equal parts to obtain N training sets, where N is an integer greater than 10; The machine learning model is trained to convergence using the N training sets, resulting in N abnormal event prediction branches, which are then combined to obtain an abnormal event analysis plugin.

7. The maintenance dynamic processing method for cable installation characteristic records according to claim 3, characterized in that, The event optimization storage ratio is adjusted to obtain the event-adaptive storage ratio, including: Based on the predicted abnormal event probability distribution, the probability percentage of each cable installation abnormal event type is calculated to obtain multiple probability percentages; Based on the occurrence frequency of multiple events within a preset historical time period, the frequency percentage of each type of cable installation abnormal event is calculated to obtain multiple frequency percentages. Multiple adjustment coefficients are calculated based on the multiple probability proportions and multiple frequency proportions. The storage proportions corresponding to multiple cable installation abnormal event types in the event optimization storage proportion are adjusted to obtain the event adaptation storage proportion. The adjustment coefficient is the ratio of the probability proportion to the frequency proportion.

8. The maintenance dynamic processing method for cable installation characteristic records according to claim 1, characterized in that, The abnormal cable installation data in the future time zone is maintained and processed according to the dynamic data processing strategy, including: If the data storage volume of a single cable installation anomaly event type meets the upper limit of the corresponding event adaptation storage ratio, a dynamic cleanup mechanism is executed. The dynamic cleanup mechanism calculates the similarity between the stored data of each anomaly event and the stored data of other anomaly events in a single cable installation anomaly event type, sums them to obtain the overall similarity of each anomaly event, and removes the anomaly event with the highest overall similarity.

9. A maintenance dynamic processing system for recording cable installation characteristics, characterized in that, The steps for implementing a maintenance dynamic processing method for cable installation feature records according to any one of claims 1 to 8 include: The event risk assessment module is used to conduct event risk assessments based on multiple types of abnormal cable installation events and obtain multiple comprehensive risk coefficients. The recording accuracy setting module is used to obtain the frequency of occurrence of the multiple cable installation abnormal event types within a preset historical period, calculate multiple event priority scores by combining the multiple comprehensive risk coefficients, and set multiple event recording accuracy. The storage ratio optimization module is used to set the initial storage ratio of events based on the proportion of events occurring within a preset historical time period, and to optimize the initial storage ratio of events by using the average storage resource consumption of multiple events and the priority scores of the multiple events, thereby obtaining the optimized storage ratio of events. The optimized storage ratio adjustment module is used to predict multiple abnormal events based on the cable installation and construction characteristics in the future time zone, output the probability distribution of predicted abnormal events, and adjust the optimized storage ratio of the events to obtain the event-adapted storage ratio. The data maintenance and processing module is used to generate dynamic data processing strategies based on the accuracy of the multiple event records and the event adaptation storage ratio, and to maintain and process the cable installation anomaly data in the future time zone.

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