Building engineering quality supervision evaluation method based on big data

Abstract

The application discloses a building engineering quality supervision evaluation method based on big data, relates to the technical field of building engineering quality detection, and forms a quality deviation correlation set Rel, identifies the transmission relationship between different quality deviations in a construction process, and makes the quality evaluation no longer limited to a certain process or a certain time point; generates a quality deviation propagation chain set Chn, structurally expresses the propagation path of the quality deviation between different processes and sub-projects, so that the originally hidden and lagging quality risk can be identified at an early stage of propagation; the quality deviation propagation chain set Chn is quantitatively analyzed, the propagation risk evaluation result Res is generated, the quality supervision department can distinguish the risk degree of different quality deviation propagation chains in a numerical and comparable manner, and the quality supervision evaluation result Out is output based on the propagation risk evaluation result Res, so that clear decision basis is provided for actual engineering supervision.

Description

Technical Field

[0001] This invention relates to the field of building engineering quality testing technology, specifically a building engineering quality supervision and evaluation method based on big data. Background Technology

[0002] As construction projects expand in scale and become increasingly complex, quality supervision has evolved from traditional on-site sampling to a comprehensive supervision and evaluation approach based on information technology and data. During construction, a large amount of process data, including construction sequence, process execution, quality inspection and rectification records, accumulates continuously, making construction quality a crucial area of ​​development in current quality supervision. Systematic analysis of multi-source project data not only reflects the overall quality status of the project but also provides more forward-looking technical support for the identification and control of quality risks, thereby meeting the higher requirements of modern construction projects for quality safety and refined supervision.

[0003] However, in existing big data-based construction project quality supervision and assessment practices, the analysis of quality problems still focuses on single quality defects or individual inspection results, paying more attention to whether a certain process or sub-project exhibits substandard quality at a specific point in time. Even though the system has accumulated a large amount of construction sequence data, quality defect type data, and rectification delay data, this data is often stored in a scattered manner and used independently, lacking a cross-process and cross-stage correlation analysis mechanism. This makes it difficult for quality assessment results to reflect the inherent connections between different quality deviations. In this situation, a construction quality deviation that occurs in a preceding process may continue to amplify in subsequent processes or adjacent sub-projects, but existing supervision and assessment methods struggle to identify this potential propagation relationship in a timely manner, thus failing to provide effective early warnings of hidden quality risks. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a big data-based method for supervising and evaluating the quality of construction projects, thus solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a construction engineering quality supervision and evaluation method based on big data, comprising the following steps:

[0006] S1. Collect multi-source quality-related data during the construction process of building engineering, and construct a quality behavior sequence set Beh that reflects the order in which quality behaviors occur;

[0007] S2. Perform correlation analysis on the quality behavior sequence set Beh, extract the correlation relationship between different quality deviations, and construct a quality deviation correlation set Rel to describe the quality deviation transmission relationship.

[0008] S3. Based on the quality deviation association set Rel, generate the propagation path of quality deviation between different processes and sub-projects according to the construction sequence constraints, and form a quality deviation propagation chain set Chn.

[0009] S4. Perform a propagation risk assessment on the quality deviation propagation chain set Chn, and generate a propagation risk assessment result Res to represent potential quality risks;

[0010] S5. Based on the aforementioned risk assessment result Res, output the construction project quality supervision and assessment result, forming the quality supervision and assessment result Out.

[0011] Preferably, S1 includes S11;

[0012] S11. Obtain multi-source quality-related data during the construction process of building engineering through the data interfaces of existing engineering management information systems, quality inspection record systems, and rectification closed-loop management systems;

[0013] The multi-source quality-related data includes construction sequence data Seq, quality defect record data Def, and rectification timeliness data Tim.

[0014] Specifically: Extract construction sequence data Seq based on the process flow records of the construction plan and daily construction reports, so that the construction sequence data Seq includes process identifier Pid, ​​sub-item identifier Sid, and sequence time Tsa;

[0015] Extract quality defect record data Def from the quality inspection form and the supervision spot check record, so that the quality defect record data Def includes defect identifier Did, defect type Typ, discovery time Tfd and project object identifier Oid;

[0016] The rectification time field is extracted from the rectification notice and rectification acceptance record, and the rectification timeliness data Tim is calculated based on the rectification completion time and the defect discovery time. The rectification timeliness data Tim is calculated by the rectification completion time (including Tdn) and the discovery time (Tfd), and the rectification timeliness data Tim is expressed as the rectification completion time (including Tdn) minus the discovery time (Tfd).

[0017] The construction sequence data Seq, the quality defect record data Def, and the rectification timeliness data Tim are processed uniformly.

[0018] The unified processing includes: processing missing fields with completion rules, deduplicating duplicate records, standardizing field formats, and performing consistency correction on abnormal timestamps;

[0019] After unified processing, the objects are associated at the project object identifier Oid and the process identifier Pid, ​​and aligned in time dimension according to the sequential time Tsa to form the quality process dataset Prc.

[0020] Preferably, S1 further includes S12;

[0021] S12. Based on the quality process dataset Prc, the quality events under the same engineering object are aggregated using the engineering object identifier Oid as the aggregation primary key, and the aggregated quality events are arranged in order using the sequential time Tsa as the sorting basis.

[0022] During the sequential arrangement process, each quality event is mapped to a quality behavior item, and each quality behavior item is assigned a behavior identifier Aid, a process identifier Pid, ​​a defect type Typ, and a rectification timeliness data Tim, forming a quality behavior sequence arranged continuously in time.

[0023] The quality behavior sequences formed under the same project object identifier Oid are aggregated to generate a quality behavior sequence set Beh that reflects the order in which quality behaviors occur.

[0024] Preferably, S2 includes S21;

[0025] S21. Based on the set of quality behavior sequences Beh, take each quality behavior sequence as an analysis unit, and perform co-occurrence statistical analysis on the quality behaviors that are adjacent in the construction sequence or satisfy the sequential association condition Ord in the same quality behavior sequence.

[0026] In the co-occurrence statistical analysis process, when different defect types Typ meet the preset order association condition Ord in the same quality behavior sequence, it is determined that there is a potential association between the corresponding quality deviations, and the co-occurrence association strength between the quality deviations is calculated based on the number of times the quality deviations co-occur in the quality behavior sequence and the order interval.

[0027] The quality deviations that have co-occurrence associations and their corresponding co-occurrence association strengths are summarized to form the quality deviation co-occurrence association set Cor.

[0028] Preferably, S2 further includes S22;

[0029] S22. Based on the co-occurrence association set Cor of the quality deviations, and combined with the order of quality behaviors in the quality behavior sequence set Beh, a directional analysis is performed on the co-occurrence association between quality deviations: when the first quality deviation occurs earlier than the second quality deviation in the quality behavior sequence, and the co-occurrence association strength between the two reaches a preset threshold, it is determined that there is a transmission relationship between the first quality deviation and the second quality deviation.

[0030] By integrating the quality deviation pairs with transmission relationships and their corresponding transmission directions and transmission intensities, a quality deviation association set Rel is constructed to describe the quality deviation transmission relationship.

[0031] Preferably, S3 includes S31;

[0032] S31. Based on the quality deviation association set Rel, extract the process occurrence sequence information corresponding to the source quality deviation and the target quality deviation for each transmission relationship, and construct the construction sequence constraint condition Ocn based on the process occurrence sequence information to determine whether the transmission relationship conforms to the construction process sequence relationship.

[0033] The construction sequence constraint condition Ocn is used to constrain the sequence of operations corresponding to the source quality deviation and the sequence of operations corresponding to the target quality deviation to satisfy the order of the construction process.

[0034] The quality deviation association set Rel is filtered based on the construction sequence constraint Ocn:

[0035] When the transitive relationship satisfies the construction sequence constraint condition Ocn, the transitive relationship is retained as a propagation edge;

[0036] When the transitive relationship does not satisfy the construction sequence constraint condition Ocn, the transitive relationship is discarded;

[0037] The propagation edges that satisfy the construction sequence constraint Ocn are aggregated to form a propagation edge set Esg for propagation path generation.

[0038] Preferably, S3 further includes S32;

[0039] S32. Based on the propagation edge set Esg, connect adjacent propagation edges sequentially according to the propagation direction of the propagation edges to form a propagation path candidate containing at least two propagation edges;

[0040] For the propagation path candidates, based on the node connection relationship between propagation edges and the corresponding process sequence relationship, a propagation chain continuity condition Ccn is constructed to determine whether the propagation path candidates have continuous propagation characteristics.

[0041] The propagation path candidates are verified based on the propagation chain continuity condition Ccn: when the propagation path candidate satisfies the propagation chain continuity condition Ccn, the propagation path candidate is determined to be a valid propagation path;

[0042] When the candidate propagation path does not satisfy the propagation chain continuity condition Ccn, the candidate propagation path is determined to be an invalid propagation path.

[0043] The identified effective propagation paths are summarized and stored in a structured manner to form a set of quality deviation propagation chains Chn, which represents the propagation trajectory of quality deviations between different processes and sub-projects.

[0044] Preferably, S4 includes S41;

[0045] S41. Based on the set of quality deviation propagation chains Chn, for each quality deviation propagation chain in the set of quality deviation propagation chains Chn formed by an effective propagation path...

[0046] Obtain the number of propagation edges that constitute the quality deviation propagation chain, the transmission strength corresponding to the propagation edge, the scope of the processes and sub-projects it crosses, and the corresponding rectification time data Tim for the quality deviation.

[0047] The chain length index Len, used to represent the propagation path length, is calculated based on the number of propagation edges.

[0048] The intensity index Trn, which represents the significance of propagation, is calculated based on the transmission intensity corresponding to the propagation edge.

[0049] Based on the different process identifiers Pid and different sub-item identifiers Sid traversed by the quality deviation propagation chain, a coverage index Cov is calculated to represent the range of propagation impact.

[0050] And based on the rectification timeliness data Tim, a lag index Lag is calculated to represent the degree of lag in the dissemination of rectification.

[0051] By combining the chain length index Len, the strength index Trn, the coverage index Cov, and the lag index Lag, a propagation risk assessment index set Met is formed to quantify the propagation risk of quality deviation propagation chains.

[0052] Preferably, S4 further includes S42;

[0053] S42. For the propagation risk assessment index set Met constructed for each quality deviation propagation chain in the quality deviation propagation chain set Chn, obtain the index values ​​of chain length index Len, intensity index Trn, coverage index Cov, and lag index Lag respectively; then perform normalization processing to eliminate indices with different dimensions and different numerical ranges and convert them into dimensionless index values.

[0054] After normalization, based on the preset index weight allocation rules, the normalized chain length index Len, strength index Trn, coverage index Cov, and lag index Lag are assigned corresponding weights, and the normalized indexes are combined and calculated by weighted summation to obtain the propagation risk score of the corresponding quality deviation propagation chain.

[0055] The propagation risk scores corresponding to each quality deviation propagation chain in the set of quality deviation propagation chains Chn are summarized and processed to obtain the propagation risk assessment result Res, which represents the potential quality risk level during the construction process of the building project.

[0056] Preferably, S5 includes S51;

[0057] S51. Based on the propagation risk assessment result Res, process the propagation risk score corresponding to each quality deviation propagation chain in the quality deviation propagation chain set Chn: sort or classify the quality deviation propagation chains according to the size of the propagation risk score, and combine the process and sub-project information involved in the quality deviation propagation chain to form the quality supervision and assessment result Out, which reflects the quality supervision and management of construction projects.

[0058] This invention provides a method for supervising and evaluating the quality of construction projects based on big data, which has the following beneficial effects:

[0059] (1) By constructing a quality behavior sequence set Beh, the scattered and discrete quality inspection records and rectification information during the construction process are transformed into a quality behavior sequence with a clear time sequence and process relationship, thus avoiding the problem of fragmented analysis of quality issues in the existing supervision methods. On this basis, by forming a quality deviation association set Rel, the transmission relationship between different quality deviations in the construction process is identified, so that the quality assessment is no longer limited to a certain process or a certain point in time, but can reflect the evolution characteristics of quality deviations in the construction process. By generating a quality deviation propagation chain set Chn, the propagation path of quality deviations between different processes and sub-projects is expressed in a structured way, so that the originally hidden and delayed quality risks can be identified in the early stage of propagation. Subsequently, by quantitatively analyzing the quality deviation propagation chain set Chn, a propagation risk assessment result Res is generated, so that the quality supervision department can distinguish the risk level of different quality deviation propagation chains in a numerical and comparable way. Finally, based on the propagation risk assessment result Res, the quality supervision assessment result Out is output, providing a clear decision-making basis for actual engineering supervision.

[0060] (2) By introducing the construction sequence constraint condition Ocn on the basis of the quality deviation association set Rel, only the quality deviation transmission relationship with sequential dependence in the construction process is retained as the propagation edge, and further forming the propagation edge set Esg, pseudo propagation relationship that does not conform to the actual construction sequence is eliminated from the source, and propagation judgments that are not meaningful to the project are avoided due to statistical correlation. On this basis, by sequentially connecting the propagation edge set Esg and verifying the propagation path candidates in combination with the propagation chain continuity condition Ccn, the final quality deviation propagation chain set Chn is made to maintain continuity and consistency at the quality deviation node level, construction sequence level and propagation span level, so as to truly reflect the actual propagation trajectory of quality deviation between different processes and sub-projects.

[0061] (3) By constructing a propagation risk assessment index set Met based on the quality deviation propagation chain set Chn, and introducing the chain length index Len, the intensity index Trn, the coverage index Cov, and the lag index Lag, the quality deviation propagation risk can be quantified simultaneously from multiple dimensions such as propagation length, propagation intensity, impact range, and rectification lag degree. On this basis, by performing normalization processing and weighted summation calculation on the propagation risk assessment index set Met, a propagation risk assessment result Res with unified dimensions and comparability is generated, thereby avoiding the situation in traditional supervision where risk level is distorted due to relying solely on experience or a single index. By performing risk ranking or risk classification on the propagation risk assessment result Res, and combining the process and sub-project information involved in the quality deviation propagation chain set Chn, a quality supervision assessment result Out that can be directly used for regulatory decision-making is formed. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the construction engineering quality supervision and evaluation method based on big data according to the present invention. Detailed Implementation

[0063] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0064] Example 1: This invention provides a method for supervising and evaluating the quality of building construction projects based on big data. Please refer to [link / reference]. Figure 1 This includes the following steps:

[0065] S1. Collect multi-source quality-related data during the construction process of building engineering, and construct a quality behavior sequence set Beh that reflects the order in which quality behaviors occur;

[0066] S2. Perform correlation analysis on the quality behavior sequence set Beh, extract the correlation relationship between different quality deviations, and construct a quality deviation correlation set Rel to describe the quality deviation transmission relationship.

[0067] S3. Based on the quality deviation association set Rel, generate the propagation path of quality deviation between different processes and sub-projects according to the construction sequence constraints, and form a quality deviation propagation chain set Chn.

[0068] S4. Perform a propagation risk assessment on the quality deviation propagation chain set Chn, and generate a propagation risk assessment result Res to represent potential quality risks;

[0069] S5. Based on the aforementioned risk assessment result Res, output the construction project quality supervision and assessment result, forming the quality supervision and assessment result Out.

[0070] In this embodiment, by constructing a quality behavior sequence set Beh, the scattered and discrete quality inspection records and rectification information during construction are transformed into a quality behavior sequence with a clear temporal order and process correlation, avoiding the problem of fragmented analysis of quality issues in existing supervision methods. Based on this, by forming a quality deviation correlation set Rel, the transmission relationship between different quality deviations in the construction process is identified, so that quality assessment is no longer limited to a certain process or a certain point in time, but can reflect the evolutionary characteristics of quality deviations during construction. By generating a quality deviation propagation chain set Chn, the propagation path of quality deviations between different processes and sub-projects is expressed in a structured way, enabling previously hidden and delayed quality risks to be identified in the early stages of propagation. Subsequently, by quantitatively analyzing the quality deviation propagation chain set Chn, a propagation risk assessment result Res is generated, allowing the quality supervision department to distinguish the risk level of different quality deviation propagation chains in a numerical and comparable manner. Finally, based on the propagation risk assessment result Res, a quality supervision assessment result Out is output, providing a clear decision-making basis for actual project supervision. For example, in actual engineering projects, when a concrete pouring quality deviation occurs repeatedly in preceding processes and the rectification period is long, this method can identify its propagation trend in subsequent structural construction in advance through the corresponding quality deviation propagation chain set Chn. The method then indicates in the quality supervision and evaluation result Out that the relevant processes need to increase the frequency of supervision or prioritize rectification, thereby preventing quality problems from being concentrated in the later stages of the project and reducing rework costs and safety risks. Therefore, this invention not only improves the foresight and pertinence of construction project quality supervision and evaluation but also transforms quality supervision activities from passive post-event handling to proactive risk prevention and control, demonstrating significant practical application value.

[0071] Example 2: Specifically: S1 includes S11;

[0072] S11. Obtain multi-source quality-related data during the construction process of building engineering through the data interfaces of existing engineering management information systems, quality inspection record systems, and rectification closed-loop management systems;

[0073] The multi-source quality-related data includes construction sequence data Seq, quality defect record data Def, and rectification timeliness data Tim.

[0074] Specifically: Extract construction sequence data Seq based on the process flow records of the construction plan and daily construction reports, so that the construction sequence data Seq includes process identifier Pid, ​​sub-item identifier Sid, and sequence time Tsa;

[0075] Extract quality defect record data Def from the quality inspection form and the supervision spot check record, so that the quality defect record data Def includes defect identifier Did, defect type Typ, discovery time Tfd and project object identifier Oid;

[0076] The rectification time field is extracted from the rectification notice and rectification acceptance record, and the rectification timeliness data Tim is calculated based on the rectification completion time and the defect discovery time. The rectification timeliness data Tim is calculated by the rectification completion time (including Tdn) and the discovery time (Tfd), and the rectification timeliness data Tim is expressed as the rectification completion time (including Tdn) minus the discovery time (Tfd).

[0077] The construction sequence data Seq, the quality defect record data Def, and the rectification timeliness data Tim are processed uniformly.

[0078] The unified processing includes: processing missing fields with completion rules, deduplicating duplicate records, standardizing field formats, and performing consistency correction on abnormal timestamps;

[0079] After completing the unified processing, the objects are associated at the object level according to the engineering object identifier Oid and the process identifier Pid, ​​and the time dimension is aligned according to the sequential time Tsa to form a quality process dataset Prc with a unified time base and process association relationship.

[0080] It should be noted that:

[0081] Construction sequence data Seq: used to depict the sequential relationship between procedures and sub-projects in the construction process, where the sequence time Tsa is used to provide a sortable time base, making the subsequent sequence construction traceable;

[0082] Quality defect record data Def: used to characterize the events in which quality deviations occur, where the defect type Typ is used to distinguish the categories of quality deviations, and the defect identifier Did is used to ensure the unique traceability of defect events at the data level;

[0083] Rectification timeliness data Tim: Used to quantify the rectification response speed. The rectification timeliness data Tim is calculated from the rectification completion time including Tdn and the discovery time Tfd. The larger the rectification timeliness data Tim, the more obvious the rectification lag is, which is helpful for drawing the "deviation retention time" in subsequent risk assessment.

[0084] Quality process dataset Prc: Used to merge construction sequence data Seq, quality defect record data Def, and rectification timeliness data Tim on the same object and the same time axis, so that when generating the quality behavior sequence set Beh, a consistent correlation benchmark can be maintained at the cross-process and cross-sub-project level.

[0085] S1 further includes S12;

[0086] S12. Based on the quality process dataset Prc, the quality events under the same engineering object are aggregated using the engineering object identifier Oid as the aggregation primary key, and the aggregated quality events are arranged in order using the sequential time Tsa as the sorting basis.

[0087] During the sequential arrangement process, each quality event is mapped to a quality behavior item, and each quality behavior item is assigned a behavior identifier Aid, a process identifier Pid, ​​a defect type Typ, and a rectification timeliness data Tim, forming a quality behavior sequence arranged continuously in time.

[0088] The quality behavior sequences formed under the same project object identifier Oid are aggregated to generate a quality behavior sequence set Beh that reflects the order in which quality behaviors occur.

[0089] It should be noted that:

[0090] Behavior identifier Aid: Used to uniquely identify quality behavior items, ensuring that quality behavior items can be stably referenced in subsequent association analysis and avoiding confusion due to similar events having the same field;

[0091] The quality behavior sequence set Beh consists of multiple quality behavior sequences. Each quality behavior sequence corresponds to an engineering object identifier Oid, and the sequential time Tsa ensures the stability of the order relationship within the sequence, so that the sequence order can be used as the propagation direction constraint when constructing the quality deviation propagation path.

[0092] In this embodiment, data from sources such as construction plans, daily construction reports, quality inspection forms, and rectification and acceptance records are collected uniformly, and construction sequence data (Seq), quality defect record data (Def), and rectification timeliness data (Tim) are constructed. This transforms quality information, originally scattered across different business systems, into standardized data with clear engineering object identifiers, process identifiers, and time sequence characteristics. Based on this, a quality process dataset (Prc) is formed, achieving precise alignment between the occurrence time of quality defects, rectification duration, and construction sequence. This avoids the situation in traditional supervision where inconsistent time standards prevent the reconstruction of the sequence of quality issues. By mapping quality events to quality behavior items and generating a quality behavior sequence set (Beh), quality behaviors under the same engineering object identifier (Oid) can form a continuous and traceable behavior sequence according to the sequential time (Tsa), thus providing a stable data benchmark for subsequent analysis. For example, in actual engineering projects, when different types of quality defects occur multiple times in adjacent processes of the same sub-project, the quality behavior sequence set (Beh) can clearly reconstruct the chronological order of the quality defects and the corresponding rectification delays, eliminating the need for manual review of multiple inspection records and rectification documents. Therefore, step S1 not only improves the completeness and consistency of construction project quality data, but also provides a reliable and reproducible data foundation for subsequent quality deviation correlation analysis, significantly enhancing the engineering operability of quality supervision and evaluation work.

[0093] Example 3: Specifically: S2 includes S21;

[0094] S21. Based on the set of quality behavior sequences Beh, take each quality behavior sequence as an analysis unit, and perform co-occurrence statistical analysis on the quality behaviors that are adjacent in the construction sequence or satisfy the sequential association condition Ord in the same quality behavior sequence.

[0095] In the co-occurrence statistical analysis process, when different defect types Typ meet the preset order association condition Ord in the same quality behavior sequence, it is determined that there is a potential association between the corresponding quality deviations, and the co-occurrence association strength between the quality deviations is calculated based on the number of times the quality deviations co-occur in the quality behavior sequence and the order interval.

[0096] The quality deviations that have co-occurrence associations and their corresponding co-occurrence association strengths are summarized to form a quality deviation co-occurrence association set Cor;

[0097] The sequential association condition Ord includes:

[0098] When different defect types (Typ) are arranged in the same quality behavior sequence according to the sequential time (Tsa), their occurrence positions satisfy the adjacent relationship between successive and successive defects.

[0099] When different defect types (Typ) are arranged in the same quality behavior sequence according to the sequential time (Tsa), the sequential interval between the occurrence locations does not exceed the preset sequential distance threshold.

[0100] Different defect types (Typ) appear in the same quality behavior sequence, and there are no other identical defect types (Typ) inserted between the corresponding quality behaviors, thus forming a continuous or quasi-continuous sequential relationship.

[0101] S2 further includes S22;

[0102] S22. Based on the co-occurrence association set Cor of the quality deviations, and combined with the order of quality behaviors in the quality behavior sequence set Beh, a directional analysis is performed on the co-occurrence association between quality deviations: when the first quality deviation occurs earlier than the second quality deviation in the quality behavior sequence, and the co-occurrence association strength between the two reaches a preset threshold, it is determined that there is a transmission relationship between the first quality deviation and the second quality deviation.

[0103] Integrate the quality deviation pairs with transmission relationships and their corresponding transmission directions and transmission intensities to construct a quality deviation association set Rel to describe the quality deviation transmission relationship;

[0104] It should be noted that:

[0105] Co-occurrence correlation is used to quantify the degree to which two quality deviations occur in the same quality behavior sequence, and its value is determined by a combination of the following factors:

[0106] The number of times two quality deviations co-occur in the same quality behavior sequence and satisfy the order association condition Ord;

[0107] The stability of the order interval between the two quality deviations in the quality behavior sequence; wherein, the more times they co-occur and the more stable the order interval, the stronger the co-occurrence association.

[0108] The propagation strength is used to quantify the significance of the propagation of quality deviation from the source quality deviation to the target quality deviation. Its value is determined by the co-occurrence correlation strength and the order stability of the quality deviation in the quality behavior sequence. The greater the co-occurrence correlation strength and the higher the order stability, the greater the propagation strength.

[0109] In this embodiment, based on the quality behavior sequence set Beh, the quality behavior is constrained by introducing the sequence association condition Ord. This makes the identification of the correlation between quality deviations no longer rely on simple simultaneous occurrence or experience judgment, but is based on the actual occurrence sequence of quality behaviors in the construction process. On this basis, by constructing a quality deviation co-occurrence association set Cor, the closeness of different defect types Typ when they repeatedly occur under the same engineering object can be quantified, avoiding misjudging sporadic and disordered quality defects as having a correlation. By introducing directional analysis on the quality deviation co-occurrence association set Cor and forming a quality deviation association set Rel, the relationship between quality deviations is upgraded from "undirected correlation" to "transmission relationship with a clear propagation direction". For example, in actual engineering, when the formwork installation deviation repeatedly appears before the rebar binding process under multiple engineering object identifiers Oid, and the sequential relationship between the two in the quality behavior sequence set Beh is stable, this method can clearly identify the transmission trend of the formwork installation deviation to the quality deviation of subsequent processes through the corresponding quality deviation association set Rel, thereby providing a basis for subsequent targeted supervision. Therefore, step S2 not only improves the accuracy of quality deviation relationship identification, but also provides a reliable input with engineering semantics and temporal constraints for the construction of subsequent quality deviation propagation paths, significantly enhancing the interpretability and practicality of the quality supervision and evaluation results.

[0110] Example 4: Specifically: S3 includes S31;

[0111] S31. Based on the quality deviation association set Rel, extract the process occurrence sequence information corresponding to the source quality deviation and the target quality deviation for each transmission relationship, and construct the construction sequence constraint condition Ocn based on the process occurrence sequence information to determine whether the transmission relationship conforms to the construction process sequence relationship.

[0112] The construction sequence constraint condition Ocn is used to constrain the sequence of operations corresponding to the source quality deviation and the sequence of operations corresponding to the target quality deviation to satisfy the order of the construction process.

[0113] The quality deviation association set Rel is filtered based on the construction sequence constraint Ocn:

[0114] When the transitive relationship satisfies the construction sequence constraint condition Ocn, the transitive relationship is retained as a propagation edge;

[0115] When the transitive relationship does not satisfy the construction sequence constraint condition Ocn, the transitive relationship is discarded;

[0116] The propagation edges that satisfy the construction sequence constraint Ocn are summarized to form a propagation edge set Esg for propagation path generation.

[0117] It should be noted that:

[0118] The construction sequence constraint Ocn is used to define the rules for determining whether a quality deviation propagation relationship is considered a propagable relationship in the construction process. The construction sequence constraint Ocn includes any one of the following:

[0119] The sequence of processes corresponding to the source quality deviation occurs earlier than the sequence of processes corresponding to the target quality deviation.

[0120] The process corresponding to the source quality deviation and the process corresponding to the target quality deviation belong to the same sub-item or to adjacent sub-items with a sequential dependency relationship;

[0121] The difference in the sequence of occurrence of the source quality deviation and the target quality deviation does not exceed the preset sequence span threshold, thereby limiting the propagation relationship to occur within a reasonable span of the construction process;

[0122] The propagation edge set Esg is used to represent the set of propagation relationships that satisfy the construction sequence constraint Ocn, wherein each propagation edge contains at least the source quality deviation, the target quality deviation, and the corresponding propagation direction information, which are used as the basic unit for subsequent propagation path splicing.

[0123] S3 further includes S32;

[0124] S32. Based on the propagation edge set Esg, connect adjacent propagation edges sequentially according to the propagation direction of the propagation edges to form a propagation path candidate containing at least two propagation edges;

[0125] For the propagation path candidates, based on the node connection relationship between propagation edges and the corresponding process sequence relationship, a propagation chain continuity condition Ccn is constructed to determine whether the propagation path candidates have continuous propagation characteristics.

[0126] The propagation chain continuity condition Ccn is used to constrain adjacent propagation edges in the propagation path candidates to maintain continuity and consistency at the quality deviation node level and the construction sequence level.

[0127] The propagation path candidates are verified based on the propagation chain continuity condition Ccn:

[0128] When the candidate propagation path satisfies the propagation chain continuity condition Ccn, the candidate propagation path is determined to be a valid propagation path;

[0129] When the candidate propagation path does not satisfy the propagation chain continuity condition Ccn, the candidate propagation path is determined to be an invalid propagation path;

[0130] The identified effective propagation paths are summarized and stored in a structured manner to form a set of quality deviation propagation chains Chn, which represents the propagation trajectory of quality deviations between different processes and sub-projects.

[0131] It should be noted that:

[0132] The propagation chain continuity condition Ccn is used to determine whether the propagation path candidate formed by splicing the propagation edge set Esg has continuous propagation characteristics that conform to the construction process logic.

[0133] The propagation chain continuity condition Ccn is dynamically constructed based on the structural relationships and construction sequence relationships between the propagation edges in the propagation path candidates. The construction process includes the following:

[0134] Based on the connection relationship between adjacent propagation edges in the propagation path candidate, it is determined whether the target quality deviation of the previous propagation edge is consistent with the source quality deviation of the next propagation edge, so as to construct node continuity constraints to ensure that there is no break in the propagation path at the quality deviation node level.

[0135] Based on the sequence information of the process occurrence corresponding to each propagation edge in the propagation path candidate, it is determined whether the sequence of the process occurrence corresponding to adjacent propagation edges maintains a progressive relationship, so as to construct a sequence continuity constraint to ensure that the propagation path is consistent with the actual process advancement direction in the construction process direction.

[0136] Based on the difference in the sequence of processes between adjacent propagation edges in the propagation path candidate, it is determined whether the difference is within a preset reasonable propagation span range, so as to construct a span continuity constraint to avoid the propagation path crossing unrelated or excessively distant construction stages.

[0137] The propagation chain continuity condition Ccn is determined by at least one or a combination of the node continuity constraint, the sequence continuity constraint, and the span continuity constraint.

[0138] In this embodiment, by introducing a construction sequence constraint Ocn on top of the quality deviation association set Rel, only quality deviation propagation relationships with sequential dependencies in the construction process are retained as propagation edges, further forming a propagation edge set Esg. This eliminates pseudo-propagation relationships that do not conform to the actual construction sequence from the source, avoiding propagation judgments without engineering significance due to statistical correlation. Furthermore, by sequentially connecting the propagation edge set Esg and verifying the propagation path candidates using the propagation chain continuity condition Ccn, the final quality deviation propagation chain set Chn maintains continuity and consistency at the quality deviation node level, construction sequence level, and propagation span level, thus truly reflecting the actual propagation trajectory of quality deviations between different processes and sub-projects. For example, in actual engineering, when a construction deviation in an early process is statistically correlated with quality problems in multiple subsequent processes, this method can exclude correlations that cross unreasonable process stages through the construction sequence constraint Ocn, and retain only effective propagation paths that are passed step-by-step during process advancement through the propagation chain continuity condition Ccn. This ensures that the final quality deviation propagation chain set Chn accurately indicates where the quality problem originates and through which processes it spreads. Therefore, step S3 not only improves the engineering credibility of identifying the propagation path of quality deviation, but also provides a clear and logically consistent propagation chain foundation for subsequent propagation risk assessment, significantly enhancing the guiding value of quality supervision and assessment results in actual construction management.

[0139] Example 5: Specifically: S4 includes S41;

[0140] S41. Based on the set of quality deviation propagation chains Chn, for each quality deviation propagation chain in the set of quality deviation propagation chains Chn formed by an effective propagation path...

[0141] Obtain the number of propagation edges that constitute the quality deviation propagation chain, the transmission strength corresponding to the propagation edge, the scope of the processes and sub-projects it crosses, and the corresponding rectification time data Tim for the quality deviation.

[0142] The chain length index Len, used to represent the propagation path length, is calculated based on the number of propagation edges.

[0143] The intensity index Trn, which represents the significance of propagation, is calculated based on the transmission intensity corresponding to the propagation edge.

[0144] Based on the different process identifiers Pid and different sub-item identifiers Sid that the quality deviation propagation chain crosses, a coverage index Cov is calculated to represent the range of propagation impact.

[0145] And based on the rectification timeliness data Tim, a lag index Lag is calculated to represent the degree of lag in the dissemination of rectification.

[0146] The chain length index Len, the strength index Trn, the coverage index Cov, and the lag index Lag are combined to form the propagation risk assessment index set Met for quantifying the propagation risk of quality deviation propagation chains.

[0147] The aforementioned propagation risk assessment index set Met is used to uniformly represent the potential risk level of the quality deviation propagation chain from multiple dimensions, including propagation length, propagation intensity, propagation scope, and degree of rectification lag.

[0148] It should be noted that:

[0149] Chain length index Len: Based on the number of consecutive propagation edges in the quality deviation propagation chain, the number of propagation edges is counted, and the counting result is defined as the chain length index Len, which is used to characterize the length of the quality deviation propagation path. The more consecutive propagation edges there are, the larger the value of the chain length index Len, thereby reflecting the extent of the extension of the quality deviation propagation level in the construction process.

[0150] Intensity index Trn: Based on the transmission intensity of each propagation edge in the quality deviation propagation chain in the quality deviation association set Rel, the transmission intensity is summarized and the summary result is defined as the intensity index Trn used to characterize the degree of significance of the gradual diffusion of quality deviation along the propagation path. The higher the transmission intensity corresponding to the propagation edge or the more high-intensity propagation edges there are, the larger the value of the intensity index Trn is, thereby reflecting the propagation driving force of quality deviation in the propagation chain.

[0151] Coverage index Cov: Based on the number of different process identifiers Pid and different sub-item identifiers Sid that the quality deviation propagation chain crosses, the scope of the process and sub-item projects is statistically processed, and the statistical results are defined as the coverage index Cov used to characterize the scope of influence of quality deviation propagation. The more processes and sub-items that are crossed, the larger the value of the coverage index Cov, thus reflecting the breadth of the impact of quality deviation propagation on the overall quality of the project.

[0152] Lag index: Based on the rectification timeliness data Tim corresponding to each quality deviation in the quality deviation propagation chain, statistical processing is performed, and the statistical result is defined as the lag index Lag used to characterize the degree of rectification stagnation of the quality deviation during the propagation process. The larger the rectification timeliness data Tim or the longer the cumulative lag time, the larger the value of the lag index Lag, thereby reflecting the time window in which the quality deviation continues to exist and propagates in the construction process.

[0153] S4 further includes S42;

[0154] S42. For the propagation risk assessment index set Met constructed for each quality deviation propagation chain in the quality deviation propagation chain set Chn, obtain the index values ​​of chain length index Len, intensity index Trn, coverage index Cov, and lag index Lag respectively; then perform normalization processing to eliminate indices with different dimensions and different numerical ranges and convert them into dimensionless index values.

[0155] After normalization, based on the preset index weight allocation rules, the normalized chain length index Len, strength index Trn, coverage index Cov, and lag index Lag are assigned corresponding weights, and the normalized indexes are combined and calculated by weighted summation to obtain the propagation risk score of the corresponding quality deviation propagation chain.

[0156] The weighting rules for the indicators are determined based on the statistical analysis results of the impact of each indicator on quality risk in historical engineering quality data, so that indicators with a greater impact on quality risk have a higher weight in the risk dissemination score.

[0157] The propagation risk scores corresponding to each quality deviation propagation chain in the set of quality deviation propagation chains Chn are summarized and processed to obtain the propagation risk assessment result Res, which represents the potential quality risk level during the construction process of the building project.

[0158] S5 includes S51;

[0159] S51. Based on the propagation risk assessment result Res, process the propagation risk score corresponding to each quality deviation propagation chain in the quality deviation propagation chain set Chn: sort or classify the quality deviation propagation chains according to the size of the propagation risk score, and combine the process and sub-project information involved in the quality deviation propagation chain to form the quality supervision and assessment result Out, which reflects the quality supervision and management of construction projects.

[0160] In this embodiment, a propagation risk assessment index set Met is constructed based on the quality deviation propagation chain set Chn, and chain length index Len, intensity index Trn, coverage index Cov, and lag index Lag are introduced to simultaneously quantify the quality deviation propagation risk from multiple dimensions such as propagation length, propagation intensity, impact range, and rectification lag. Based on this, the propagation risk assessment index set Met is normalized and weighted summation is performed to generate a propagation risk assessment result Res with unified dimensions and comparability, thereby avoiding the distortion of risk levels caused by relying solely on experience or a single indicator in traditional supervision. In step S5, the propagation risk assessment result Res is sorted or graded for risk, and combined with the process and sub-project information involved in the quality deviation propagation chain set Chn, a quality supervision assessment result Out that can be directly used for regulatory decision-making is formed. For example, in actual engineering supervision, when multiple quality deviation propagation chains exist simultaneously, this method can clearly distinguish which propagation chains have higher risks due to the large number of processes covered and the larger rectification timeliness data (Tim) through the propagation risk assessment result (Res). It can then prioritize relevant sub-projects in the quality supervision assessment result (Out) by indicating the need for strengthened on-site sampling or an increased rectification response level, thereby enabling quality supervision resources to be precisely allocated to the most risky construction stages. Therefore, steps S4 and S5 not only improve the objectivity and comparability of the quality risk assessment results but also achieve an effective connection from risk quantification analysis to supervision and management decision output, significantly enhancing the pertinence and efficiency of construction engineering quality supervision.

[0161] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for building engineering quality supervision evaluation based on big data, characterized in that: The method comprises the following steps: S1, collecting multi-source quality-related data in the construction process of the building project, and constructing a quality behavior sequence set Beh reflecting the sequence of quality behavior occurrence; S2, performing correlation analysis on the quality behavior sequence set Beh, extracting the correlation between different quality deviations, and constructing a quality deviation correlation set Rel for describing the transmission relationship of quality deviations; S3, based on the quality deviation correlation set Rel, generating the propagation path of quality deviation between different processes and sub-projects according to the construction sequence constraint, and forming a quality deviation propagation chain set Chn; The S3 comprises S31; S31, based on the quality deviation correlation set Rel, extracting the process occurrence sequence information corresponding to the source quality deviation and the target quality deviation for each transmission relationship, and constructing a construction sequence constraint condition Ocn for judging whether the transmission relationship meets the construction process sequence according to the process occurrence sequence information; The construction sequence constraint condition Ocn is used to constrain the process occurrence sequence corresponding to the source quality deviation and the process occurrence sequence corresponding to the target quality deviation to meet the construction process sequence; Based on the construction sequence constraint condition Ocn, the quality deviation correlation set Rel is screened: When the transmission relationship meets the construction sequence constraint condition Ocn, the transmission relationship is retained as a propagation edge; When the transmission relationship does not meet the construction sequence constraint condition Ocn, the transmission relationship is removed; The propagation edges meeting the construction sequence constraint condition Ocn are summarized to form a propagation edge set Esg for propagation path generation; S4, performing propagation risk assessment on the quality deviation propagation chain set Chn, and generating a propagation risk assessment result Res for representing potential quality risks; S5, outputting the building engineering quality supervision evaluation result according to the propagation risk assessment result Res, and forming a quality supervision evaluation result Out.

2. The big data based construction engineering quality supervision evaluation method according to claim 1, characterized in that: The S1 comprises S11; S11, through the data interface of the existing engineering management information system, the quality inspection record system and the rectification closed-loop management system, the multi-source quality-related data in the construction process of the building project is obtained; The multi-source quality-related data comprises construction sequence data Seq, quality defect record data Def and rectification time limit data Tim; Among them, the construction sequence data Seq is extracted according to the process flow record of the construction plan and the construction daily report, so that the construction sequence data Seq comprises process identification Pid, sub-item identification Sid and sequence time Tsa; The quality defect record data Def is extracted according to the quality inspection form and the supervision sampling record, so that the quality defect record data Def comprises defect identification Did, defect type Typ, discovery time Tfd and engineering object identification Oid; The rectification time field is extracted according to the rectification notice and the rectification acceptance record, and the rectification time limit data Tim is calculated based on the rectification completion time and the defect discovery time, wherein the rectification time limit data Tim is calculated by the rectification completion time including Tdn and the discovery time Tfd, and the rectification time limit data Tim is represented as the rectification completion time including Tdn minus the discovery time Tfd; Perform unified processing on the construction sequence data Seq, the quality defect record data Def, and the rectification time limit data Tim; The unified processing includes: field missing completion rule processing, duplicate record removal processing, field format normalization processing, and abnormal timestamp consistency correction processing; After completing the unified processing, object-level association is performed according to the engineering object identifier Oid and the process identifier Pid, and time dimension alignment is performed according to the sequence time Tsa, to form a quality process data set Prc.

3. The big data based construction engineering quality supervision evaluation method according to claim 2, characterized in that: The S1 further includes S12; S12, based on the quality process data set Prc, collects quality events under the same engineering object with the engineering object identifier Oid as the aggregation primary key, and sequentially arranges the collected quality events with the sequence time Tsa as the sorting reference; In the sequential arrangement process, each quality event is mapped to a quality behavior item, and each quality behavior item is assigned a behavior identifier Aid, a process identifier Pid, a defect type Typ, and rectification time limit data Tim, to form a quality behavior sequence arranged in time sequence; The quality behavior sequences formed under the same engineering object identifier Oid are summarized to generate a quality behavior sequence set Beh reflecting the sequence of quality behavior occurrence.

4. The big data-based construction engineering quality supervision evaluation method according to claim 3, characterized in that: The S2 includes S21; S21, based on the quality behavior sequence set Beh, performs co-occurrence statistical analysis on quality behaviors in the same quality behavior sequence that are adjacent in construction sequence or meet the sequence association condition Ord, taking each quality behavior sequence as an analysis unit; In the co-occurrence statistical analysis process, when different defect types Typ meet the preset sequence association condition Ord in the same quality behavior sequence, it is determined that there is a potential association relationship between the corresponding quality deviations, and the co-occurrence association strength between the quality deviations is calculated based on the co-occurrence occurrence number and the sequence interval of the quality deviations in the quality behavior sequence; The quality deviations with co-occurrence association relationship and their corresponding co-occurrence association strength are summarized to form a quality deviation co-occurrence association set Cor.

5. The big data based construction engineering quality supervision evaluation method according to claim 4, characterized in that: The S2 further includes S22; S22, based on the quality deviation co-occurrence association set Cor, analyzes the direction of the co-occurrence association between the quality deviations in combination with the sequence relationship of the quality behaviors in the quality behavior sequence set Beh: when the occurrence order of a first quality deviation in the quality behavior sequence is earlier than that of a second quality deviation, and the co-occurrence association strength between them reaches a preset threshold, it is determined that there is a transmission relationship from the first quality deviation to the second quality deviation; The quality deviation pairs with transmission relationship and the corresponding transmission direction and transmission strength are integrated to construct a quality deviation association set Rel for describing the transmission relationship of quality deviations.

6. The big data based construction engineering quality supervision evaluation method according to claim 1, characterized in that: The S3 further includes S32; S32, based on the propagation edge set Esg, sequentially connects adjacent propagation edges according to the transmission direction of the propagation edges to form a propagation path candidate containing at least two propagation edges; For the propagation path candidate, based on the node connection relationship between the propagation edges and the corresponding process occurrence sequence relationship, a propagation chain continuity condition Ccn for determining whether the propagation path candidate has a continuous propagation feature is constructed; Based on the propagation chain continuity condition Ccn, the propagation path candidate is checked: When the propagation path candidate meets the propagation chain continuity condition Ccn, it is determined that the propagation path candidate is a valid propagation path; When the propagation path candidate does not meet the propagation chain continuity condition Ccn, it is determined that the propagation path candidate is an invalid propagation path; The determined valid propagation path is summarized and stored in a structured manner to form a quality deviation propagation chain set Chn representing the propagation trajectory of the quality deviation between different processes and sub-projects.

7. The big data based construction engineering quality supervision evaluation method according to claim 6, characterized in that: The S4 includes S41; S41, based on the quality deviation propagation chain set Chn, for each quality deviation propagation chain formed by the valid propagation path in the quality deviation propagation chain set Chn; Obtain the number of propagation edges constituting the quality deviation propagation chain, the transmission intensity corresponding to the propagation edge, the process and sub-project range crossed, and the rectification timeliness data Tim of the corresponding quality deviation; Based on the number of propagation edges, calculate the chain length index Len for representing the length of the propagation path; Based on the transmission intensity corresponding to the propagation edge, calculate the intensity index Trn for representing the significant degree of propagation transmission; Based on the different process identifiers Pid and different sub-project identifiers Sid crossed by the quality deviation propagation chain, calculate the coverage index Cov for representing the propagation impact range; And based on the rectification timeliness data Tim, calculate the lag index Lag for representing the lag degree of propagation rectification; Combine the chain length index Len, the intensity index Trn, the coverage index Cov and the lag index Lag to form a propagation risk evaluation index set Met for quantifying the propagation risk of the quality deviation propagation chain.

8. The big data based construction engineering quality supervision evaluation method according to claim 7, characterized in that: The S4 also includes S42; S42, for each quality deviation propagation chain in the quality deviation propagation chain set Chn, the propagation risk evaluation index set Met constructed, respectively, the index values of the chain length index Len, the intensity index Trn, the coverage index Cov and the lag index Lag are obtained; Then perform normalization processing to eliminate different dimensions and different numerical range index conversion to dimensionless index value; After completing the normalization processing, based on the preset index weight allocation rule, the normalized chain length index Len, the intensity index Trn, the coverage index Cov and the lag index Lag are respectively given corresponding weights, and the normalized indexes are combined and calculated by using the weighted summation method. Get the propagation risk score of the corresponding quality deviation propagation chain; The propagation risk scores corresponding to each quality deviation propagation chain in the quality deviation propagation chain set Chn are summarized and processed to obtain the propagation risk evaluation result Res representing the potential quality risk level in the construction process of the building engineering.

9. The big data based construction engineering quality supervision evaluation method according to claim 8, characterized in that: The S5 includes S51; S51, based on the propagation risk assessment result Res, processing the propagation risk score corresponding to each quality deviation propagation chain in the quality deviation propagation chain set Chn: ranking or classifying the quality deviation propagation chains according to the size of the propagation risk score, and combining the process and sub-item engineering information involved in the quality deviation propagation chain to form a quality supervision evaluation result Out for reflecting the construction engineering quality supervision management.

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