An industrial big data-based ring main unit quality analysis method, device and medium

Abstract

The application discloses a kind of ring network cabinet quality analysis method, equipment and medium based on industrial big data, relating to quality management technical field, including, quality influence is approved state is converted into quality disposal task, quality disposal process is regarded as intervention event and is hung back to equipment quality pedigree, the quality state after disposal is collected and is compared with shadow quality track, according to quality deviation reduction and recovery acceptance situation, the closed state of the corresponding quality influence chain of main quality influence factor is approved, and ring network cabinet quality analysis state is generated.The application realizes the contrast verification of candidate quality influence factor and intervention counter-evidence, for determining main quality influence factor and verifying disposal effectiveness, improve ring network cabinet quality analysis reliability.

Description

Technical Field

[0001] This invention relates to the field of quality management technology, and in particular to a method, equipment and medium for quality analysis of ring main units based on industrial big data. Background Technology

[0002] With the development of the Industrial Internet, intelligent manufacturing, and enterprise digital management systems, ring main units continuously generate a large amount of industrial data with equipment ownership, business sequence, and lifecycle characteristics during design, production, testing, operation, and maintenance. Existing quality management technologies are gradually evolving from single inspection record management to multi-source data aggregation, equipment quality evaluation, and full lifecycle quality tracking. This typically involves using unified equipment identification to achieve cross-system data association, combined with statistical analysis, quality indicator evaluation, machine learning, and knowledge association technologies to identify the quality status of ring main units during production and operation, providing data support for enterprise quality management, anomaly tracking, and handling decisions.

[0003] Existing equipment quality analysis methods based on industrial data still have two shortcomings: First, although multi-source quality records can be aggregated according to equipment objects, quality changes between different production and operation links are usually evaluated by independent indicators, stage scores, or static correlations. It is difficult to continuously characterize the formation process of quality deviations according to the life cycle position, and it is also difficult to identify the temporal succession relationship when preceding quality factors propagate to subsequent quality deviations. Second, existing quality attribution methods usually determine influencing factors based on the degree of correlation of characteristics or historical correlations. They lack a mechanism to re-verify the quality impact relationship by using the comparison trajectory of equipment from the same source and the recovery process after treatment. This can easily lead to insufficient basis for judging the main quality influencing factors and related equipment, making it difficult to form a closed-loop quality analysis process from quality deviation identification, influencing factor verification to treatment verification. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a quality analysis method for ring main units based on industrial big data to solve the problems of difficulty in tracing the propagation relationship of quality deviations and difficulty in verifying the factors affecting quality.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] Firstly, this invention provides a method for quality analysis of ring main units based on industrial big data, comprising: collecting multi-source industrial big data of the entire life cycle of the ring main unit, performing object merging and time-series linkage, determining the target ring main unit, establishing an equipment quality spectrum according to the life cycle relationship between production and operation links, and extracting the same-source quality baseline from normal equipment to generate the basic state of the equipment quality spectrum; based on the basic state of the equipment quality spectrum, continuously deviating and verifying the change of the current quality state of the target ring main unit from the same-source quality baseline, obtaining quality deviation segments, and collecting the occurrence intervals of preceding quality factors and subsequent quality deviations to form a quality propagation delay fingerprint; tracing preceding factors and verifying propagation delays along the equipment quality spectrum to generate candidate ring main units. Quality Influence Chain: Based on candidate quality influence chains, ring main units (RN units) without corresponding candidate quality influence factors are screened from industrial big data. Their time and location are aligned according to their state before the quality deviation occurs, constructing a shadow quality trajectory. The actual quality trajectory of the target RN unit is compared with the shadow quality trajectory, and the main quality influence factors and associated equipment set are determined by combining quality propagation delay fingerprints, generating a quality influence verification status. This quality influence verification status is converted into a quality handling task, and the quality handling process is treated as an intervention event and linked back to the equipment quality spectrum. The post-handling quality status is collected and compared with the shadow quality trajectory. Based on the reduction and recovery of quality deviation, the closure status of the quality influence chain corresponding to the main quality influence factors is verified, generating the RN unit quality analysis status.

[0008] As a preferred embodiment of the ring main unit quality analysis method based on industrial big data described in this invention, the steps for generating the basic state of the equipment quality spectrum are as follows:

[0009] Multi-source industrial big data of the entire life cycle of ring main units is collected according to the data source and the time sequence of business occurrence. The object belonging of quality records is checked according to the equipment identification. Quality records belonging to the same ring main unit are linked to a unified equipment object. The chronological position of each quality record is sorted according to the corresponding occurrence time to form ring main unit quality association data.

[0010] The quality analysis object is located for the quality correlation data of the ring main unit, the target ring main unit is determined, and the connection relationship between each production and operation link is verified with the target ring main unit as the center. The quality records formed by different links are associated according to the equipment life cycle advancement direction, and the source object and connection position of the corresponding quality records are retained to form the equipment quality spectrum.

[0011] Based on the equipment quality spectrum, we select similar equipment from historical industrial big data that have comparable quality to the target ring main unit and maintain normal quality status. We collect the quality records formed by the similar equipment at the corresponding life cycle positions, determine the similar quality baseline, and link it with the equipment quality spectrum to generate the basic status of the equipment quality spectrum.

[0012] As a preferred embodiment of the ring main unit quality analysis method based on industrial big data described in this invention, the step of continuously deviating from and verifying the current quality status of the target ring main unit against a baseline of similar quality to obtain quality deviation segments is as follows:

[0013] Read the basic status of the equipment quality spectrum, and according to the life cycle position of the target ring network cabinet in the equipment quality spectrum, match the current quality status with the same quality baseline to the same quality record position, calculate the quality deviation, check the deviation direction and deviation continuation position by position according to the quality deviation, and collect the deviation records that can be continuously accepted along the life cycle time sequence in sequence to form a quality deviation acceptance sequence.

[0014] Based on the quality deviation sequence, the direction of change, continuity, and link to which the quality record belongs are verified between adjacent deviation records. Records that maintain continuous deviation and can form a relationship between previous and subsequent quality changes are merged into the same quality change segment. Records that lose the basis of continuous continuity are truncated. Each quality change segment is organized according to the quality change time sequence of the target ring network cabinet to obtain quality deviation segments.

[0015] As a preferred embodiment of the ring main unit quality analysis method based on industrial big data described in this invention, the steps of tracing preceding factors and verifying propagation delays along the equipment quality spectrum to generate candidate quality influence chains are as follows:

[0016] Based on the quality deviation segment, historical quality records with sequential relationships to the corresponding quality changes are extracted from historical industrial big data. The time sequence is matched according to the location of the preceding quality factor and the location of the subsequent quality deviation. The quality propagation delay is calculated. The time delay of the same type of repeated sequential relationship is aggregated. Based on the time delay distribution after aggregation, the propagation time sequence characteristics corresponding to different quality relationships are determined, forming a quality propagation time delay fingerprint.

[0017] Based on the quality propagation delay fingerprint, the preceding quality factors are traced step by step along the equipment quality spectrum to the life cycle position before the quality deviation segment. The actual occurrence interval between each preceding quality factor and the quality deviation segment is verified. The correlation between the object to which the preceding quality factor belongs and the subsequent quality change is combined to screen out the correlation between time sequence inconsistencies and interruptions. The remaining preceding quality factors are connected in sequence according to the quality propagation direction to generate candidate quality influence chains.

[0018] As a preferred embodiment of the ring main unit quality analysis method based on industrial big data described in this invention, the step of aligning the time and position according to the state before the quality deviation occurs to construct a shadow quality trajectory includes the following steps:

[0019] Around the candidate quality influencing factors in the candidate quality influencing chain, search for ring main units with the same quality basis as the target ring main unit in industrial big data, check the quality status of each ring main unit before the occurrence of the corresponding quality influencing factors and the equipment quality spectrum inheritance status, and collect the ring main units that do not have corresponding candidate quality influencing factors and maintain a quality comparable relationship with the target ring main unit to form a shadow reference equipment set.

[0020] Based on the set of shadow reference devices, taking the quality state of the target ring main unit before the formation of quality deviation as the alignment starting point, the quality records formed by each shadow reference ring main unit at the corresponding life cycle position are calibrated in time and position, and the continuous quality changes under the condition that there are no candidate quality influencing factors are collected along the direction of quality state advancement, so as to construct the shadow quality trajectory corresponding to the actual quality change of the target ring main unit.

[0021] As a preferred embodiment of the ring main unit quality analysis method based on industrial big data described in this invention, the steps of determining the main quality influencing factors and associated equipment set by combining quality propagation delay fingerprints to generate a quality impact verification status are as follows:

[0022] The actual quality trajectory of the target ring network cabinet and the shadow quality trajectory are compared according to the corresponding time position. The quality deviation direction, deviation continuity relationship and change connection position of the two trajectories after the appearance of candidate quality influencing factors are checked. The propagation time delay fingerprint is combined to verify the propagation time sequence between candidate quality influencing factors and subsequent quality deviations. The influence degree of candidate factors is calculated to form a candidate factor influence verification sequence.

[0023] Based on the candidate factor impact verification sequence, the impact order of each candidate quality impact factor in the candidate quality impact chain is verified, the main quality impact factor that has the main bearing effect on the quality deviation of the target ring main unit is determined, and the ring main units that have the corresponding relationship with the main quality impact factor are retrieved along the equipment quality spectrum. Combined with the quality deviation bearing situation, the associated equipment set is determined, and the quality impact verification status is generated.

[0024] As a preferred embodiment of the ring main unit quality analysis method based on industrial big data described in this invention, the steps of converting the quality impact verification status into a quality handling task and reverting the quality handling process as an intervention event back to the equipment quality spectrum are as follows:

[0025] Based on the quality impact assessment status, the object ownership of the ring main unit is confirmed, and the disposal objects and disposal order are arranged according to the quality impact relationship. Corresponding quality disposal content is configured for the target ring main unit and related equipment to form a quality disposal task.

[0026] To execute quality handling tasks, each handling operation is registered as an intervention event according to its actual location and linked to the corresponding quality impact location along the equipment quality spectrum. The quality changes of the target ring main unit after the intervention event are collected, and the quality records before and after the handling are organized according to the equipment life cycle position to form a quality response sequence after handling.

[0027] As a preferred embodiment of the ring main unit quality analysis method based on industrial big data described in this invention, the steps for generating the ring main unit quality analysis status are as follows:

[0028] Based on the post-intervention quality response sequence, the actual quality changes and shadow quality trajectories after the intervention event are aligned and verified according to their corresponding time positions. The reduction direction of the original quality deviation and the recovery and continuity relationship between each quality change are checked along the candidate quality influence chain. The consistency between the actual recovery process and the quality propagation relationship corresponding to the main quality influencing factors is verified to form a quality recovery verification status.

[0029] Based on the quality recovery verification status, the deviations before handling, reductions after handling, and recovery status of the quality impact chain corresponding to the main quality influencing factors are continuously verified to determine whether the quality impact chain has been closed. The closed verification content is then back-collected according to the equipment ownership and quality record location to generate the ring main unit quality analysis status.

[0030] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the ring main unit quality analysis method based on industrial big data as described in the first aspect of the present invention.

[0031] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the ring main unit quality analysis method based on industrial big data as described in the first aspect of the present invention.

[0032] The beneficial effects of this invention are as follows: by constructing a quality propagation delay fingerprint and tracing the spectrum, the temporal succession verification of preceding quality factors and subsequent quality deviations is realized, which is used to screen out related factors that lack propagation evidence and improve the accuracy of identifying quality influencing factors; by comparing shadow quality trajectories and restoring verification after treatment, the comparative verification and intervention counter-evidence of candidate quality influencing factors are realized, which is used to determine the main quality influencing factors and verify the effectiveness of treatment, thereby improving the reliability of ring main unit quality analysis. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart of a method for quality analysis of ring main units based on industrial big data.

[0035] Figure 2 A flowchart for constructing equipment quality spectrum and homogeneous quality baselines.

[0036] Figure 3 Flowchart for forming fingerprints to handle quality deviations and their propagation delays.

[0037] Figure 4 Flowchart for verifying candidate quality influencing factors and determining major quality influencing factors. Detailed Implementation

[0038] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0039] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0040] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0041] Reference Figures 1-4This is one embodiment of the present invention, which provides a method for quality analysis of ring main units based on industrial big data, including the following steps:

[0042] S1: Collect multi-source industrial big data of the entire life cycle of ring main units, perform object merging and time sequence connection, determine the target ring main unit, establish the equipment quality spectrum according to the life cycle relationship between production operation links, and extract the same source quality baseline from normal equipment of the same source to generate the basic status of the equipment quality spectrum.

[0043] S1.1: Collect multi-source industrial big data of the entire life cycle of ring main units according to the data source and the time sequence of business occurrence. Verify the object ownership of quality records according to equipment identification. Link quality records belonging to the same ring main unit to a unified equipment object. Organize the chronological position of each quality record according to the corresponding occurrence time to form ring main unit quality association data.

[0044] Furthermore, the multi-source industrial big data of the entire life cycle of ring main units is collected according to the source system and the time of business occurrence. While preserving the original data source relationship, the equipment identifier is used as the main index for quality record attribution verification. The ring main unit objects corresponding to quality records from different sources are checked item by item. Records with consistent equipment identifiers and clear object references are grouped into the same equipment object. Records with different identifiers are confirmed to be attributed by combining the existing equipment correspondence. After the attribution verification is completed, a unified equipment object is established for the same ring main unit. Quality records scattered in different production and operation links are linked to this equipment object, so that the originally independent quality records form a related set centered on a single ring main unit. The original business occurrence position of each record is preserved, providing a unified data organization foundation for the establishment of subsequent life cycle inheritance relationships.

[0045] For quality records attached to the same equipment object, the record positions are organized from first to last according to the actual occurrence time of the business. The occurrence time corresponding to the quality record is used as the basis for time sequence positioning. It is checked whether there are time inversions, duplicate records, and cases where they cannot be continuously assigned between adjacent records. For records whose chronological relationship can be confirmed, time sequence succession positions are established in sequence. For records with conflicting time positions, no direct succession relationship is established for the time being, and their original assignment information is retained. After completing the time sequence organization within a single ring main unit, the quality records formed by different production and operation links are collected according to the unified equipment object. The data source, object assignment, and occurrence position of each quality record are retained, so that the scattered records form a continuous and distinguishable time arrangement under the unified equipment object. At the same time, it avoids the cross-mixing of quality records between different ring main units and forms ring main unit quality association data.

[0046] S1.2: Locate the quality analysis object of the ring main unit quality correlation data, determine the target ring main unit, verify the connection relationship between each production and operation link with the target ring main unit as the center, associate the quality records formed by different links according to the equipment life cycle advancement direction, and retain the source object and connection position of the corresponding quality records to form the equipment quality spectrum.

[0047] Furthermore, the analysis targets the equipment objects and their quality records in the ring main unit quality correlation data. The completeness and lifecycle coverage of each equipment object's records are verified according to the quality analysis object screening criteria. Ring main units with continuous quality records and clearly defined object affiliations are included in the candidate analysis equipment set to determine the target ring main unit. The collected quality records are then read around the target ring main unit, and the business position of each record within the equipment's lifecycle is located according to the production operation stage to which it belongs. Records within the same stage maintain their original temporal relationship, while cross-stage records verify the object continuity between the end position of the previous stage and the start position of the next stage. This ensures that the quality records of the target ring main unit, scattered across different stages, are arranged continuously according to the actual lifecycle progression direction.

[0048] Using the lifecycle progression of the target ring main unit as the main thread, the quality records that have completed business location positioning are linked one by one. It is verified whether records in adjacent links point to the same equipment object and whether the previous record can inherit the subsequent record. For quality records with a clear basis for inheritance, a continuous link is established. For records with discontinuous inheritance positions or inconsistent object relationships, the original position is retained and the inheritance boundary is marked. The source object is determined according to the original source of each quality record, and the source object is retained along with the corresponding quality record. A quality record association structure with a clear source and inheritance position is formed between different production and operation links. The association relationships are organized according to the lifecycle position to form an equipment quality spectrum centered on the target ring main unit.

[0049] S1.3: Based on the equipment quality spectrum, select similar equipment with comparable quality to the target ring main unit and maintaining normal quality status from historical industrial big data, collect the quality records formed by the similar equipment at the corresponding life cycle position, determine the similar quality baseline, and link it with the equipment quality spectrum to generate the basic status of the equipment quality spectrum.

[0050] Furthermore, using the lifecycle position and quality record continuity of the target ring main unit in the equipment quality spectrum as the basis for source selection, we search for equipment with the same equipment category and whose quality records can be mapped to similar lifecycle positions from historical industrial big data. We verify the direction of quality change, record continuity, and spectrum continuity of each equipment at the corresponding position, and exclude equipment with obvious quality anomalies, interrupted continuity, or unclear object attribution. Equipment with stable quality status and comparable to the target ring main unit is included in the source equipment set. According to the lifecycle position in the target ring main unit equipment quality spectrum, the quality records of source equipment are matched to the position, so that the historical quality records formed by different equipment can be uniformly organized under the same lifecycle position.

[0051] Around the set of equipment of the same origin, historical quality records under normal quality status are collected according to the corresponding life cycle position. The quality changes of different equipment of the same origin at the same position are centrally organized, and the direction of change and stability between records are checked to reduce the impact of occasional fluctuations of individual equipment on the normal quality status. Based on the collected quality records, the representative normal quality level and its continuous change relationship at each life cycle position are determined. The normal quality levels formed at different positions are connected according to the life cycle advancement direction to form a common quality baseline. Then, the baseline is connected according to the corresponding position in the quality spectrum of the target ring network cabinet equipment, so that the equipment quality spectrum retains the actual quality record of the target ring network cabinet and the corresponding common normal reference relationship, generating the basic state of the equipment quality spectrum.

[0052] It should be noted that historical industrial big data refers to the collection of industrial data that enterprises have continuously accumulated and retained during the past production and operation of ring main units, which can reflect the quality changes of the equipment life cycle and its business relationships.

[0053] Quality comparability basis: refers to the correspondence between source equipment and target ring main unit in terms of equipment category, life cycle location, quality record structure and operation stage, which enables comparison of quality status.

[0054] Normal quality status: refers to the quality status of equipment when there is no continuous quality deviation or confirmed quality abnormality at the corresponding life cycle position, and the quality records maintain a stable continuity.

[0055] S2: Based on the basic state of the equipment quality spectrum, continuously deviate from and change the current quality state of the target ring network cabinet to the same quality baseline, obtain quality deviation segments, and collect the occurrence intervals of preceding quality factors and subsequent quality deviations to form a quality propagation delay fingerprint. Follow the equipment quality spectrum to trace the preceding factors and verify the propagation delay of the quality deviation segments to generate candidate quality influence chains.

[0056] S2.1: Read the basic status of the equipment quality spectrum, and according to the life cycle position of the target ring network cabinet in the equipment quality spectrum, match the current quality status with the same quality baseline to the same quality record position, calculate the quality deviation, check the deviation direction and deviation continuation position by position according to the quality deviation, and collect the deviation records that can be continuously accepted along the life cycle time sequence in sequence to form a quality deviation acceptance sequence.

[0057] Furthermore, the basic status of the equipment quality spectrum is read, and the current quality status and corresponding homogeneous quality baseline are extracted according to the life cycle progression position of the target ring main unit in the equipment quality spectrum. The quality record positions corresponding to the two are aligned item by item to ensure that the quality records participating in the comparison have consistent life cycle positions and quality attributes. The numerical quality parameters in the corresponding quality records are extracted as the current quality status value. The corresponding baseline value and baseline fluctuation scale are determined by the historical quality status of homogeneous normal equipment with the same quality attribute. Around the aligned quality records, the degree of difference between the current quality status of the target ring main unit and the homogeneous quality baseline is compared, the quality deviation is calculated, and the current quality status is identified as moving closer to the normal baseline or continuously deviating according to the direction of change of the quality deviation. At the same time, the life cycle position and occurrence order of each deviation record are retained so that the quality deviation at each position can be continuously compared under a unified spectrum relationship.

[0058] For the calculated quality deviation, the deviation direction and continuity between adjacent quality records are checked position by position along the life cycle sequence of the target ring main unit. Sequential connections are established for records that continuously deviate in the same direction and have a basis for connection between the preceding and following positions. Positions where the deviation direction reverses, the deviation degree recovers to near the same quality baseline, or the life cycle connection is interrupted are separated. According to the order of the quality deviation records in the equipment quality spectrum, the deviation records that can continuously reflect the same quality change process are sequentially collected, while retaining the connection position and the continuity of change between each deviation record. This makes the discrete quality deviation records form a continuous sequence with a clear temporal progression relationship, forming a quality deviation connection sequence.

[0059] ;

[0060] in, Indicates the target ring main unit is in the first... Quality deviation corresponding to each lifecycle quality record location Indicates the location of lifecycle quality records. Indicates the target ring main unit is in the first... The current quality status value corresponding to each lifecycle quality record location. Indicates the homogeneous quality baseline at the th The baseline value corresponding to each lifecycle quality record location. Indicates that the same source normal equipment is in the first The baseline fluctuation scale formed by each lifecycle quality record location Indicates the first Deviation of the lifecycle quality record location from the acceptance mark. Indicates the target ring main unit is in the first... The current quality status value corresponding to each lifecycle quality record location. Indicates the homogeneous quality baseline at the th The baseline value corresponding to each lifecycle quality record location. Indicates that the same source normal equipment is in the first The baseline fluctuation scale formed by each lifecycle quality record location It represents a very small positive number.

[0061] It should be noted that the quality deviation is calculated using a combination of deviation from the same-source quality baseline and correction for adjacent lifecycle locations. The difference between the current quality status of the target ring main unit and the corresponding same-source quality baseline is used to characterize the direction and degree of quality deviation at the current location. The baseline fluctuation scale is used to eliminate the impact of differences in the normal quality fluctuation degree at different lifecycle locations. Based on this, the deviation amplitude change between the previous receiving location and the current location is introduced, and the deviation change of adjacent locations is controlled by the deviation receiving mark to determine whether the deviation change of adjacent locations enters the exponential correction. When adjacent locations maintain the same deviation and the deviation amplitude continues to increase, the quality deviation of the current location is increased. When the deviation amplitude gradually converges, the corresponding correction effect is reduced. When the deviation direction changes, the correction for the current location by the previous deviation is stopped. Thus, the calculation result simultaneously characterizes the quality anomaly degree of a single location and the trend of continuous development of quality deviation along the equipment lifecycle.

[0062] The baseline fluctuation scale is obtained from the historical quality status statistics of normal equipment at the corresponding life cycle quality record position. It is used to characterize the degree of natural fluctuation that normal equipment can have at the same position, and serves as the basis for scale correction of the deviation of the current position and the previous receiving position. The deviation receiving mark is obtained by checking the deviation direction of two adjacent life cycle positions relative to their respective same source quality baselines. It is used to determine whether the deviation change of the previous position participates in the exponential receiving correction of the current position. The minimum positive number is set as a calculation stability parameter in the denominator position involving the baseline fluctuation scale to avoid the situation where the baseline fluctuation scale is zero or close to zero and cannot be calculated or is abnormally amplified. The same source quality baseline value is directly taken from the same source quality baseline of the corresponding life cycle position in the basic state of the equipment quality spectrum. The quality state of the previous position and the baseline value of the previous position are obtained from the adjacent receiving records along the equipment quality spectrum and jointly participate in the calculation of the deviation amplitude change of adjacent positions.

[0063] S2.2: Based on the quality deviation acceptance sequence, verify the direction of change, continuity relationship and the link to which the quality record belongs between adjacent deviation records. Merge records that maintain continuous deviation and can form a relationship between previous and subsequent quality changes into the same quality change segment. Cut off the segments of records that have lost the basis for continuous acceptance. Organize each quality change segment according to the quality change time sequence of the target ring network cabinet to obtain the quality deviation segment.

[0064] Furthermore, the quality deviation sequence is read, and adjacent deviation records are extracted sequentially along the life cycle progression direction of the target ring network cabinet. The direction of change of the corresponding quality deviation and its continuity are compared, and it is verified whether the adjacent records are in the production operation links that can form quality status transmission. For cases where the deviation direction is consistent and there is a continuous connection between adjacent records, the corresponding records are kept in the same quality change segment. For cases where the quality deviation is obviously reversed, the adjacent life cycle positions cannot continue to be connected, or there is a lack of connection between the links, the current quality change segment is terminated at the corresponding position, so that deviation records from different sources form a continuous segment with clear boundaries according to the actual quality change process.

[0065] Based on the deviation records for acceptance verification, records belonging to the same continuous change process are segmented and merged according to their chronological position in the equipment quality spectrum. The starting position of the continuous deviation is taken as the starting point of the quality change segment, and the ending point of the acceptance relationship is taken as the ending point of the segment. The original order of occurrence and quality change relationship of each deviation record within the segment are maintained. For adjacent segments that have been truncated, their change direction and life cycle position are sorted out to avoid different quality change processes being merged into the same segment. The resulting quality change segments are reorganized according to the actual evolution sequence of the target ring network cabinet's quality status, so that each segment can independently represent a continuous quality deviation process and obtain quality deviation segments.

[0066] It should be noted that a quality change segment refers to a continuous quality change process consisting of several quality deviation records that are consistent in direction of deviation, continuous in time and location, and have a quality succession relationship.

[0067] S2.3: Focusing on quality deviation segments, extract historical quality records from historical industrial big data that have a sequential relationship with the corresponding quality changes. Calculate the quality propagation delay by matching the location of the preceding quality factor with the location of the subsequent quality deviation. Collect the time delays of recurring similar relationships and determine the propagation time sequence characteristics of different quality relationships based on the collected time delay distribution, thus forming a quality propagation time delay fingerprint.

[0068] Furthermore, focusing on the formation position of quality deviation segments within the equipment quality spectrum, historical quality records with sequential relationships to corresponding quality changes are retrieved from historical industrial big data. These records are organized according to the order of occurrence of quality factors and the formation sequence of quality deviations. It is then verified whether the preceding quality factors and subsequent quality deviations are at a lifecycle position capable of forming a continuous quality impact relationship. For historical records that have completed the sequential verification, continuous valid records after the preceding quality factors reach the propagation start-end judgment threshold are collected into a preceding valid record set, and records of subsequent quality deviations reaching the propagation start-end judgment threshold and remaining continuous and stable are collected into a subsequent valid record set. The occurrence time position of preceding valid records is weighted according to the intensity of factor change, and the occurrence time position of subsequent valid records is weighted according to the degree of quality deviation. The effective occurrence time position of preceding quality factors and the effective formation time position of subsequent quality deviations are determined separately. The difference between the two time positions on a unified time axis is determined as the quality propagation delay. Simultaneously, the equipment affiliation and lifecycle position corresponding to the preceding and following quality relationships are retained, ensuring that each quality propagation delay corresponds to a clearly defined preceding quality factor and subsequent quality deviation.

[0069] For recurring similar quality relationships in historical industrial big data, the calculated quality propagation delays are aggregated according to the types of preceding quality factors and subsequent quality deviations. The concentration and stability of different quality propagation delays in historical records are examined. Records that deviate significantly from the main delay distribution and lack continuous continuity are distinguished, and delay information that can stably represent the propagation process of the corresponding quality relationship is retained. Typical propagation sequences and delay change characteristics are organized according to different quality relationships. The propagation sequence relationships with recurring basis between preceding quality factors and subsequent quality deviations are aggregated and solidified, so that different quality relationships form corresponding historical propagation references and form quality propagation delay fingerprints.

[0070] It should be noted that the threshold for determining the start and end of propagation (example range: 0.2 to 0.4) is specifically determined based on a comprehensive consideration of the historical quality fluctuation range of normal equipment from the same source, the magnitude of changes in preceding quality factors, and the continuous stability of subsequent quality deviations.

[0071] Preceding quality factors: These are quality influencing factors that appear before quality deviations occur and have a temporal sequence and successive relationship with subsequent quality changes in the equipment quality spectrum.

[0072] Subsequent quality deviation: refers to quality changes that occur at the corresponding lifecycle position after the occurrence of preceding quality factors and that deviate continuously from the original quality baseline.

[0073] Quality propagation delay fingerprint refers to a historical time series reference formed by aggregating the propagation delays of repeated occurrences between similar preceding quality factors and subsequent quality deviations, used to characterize the typical propagation order and time interval characteristics of corresponding quality relationships.

[0074] The formula for calculating the quality propagation delay is:

[0075] ;

[0076] in, Indicates the first The quality propagation delay corresponding to the quality relationship before and after the group. Indicates the first Quality relationship before and after grouping Indicates the first In the quality relationship before and after group 1 The time and location of occurrence of each subsequent quality deviation record. Indicates the subsequent quality deviation side. Indicates the first side of subsequent quality deviation One record, Indicates the first In the quality relationship before and after group 1 Each subsequent quality deviation record corresponds to a quality deviation degree. Indicates the first The number of subsequent quality deviation records in the quality relationship before and after the group. Indicates the first In the quality relationship before and after group 1 The occurrence time and location of each preceding quality factor record. Indicates the preceding quality factor side. Indicates the first A preliminary quality factor record. Indicates the first In the quality relationship before and after group 1 Each preceding quality factor record corresponds to the intensity of factor change, and the values ​​satisfy the following: This is used as a weighting basis for the timing of preceding quality factors. Indicates the first The number of preceding quality factors recorded in the preceding-following quality relationship of the group.

[0077] It should be noted that the quality propagation delay is calculated using the weighted time difference between the effective occurrence location of the preceding quality factor and the effective formation location of the subsequent quality deviation, rather than directly selecting a single quality record as the propagation start and end point. The preceding quality factor usually exhibits a continuous change process, and the subsequent quality deviation may also be gradually formed through multiple quality records. Directly subtracting from a single moment is easily affected by the collection interval and local fluctuations. Therefore, the occurrence time of the quality records on both sides is weighted using the intensity of factor change and the degree of quality deviation, so that the record with a greater degree of change has a higher participation in the determination of the effective time position. The subsequent quality deviation side uses the quality deviation obtained above, and the preceding quality factor side uses the corresponding intensity of factor change. The interval between the two weighted time positions is used as the quality propagation delay, so that the calculation result can reflect the actual propagation process from the change of the quality factor to the stable formation of the quality deviation.

[0078] The intensity of factor change is determined by the degree of change of the same preceding quality factor in continuous quality records. It is obtained by uniformly scaling the deviation of the preceding quality factor from the level of the record before the effective change, and is used as the weighting basis for the occurrence time position of the preceding quality factor. The more obvious the degree of change, the greater the contribution to the effective occurrence position of the preceding quality factor. The number of preceding quality factor records and the number of subsequent quality deviation records are obtained by statistically analyzing the effective records included in the corresponding propagation process after the completion of the before-and-after verification, and are used to limit the number of records covered by the weighted calculation on both sides. The occurrence time positions of the two types of records are directly taken from the unified time axis after the completion of the time sequence correspondence, and participate in the weighted calculation of the effective occurrence position of the preceding quality factor and the effective formation position of the subsequent quality deviation, respectively. Finally, the quality propagation delay is obtained by the difference between the two effective time positions.

[0079] S2.4: Based on the quality propagation delay fingerprint, trace the preceding quality factors step by step along the equipment quality spectrum to the life cycle position before the quality deviation segment. Verify the actual occurrence interval between each preceding quality factor and the quality deviation segment. Combine the spectrum connection relationship between the object to which the preceding quality factor belongs and the subsequent quality change to screen out the correlation relationship of timing inconsistencies and interruptions. Connect the remaining preceding quality factors in sequence according to the quality propagation direction to generate a candidate quality influence chain.

[0080] Furthermore, the quality propagation delay fingerprint and equipment quality spectrum are read. Taking the starting position of the quality deviation segment in the equipment quality spectrum as the tracing starting point, the quality records preceding the quality deviation segment are searched step by step along the reverse position of the life cycle advancement direction. The preceding quality factors that may participate in the quality change are screened according to the propagation sequence relationship between the preceding quality factors and the subsequent quality deviations in the quality propagation delay fingerprint. For each preceding quality factor, the corresponding occurrence position and time position are extracted. The occurrence time of the preceding quality factor is matched with the formation time of the quality deviation segment to determine the actual occurrence interval. The actual occurrence interval is then matched with the historical propagation sequence of the corresponding quality relationship in the quality propagation delay fingerprint to verify the alignment. The preceding quality factors with consistent propagation order and corresponding occurrence intervals are retained.

[0081] To verify the propagation delay of the preceding quality factors, the system checks whether there is a continuous lifecycle connection between the object to which the preceding quality factor belongs and subsequent quality changes according to the equipment quality spectrum. It verifies whether the quality record of the preceding quality factor can continuously point to the quality deviation segment along the spectrum. It filters out the correlations that are inconsistent with the order of occurrence and the direction of quality propagation, and truncates the correlations where the spectrum position is broken or the object affiliation cannot be continuously connected. The system organizes the correlations according to the order of occurrence and the direction of quality propagation of the preceding quality factors in the equipment quality spectrum. The preceding quality factors that can pass the propagation delay verification and maintain the continuity of the spectrum are connected in sequence, so that different preceding quality factors and quality deviation segments form a quality influence path with a clear propagation order, generating a candidate quality influence chain.

[0082] It should be noted that the candidate quality impact chain refers to the quality impact path formed by sequentially connecting the preceding quality factors that may lead to quality deviations according to their actual occurrence order and quality propagation direction after verification of propagation delay and screening by the equipment quality spectrum.

[0083] S3: Based on the candidate quality impact chain, screen out the same source ring main units that do not have corresponding candidate quality impact factors from industrial big data, align the time and position according to the state before the quality deviation occurs, construct the shadow quality trajectory, compare the actual quality trajectory of the target ring main unit with the shadow quality trajectory, and combine the quality propagation delay fingerprint to determine the main quality impact factors and related equipment set, and generate the quality impact verification status.

[0084] S3.1: Based on the candidate quality influencing factors in the candidate quality influence chain, search for ring main units with the same quality basis as the target ring main unit in industrial big data, verify the quality status of each ring main unit before the occurrence of the corresponding quality influencing factors and the equipment quality spectrum inheritance status, and collect the ring main units that do not have corresponding candidate quality influencing factors and maintain a quality comparable relationship with the target ring main unit to form a shadow reference equipment set.

[0085] Furthermore, focusing on the candidate quality influencing factors already retained in the candidate quality influence chain, historical equipment records with the same quality basis as the target ring main unit are retrieved from industrial big data. The comparability between different ring main units and the target ring main unit is verified according to equipment category, life cycle position, and continuity of quality records. The quality status before the emergence of candidate quality influencing factors is used as a screening reference to check whether different ring main units maintain stable quality changes at the corresponding life cycle position. At the same time, it is verified whether the equipment quality spectrum can be continuously inherited between the preceding and following stages. Ring main units whose quality status has deviated significantly or whose spectrum relationship is interrupted are excluded, and comparable equipment that can reflect the normal quality evolution process is retained.

[0086] For each of the retained comparable devices, the presence of candidate quality influencing factors in the corresponding quality record position is checked. Ring main units with the same candidate quality influencing factors are removed from the candidate reference devices. For ring main units without corresponding candidate quality influencing factors, the quality status is further checked to see if it remains similar to that of the target ring main unit before the deviation occurs, and the consistency of the corresponding life cycle position and lineage continuity is checked. Ring main units that simultaneously meet the conditions of missing candidate quality influencing factors, comparable quality status, and continuous lineage position are grouped together, and the life cycle position and quality record continuity of each ring main unit are retained. This ensures that the resulting reference devices can represent normal quality changes when candidate quality influencing factors are absent, forming a shadow reference device set.

[0087] It should be noted that candidate quality influencing factors refer to the preceding quality factors that appear before the formation of quality deviations and may affect subsequent quality changes after quality propagation delay verification and equipment quality spectrum acceptance screening.

[0088] S3.2: Based on the shadow reference device set, taking the quality state of the target ring main unit before the formation of the quality deviation as the alignment starting point, the quality records formed by each shadow reference ring main unit at the corresponding life cycle position are calibrated in time and position, and the continuous quality changes under the condition that there are no candidate quality influencing factors are collected along the quality state advancement direction, and the shadow quality trajectory corresponding to the actual quality change of the target ring main unit is constructed.

[0089] Furthermore, the shadow reference device set is read, and the quality state of the target ring main unit before the formation of the quality deviation is used as the time position alignment benchmark to determine the corresponding life cycle position of the target ring main unit in the equipment quality spectrum, and the quality records formed by each shadow reference ring main unit at the same life cycle position are extracted; according to the occurrence order of the quality records, the alignment starting point of the shadow reference ring main unit and the target ring main unit is unified to the same time position, and the continuity of the quality records before and after the alignment position is checked. For records with advanced or lagging time positions, the position is calibrated according to the life cycle correspondence, so that the quality changes of different ring main units have a consistent time sequence comparison basis, while retaining the original life cycle position and the continuity relationship of each quality record.

[0090] Around the shadow reference ring main unit that has completed time and position calibration, the quality records formed after the alignment starting point are sequentially organized along the actual progress direction of the quality status. The continuous quality records are checked for the presence of candidate quality influencing factors. Quality changes with the involvement of candidate quality influencing factors are eliminated. Records without candidate quality influencing factors and with continuous quality status are sequentially collected. The continuous quality changes of different shadow reference ring main units are collected according to a unified time and position. The quality status under the same life cycle position is organized accordingly. The effective quality status values ​​of each shadow reference ring main unit at the same position are averaged. The average value is used as the shadow quality status value of the corresponding position, forming a continuous quality evolution path under the condition that no candidate quality influencing factors have appeared. Then, the position is matched according to the time progression order of the actual quality trajectory of the target ring main unit to construct the shadow quality trajectory.

[0091] It should be noted that the shadow quality trajectory refers to the reference trajectory formed by aligning and aggregating the continuous quality states of comparable ring main units from the same source under the condition of excluding the intervention of candidate quality influencing factors. It is used to characterize the expected quality change process of the target ring main unit under normal conditions.

[0092] S3.3: Compare the actual quality trajectory and shadow quality trajectory of the target ring network cabinet according to the corresponding time position, check the quality deviation direction, deviation continuity relationship and change connection position of the two trajectories after the appearance of candidate quality influencing factors, and combine the quality propagation delay fingerprint to verify the propagation time sequence between candidate quality influencing factors and subsequent quality deviations, calculate the influence degree of candidate factors, and form a candidate factor influence verification sequence.

[0093] Furthermore, the actual quality trajectory and shadow quality trajectory of the target ring main unit are aligned segment by segment according to the corresponding time position. Taking the location of the candidate quality influencing factor as the comparison starting point, the changes of the two trajectories at the same life cycle position are checked sequentially along the quality status advancement direction. The deviation direction of the actual quality status of the target ring main unit relative to the shadow quality status is compared to see if they are consistent. It is verified whether the deviation continues to expand, remains stable, or gradually returns after it is formed, and whether there are continuous connection positions between adjacent quality changes is checked. Records where the two trajectories cannot correspond in time position or where the quality change relationship is interrupted are separated. The trajectory segments that can maintain the basis for continuous comparison are sorted in sequence so that the actual quality change after the appearance of the candidate quality influencing factor and the quality change when there is no corresponding candidate quality influencing factor can form a continuously verifiable comparison relationship.

[0094] Based on the quality change records of the completed trajectory connection comparison, the propagation time sequence characteristics corresponding to the candidate quality influencing factors are read from the quality propagation delay fingerprint. The occurrence interval between the actual occurrence position of the candidate quality influencing factor and the subsequent quality deviation formation position is time sequence verified to check whether the actual propagation process falls into the stable time sequence relationship characterized by historical propagation patterns. Combining the degree of deviation, the duration of deviation and the degree of conformity of propagation time sequence between the actual quality trajectory and the shadow quality trajectory, the influence degree of the candidate factor is calculated. The corresponding influence degree and verification position are organized according to the occurrence order of the candidate quality influencing factors in the candidate quality influence chain, so that each candidate quality influencing factor has corresponding trajectory difference information and propagation time sequence verification information, forming a candidate factor influence verification sequence.

[0095] The formula for calculating the influence of candidate factors is:

[0096] ;

[0097] in, Indicates the first The influence degree of each candidate quality influencing factor. Indicates the first The actual quality trajectory of the target ring main unit corresponding to each candidate quality influencing factor is in the [number]th [period]. Quality status values ​​at each lifecycle quality record location. This indicates the sequence number of the candidate quality influencing factors. Indicates the first The shadow quality trajectory corresponding to each candidate quality influencing factor is in the _th Quality status values ​​at each lifecycle quality record location. Indicates the first The number of valid lifecycle quality record locations corresponding to each candidate quality influencing factor. Indicates the first The actual propagation interval from each candidate quality influencing factor to subsequent quality deviations. Indicates the first Typical quality propagation delay for each candidate quality influencing factor.

[0098] It should be noted that the influence of candidate factors is calculated using the average deviation between the actual quality trajectory and the shadow quality trajectory, as well as the degree of conformity between the quality propagation timeline and the actual quality trajectory. The difference between the actual quality trajectory and the shadow quality trajectory at the same lifecycle quality record position is used to characterize the magnitude of quality change after the emergence of candidate quality influencing factors. The shadow quality state value is used for scale correction to reduce the impact of differences in quality state levels at different lifecycle positions on trajectory comparison. Based on trajectory deviation, the difference between the actual propagation interval and the typical quality propagation delay is introduced, and a negative first power form is used to form the propagation timeline correction. The closer the actual propagation interval is to the typical propagation delay characterized by the quality propagation delay fingerprint, the stronger the propagation timeline correction effect. Candidate quality influencing factors with similar deviation but whose propagation timeline does not conform to historical propagation patterns are suppressed, so that the influence of candidate factors can simultaneously reflect the degree of trajectory difference and the conformity of quality propagation timeline.

[0099] The actual quality status value is extracted from the lifecycle quality record position corresponding to the actual quality trajectory of the target ring network cabinet. The shadow quality status value is extracted from the position corresponding to the shadow quality trajectory after the completion time and position are aligned. Both are used to calculate the trajectory deviation. The number of effective lifecycle quality record positions is obtained by statistically analyzing the positions where the two trajectories can be continuously compared. This is used to average the trajectory differences at each position. The actual propagation interval is determined by the candidate quality influencing factors and the time and position corresponding to the quality deviation. The typical quality propagation delay is extracted from the historical propagation delay collection results corresponding to the quality relationship before and after in the quality propagation delay fingerprint. Both are used to correct the propagation timing. The minimum positive number follows the settings in the above calculation to avoid the situation where the shadow quality status value or the typical quality propagation delay is zero and cannot be calculated.

[0100] S3.4: Based on the candidate factor impact verification sequence, the impact order of each candidate quality impact factor in the candidate quality impact chain is verified, the main quality impact factor that has the main bearing effect on the quality deviation of the target ring main unit is determined, and the ring main units that have the corresponding relationship with the main quality impact factor are retrieved along the equipment quality spectrum. Combined with the quality deviation bearing situation, the associated equipment set is determined, and the quality impact verification status is generated.

[0101] Furthermore, the influence verification sequence of candidate factors is read, and the influence degree, trajectory deviation, and propagation time sequence verification of each candidate quality influence factor are extracted according to the order of occurrence in the candidate quality influence chain. The degree of acceptance of the formation process of the quality deviation of the target ring network cabinet by different candidate quality influence factors is compared. The continuity relationship between the preceding quality factors and the subsequent quality changes is checked step by step along the candidate quality influence chain. The influence order of candidate quality influence factors that are lower than the influence degree screening threshold, whose propagation time sequence does not conform to the quality propagation delay fingerprint, or that cannot be continuously accepted to the quality deviation segment is reduced. The influence order of candidate quality influence factors that can continuously accept the subsequent quality deviation and form a significant difference between the actual quality trajectory and the shadow quality trajectory is increased. The main quality influence factors that have the main acceptance effect on the quality deviation of the target ring network cabinet are determined according to the verified influence order.

[0102] Based on the location and association of the main quality influencing factors within the equipment quality spectrum, ring main units (RNBs) with common associations to the main quality influencing factors are retrieved along the corresponding spectrum. Equipment objects whose associations with the main quality influencing factors can continue to be linked along the production and operation stages are identified. For each retrieved RNB, the deviation direction, location of deviation, and preceding and following connections in the quality records are checked. Equipment with similar quality deviations and continuous connections to the main quality influencing factors is retained. Equipment with only object associations but no corresponding quality deviation connection is distinguished. Target RNBs and associated RNBs are organized according to equipment affiliation and quality impact connection, determining the associated equipment set. The main quality influencing factors, their order of influence, and the associated equipment set are then aggregated accordingly to generate a quality impact verification status.

[0103] It should be noted that the impact screening threshold (the example range can be set to 0.60 to 0.80) is calibrated based on the distribution of the impact of candidate factors in historically confirmed quality anomaly samples, the impact level of normal equipment, and the requirements for controlling the false positive rate.

[0104] The associated equipment set refers to a set of ring main unit equipment that has a quality spectrum relationship with the main quality influencing factors and forms similar quality deviations and continuous inheritance relationships at the corresponding life cycle positions.

[0105] S4: Convert the quality impact verification status into a quality handling task, and link the quality handling process back to the equipment quality spectrum as an intervention event. Collect the quality status after handling and compare it with the shadow quality trajectory. Based on the reduction and recovery of quality deviation, verify the closed state of the quality impact chain corresponding to the main quality impact factors, and generate the ring main unit quality analysis status.

[0106] S4.1: Based on the quality impact assessment status, confirm the object ownership of the ring main unit, arrange the disposal objects and disposal order according to the quality impact relationship, configure the corresponding quality disposal content for the target ring main unit and related equipment, and form a quality disposal task.

[0107] Furthermore, the quality impact verification status is read, and the target ring network cabinet and related equipment are identified as disposal targets according to the equipment affiliation and quality impact bearing position corresponding to the main quality impact factors. It is verified whether each device is in the spectrum position where the main quality impact factors can continuously exert their effects. Devices that only have object association but lack the basis for bearing quality deviations are not included in the current disposal targets. The quality impact relationship between disposal targets is sorted out along the direction of quality deviation propagation from the main quality impact factors. The disposal priority relationship is determined according to the order of formation of quality deviations and the order of impact bearing, so that the disposal order is consistent with the quality propagation direction already verified in the candidate quality impact chain, forming disposal arrangement content with clear object affiliation and execution order.

[0108] Based on the completed categorization of the disposal targets, the quality links requiring intervention are determined according to the corresponding positions of the main quality influencing factors in the equipment quality spectrum. The corresponding quality disposal operations are determined based on the quality attributes, deviation directions, and quality impact locations of the main quality influencing factors. The corresponding quality disposal content is configured in conjunction with the quality deviation acceptance status of the target ring network cabinet and related equipment, ensuring that each disposal content corresponds to a clearly defined equipment object and quality impact location. The execution order of each quality disposal content is organized according to the aforementioned disposal priority relationship. Disposal content with continuous quality impact relationships is maintained sequentially, while disposal content without direct relationship is assigned to the corresponding equipment object. The disposal targets, disposal order, quality impact locations, and quality disposal content are uniformly collected to form a quality disposal task.

[0109] S4.2: Execute quality handling tasks, register each handling operation as an intervention event according to its actual location, and link it to the corresponding quality impact location along the equipment quality spectrum. Continue to collect the quality changes of the target ring network cabinet after the intervention event occurs, and organize the quality records before and after the handling according to the equipment life cycle position to form a quality response sequence after handling.

[0110] Furthermore, in accordance with the identified objects, order, and locations of quality impact in the quality handling tasks, corresponding quality handling content is executed. When each handling operation actually occurs, the corresponding equipment object, location, and execution order are recorded, and the handling operations are uniformly registered as intervention events. The location of the intervention event is determined according to the location of the main quality influencing factors in the equipment quality spectrum, and a temporal sequence relationship is established between the intervention event and the corresponding quality record. This allows the handling behavior to be embedded in the original quality change process, while maintaining a continuous correspondence between the quality deviation before handling, the main quality influencing factors, and the intervention event. Multiple consecutively executed intervention events are organized according to their actual occurrence order to form a clear sequence of handling events.

[0111] Around the intervention events that have been completed, continue to collect the quality records of the target ring main unit after the intervention events occur. Use the location of the intervention event as the dividing point between the quality status before and after the intervention, and organize the quality changes after the intervention along the direction of the equipment life cycle. The quality deviation records formed before the intervention and the newly added quality records after the intervention are connected and verified according to the corresponding life cycle positions. Check the continuity of adjacent quality records in terms of time location and equipment affiliation. Establish a connection relationship for quality records that can continuously reflect the impact of the intervention process. Separate records with time interruptions or inconsistent object affiliation. Collect continuous quality records according to the order of the intervention events and the order of quality status changes to form a quality response sequence after the intervention.

[0112] S4.3: Based on the post-intervention quality response sequence, the actual quality changes and shadow quality trajectories after the intervention event are aligned and verified according to their corresponding time positions. The reduction direction of the original quality deviation and the recovery and continuity relationship between each quality change are checked along the candidate quality influence chain. The consistency between the actual recovery process and the quality propagation relationship corresponding to the main quality influencing factors is verified to form a quality recovery verification status.

[0113] Furthermore, the post-treatment quality response sequence is read, and the location of the intervention event is used as the starting point for verifying the actual quality change. Continuous quality records after the intervention event are extracted along the equipment lifecycle direction, and aligned segment by segment with the shadow quality trajectory according to the corresponding time position. During the alignment process, it is verified whether the actual quality change and the shadow quality change are at the same lifecycle position. The direction and duration of the original quality deviation after the intervention event are compared to identify the process of the deviation gradually weakening, remaining unchanged, or continuing to expand. At the same time, it is checked whether adjacent quality records maintain continuity. Records where the time position cannot correspond or the quality change is interrupted are separated, so that the actual quality change after treatment and the shadow quality change in the absence of candidate quality influencing factors form a continuous comparison relationship.

[0114] Read the quality propagation relationship between the main quality influencing factors and the original quality deviation along the candidate quality influence chain. Check the recovery sequence of each quality change position after the intervention event according to the propagation order in the quality influence chain. Verify whether the subsequent quality state recovery can be formed at the corresponding propagation position after the reduction of the preceding quality deviation. Compare the consistency between the actual recovery process and the quality propagation relationship corresponding to the main quality influencing factors in terms of occurrence order and direction of inheritance. Establish the recovery inheritance relationship for records where the recovery order can be continuously corresponded and the quality deviation is reduced step by step along the original propagation direction. For records with conflicting recovery order or interrupted inheritance, retain the corresponding verification position. Collect the original quality deviation reduction status, recovery inheritance relationship and propagation consistency verification content to form the quality recovery verification status.

[0115] It should be noted that the reduction of quality deviation refers to the situation where, after the quality treatment is completed, the original quality deviation of the target ring main unit is reduced or restored in terms of the degree of deviation and the duration of the deviation compared to before the treatment.

[0116] Recovery status: refers to the state in which each quality change location forms a continuous recovery relationship according to the propagation order corresponding to the quality impact chain after quality treatment.

[0117] S4.4: Based on the quality recovery verification status, continuously verify the pre-treatment deviation, post-treatment reduction, and recovery status of the quality impact chain corresponding to the main quality influencing factors, determine whether the quality impact chain has been closed, and collect the closed verification content according to the equipment ownership and quality record location to generate the ring main unit quality analysis status.

[0118] Furthermore, the quality recovery verification status is read, and according to the corresponding position of the main quality influencing factors in the quality impact chain, the quality deviation formed before the intervention, the deviation reduction status after the intervention event, and the quality change recovery acceptance record are extracted in sequence. The quality changes before and after the intervention are continuously checked along the propagation direction of the quality impact chain. It is checked whether the original quality deviation has continued to weaken after the intervention event, whether the quality changes before and after the intervention have recovered according to the corresponding propagation order, and whether the effect of the intervention can be continuously transmitted from the position of the main quality influencing factor to the subsequent quality deviation position in combination with the acceptance position recorded in the quality recovery verification status, so that the quality formation process in the quality impact chain and the quality recovery process after the intervention form a before-and-after correspondence.

[0119] To ensure the completion of the continuously verified quality impact chain, check whether the original quality deviations corresponding to the main quality impact factors have been reduced and whether the continuity between each recovery point is maintained. Quality impact chains that can form a complete correspondence between the quality propagation process before treatment and the quality recovery process after treatment are identified as closed. Quality impact chains that still have quality deviations or whose recovery continuity is interrupted are marked as unclosed. According to the ownership of the ring main unit equipment and the life cycle position of the quality record, the closure status of the quality impact chain, the corresponding main quality impact factors, and the quality recovery verification content are linked back to the corresponding quality record. The closure verification content is kept consistent with the original quality change relationship in the equipment quality spectrum, and the data is collected to form the ring main unit quality analysis status.

[0120] This embodiment also provides a computer device applicable to the ring main unit quality analysis method based on industrial big data, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the ring main unit quality analysis method based on industrial big data proposed in the above embodiment.

[0121] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0122] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the method for quality analysis of ring main units based on industrial big data as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0123] In summary, this invention achieves temporal continuity verification between preceding quality factors and subsequent quality deviations through: constructing a quality propagation delay fingerprint and tracing its lineage, thereby filtering out related factors lacking propagation evidence and improving the accuracy of identifying quality influencing factors; and through shadow quality trajectory comparison and post-treatment recovery verification, it achieves comparative verification and intervention counter-evidence of candidate quality influencing factors, thereby identifying major quality influencing factors and verifying the effectiveness of treatment, thus improving the reliability of ring main unit quality analysis.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for quality analysis of ring main units based on industrial big data, characterized in that, include: Collect multi-source industrial big data of the entire life cycle of ring main units, merge objects and attach them in time sequence, determine the target ring main units, establish equipment quality spectrum according to the life cycle relationship between production and operation links, and extract the same quality baseline from normal equipment of the same source to generate the basic status of equipment quality spectrum; Based on the basic state of the equipment quality spectrum, the current quality state of the target ring network cabinet is continuously deviated from and the change is verified against the same quality baseline. Quality deviation segments are obtained, and the occurrence intervals of preceding quality factors and subsequent quality deviations are collected to form a quality propagation delay fingerprint. The preceding factors are traced and the propagation delay is checked along the equipment quality spectrum to generate candidate quality influence chains. Based on the candidate quality impact chain, the same source ring main units that do not have corresponding candidate quality impact factors are screened from industrial big data. The time and position are aligned according to the state before the quality deviation occurs to construct the shadow quality trajectory. The actual quality trajectory of the target ring main unit is compared with the shadow quality trajectory. The main quality impact factors and associated equipment set are determined by combining the quality propagation delay fingerprint, and the quality impact verification status is generated. The quality impact assessment status is converted into a quality handling task. The quality handling process is treated as an intervention event and linked back to the equipment quality spectrum. The quality status after handling is collected and compared with the shadow quality trajectory. Based on the reduction and recovery of quality deviation, the closed state of the quality impact chain corresponding to the main quality impact factors is determined, and the quality analysis status of the ring main unit is generated.

2. The method for quality analysis of ring main units based on industrial big data as described in claim 1, characterized in that, The steps for generating the basic state of the device's mass spectrum are as follows: Multi-source industrial big data of the entire life cycle of ring main units is collected according to the data source and the time sequence of business occurrence. The object belonging of quality records is checked according to the equipment identification. Quality records belonging to the same ring main unit are linked to a unified equipment object. The chronological position of each quality record is sorted according to the corresponding occurrence time to form ring main unit quality association data. The quality analysis object is located for the quality correlation data of the ring main unit, the target ring main unit is determined, and the connection relationship between each production and operation link is verified with the target ring main unit as the center. The quality records formed by different links are associated according to the equipment life cycle advancement direction, and the source object and connection position of the corresponding quality records are retained to form the equipment quality spectrum. Based on the equipment quality spectrum, we select similar equipment from historical industrial big data that have comparable quality to the target ring main unit and maintain normal quality status. We collect the quality records formed by the similar equipment at the corresponding life cycle positions, determine the similar quality baseline, and link it with the equipment quality spectrum to generate the basic status of the equipment quality spectrum.

3. The method for quality analysis of ring main units based on industrial big data as described in claim 2, characterized in that, The steps for continuously deviating from and verifying the current quality status of the target ring main unit against the baseline quality to obtain quality deviation segments are as follows: Read the basic status of the equipment quality spectrum, and according to the life cycle position of the target ring network cabinet in the equipment quality spectrum, match the current quality status with the same quality baseline to the same quality record position, calculate the quality deviation, check the deviation direction and deviation continuation position by position according to the quality deviation, and collect the deviation records that can be continuously accepted along the life cycle time sequence in sequence to form a quality deviation acceptance sequence. Based on the quality deviation sequence, the direction of change, continuity, and link to which the quality record belongs are verified between adjacent deviation records. Records that maintain continuous deviation and can form a relationship between previous and subsequent quality changes are merged into the same quality change segment. Records that lose the basis of continuous continuity are truncated. Each quality change segment is organized according to the quality change time sequence of the target ring network cabinet to obtain quality deviation segments.

4. The method for quality analysis of ring main units based on industrial big data as described in claim 1, characterized in that, The steps for tracing preceding factors and verifying propagation delays along the equipment quality spectrum to generate candidate quality influence chains are as follows: Based on the quality deviation segment, historical quality records with sequential relationships to the corresponding quality changes are extracted from historical industrial big data. The time sequence is matched according to the location of the preceding quality factor and the location of the subsequent quality deviation. The quality propagation delay is calculated. The time delay of the same type of repeated sequential relationship is aggregated. Based on the time delay distribution after aggregation, the propagation time sequence characteristics corresponding to different quality relationships are determined, forming a quality propagation time delay fingerprint. Based on the quality propagation delay fingerprint, the preceding quality factors are traced step by step along the equipment quality spectrum to the life cycle position before the quality deviation segment. The actual occurrence interval between each preceding quality factor and the quality deviation segment is verified. The correlation between the object to which the preceding quality factor belongs and the subsequent quality change is combined to screen out the correlation between time sequence inconsistencies and interruptions. The remaining preceding quality factors are connected in sequence according to the quality propagation direction to generate candidate quality influence chains.

5. The method for quality analysis of ring main units based on industrial big data as described in claim 4, characterized in that, The steps for aligning the time position according to the state before the quality deviation occurred and constructing the shadow quality trajectory are as follows: Around the candidate quality influencing factors in the candidate quality influencing chain, search for ring main units with the same quality basis as the target ring main unit in industrial big data, check the quality status of each ring main unit before the occurrence of the corresponding quality influencing factors and the equipment quality spectrum inheritance status, and collect the ring main units that do not have corresponding candidate quality influencing factors and maintain a quality comparable relationship with the target ring main unit to form a shadow reference equipment set. Based on the set of shadow reference devices, taking the quality state of the target ring main unit before the formation of quality deviation as the alignment starting point, the quality records formed by each shadow reference ring main unit at the corresponding life cycle position are calibrated in time and position, and the continuous quality changes under the condition that there are no candidate quality influencing factors are collected along the direction of quality state advancement, so as to construct the shadow quality trajectory corresponding to the actual quality change of the target ring main unit.

6. The method for quality analysis of ring main units based on industrial big data as described in claim 5, characterized in that, The steps for determining the main quality influencing factors and associated device set by combining the quality propagation delay fingerprint and generating the quality impact verification status are as follows: The actual quality trajectory of the target ring network cabinet and the shadow quality trajectory are compared according to the corresponding time position. The quality deviation direction, deviation continuity relationship and change connection position of the two trajectories after the appearance of candidate quality influencing factors are checked. The propagation time delay fingerprint is combined to verify the propagation time sequence between candidate quality influencing factors and subsequent quality deviations. The influence degree of candidate factors is calculated to form a candidate factor influence verification sequence. Based on the candidate factor impact verification sequence, the impact order of each candidate quality impact factor in the candidate quality impact chain is verified, the main quality impact factor that has the main bearing effect on the quality deviation of the target ring main unit is determined, and the ring main units that have the corresponding relationship with the main quality impact factor are retrieved along the equipment quality spectrum. Combined with the quality deviation bearing situation, the associated equipment set is determined, and the quality impact verification status is generated.

7. The method for quality analysis of ring main units based on industrial big data as described in claim 1 or 6, characterized in that, The steps for converting the quality impact assessment status into a quality handling task and then linking the quality handling process back to the equipment quality spectrum as an intervention event are as follows: Based on the quality impact assessment status, the object ownership of the ring main unit is confirmed, and the disposal objects and disposal order are arranged according to the quality impact relationship. Corresponding quality disposal content is configured for the target ring main unit and related equipment to form a quality disposal task. To execute quality handling tasks, each handling operation is registered as an intervention event according to its actual location and linked to the corresponding quality impact location along the equipment quality spectrum. The quality changes of the target ring main unit after the intervention event are collected, and the quality records before and after the handling are organized according to the equipment life cycle position to form a quality response sequence after handling.

8. The method for quality analysis of ring main units based on industrial big data as described in claim 7, characterized in that, The steps for generating the quality analysis status of the ring main unit are as follows: Based on the post-intervention quality response sequence, the actual quality changes and shadow quality trajectories after the intervention event are aligned and verified according to their corresponding time positions. The reduction direction of the original quality deviation and the recovery and continuity relationship between each quality change are checked along the candidate quality influence chain. The consistency between the actual recovery process and the quality propagation relationship corresponding to the main quality influencing factors is verified to form a quality recovery verification status. Based on the quality recovery verification status, the deviations before handling, reductions after handling, and recovery status of the quality impact chain corresponding to the main quality influencing factors are continuously verified to determine whether the quality impact chain has been closed. The closed verification content is then back-collected according to the equipment ownership and quality record location to generate the ring main unit quality analysis status.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the ring main unit quality analysis method based on industrial big data as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the ring main unit quality analysis method based on industrial big data as described in any one of claims 1 to 8.

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