Construction project quality detection method and system

Abstract

The invention relates to the field of construction project quality detection, and provides a construction project quality detection method and system, and the method comprises the steps: carrying out the information association processing of construction records, building a process construction combination, dynamically updating a process interval time length reference interval, combining the actual interval time length and specific risk material information, intelligently judging and carrying out the early warning of a potential risk. Therefore, the hidden quality risk caused by the too short process interval duration and the specific risk material can be effectively identified, and the method has the advantages that the hidden quality risk caused by the too short process interval duration and the specific risk material can be effectively identified, and the defects in the aspect of complex multi-factor associated risk identification in the prior art are overcome.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of construction engineering quality detection, in particular, to a construction engineering quality detection method and system. BACKGROUND

[0002] In the field of construction engineering, especially in large-scale project construction, project managers often face great pressure of tight construction period. In order to ensure the delivery of the project on schedule, sometimes measures such as introducing new chemical admixtures to shorten the setting and curing time of materials are taken to accelerate the construction process. However, this seemingly efficient strategy may inadvertently bury potential quality risks in concealed engineering. The particularity of these risks is that they are not caused by obvious construction errors or abnormal duration of a single process, but by the interaction of various factors such as the physical and chemical properties of specific materials, the close connection between processes, and environmental conditions within a very short time window. Traditional quality control systems often focus on compliance checks and sequential verification of single events, and are difficult to capture hidden defects caused by complex event sequences and internal physical mechanisms that form after the standard acceptance node. Therefore, how to identify the missed detection risks hidden behind the seemingly compliant process in this complex and time-sensitive construction environment has become a technical problem to be solved.

[0003] The prior art needs to be improved in view of the above problems. SUMMARY

[0004] In order to solve the problems of the prior art, the present application provides a construction engineering quality detection method and system, which can effectively identify hidden quality risks caused by too short process interval duration and specific risk materials, and makes up for the shortcomings of the prior art in complex multi-factor related risk identification.

[0005] The present application provides a construction engineering quality detection method, which comprises: Collecting construction records corresponding to the construction engineering, and performing information association processing on the process flow time execution information in the construction records to generate process records associated with process type, execution subject, environmental conditions and material use information; Establishing a plurality of process construction combinations according to the process records, determining and updating the process interval duration reference interval of each process construction combination; the process construction combination comprises process type, execution subject and environmental conditions; Comparing the actual interval duration of the current process with the process interval duration reference interval, and when the actual interval duration is lower than the lower limit of the process interval duration reference interval and the material use information associated with the current process includes specific risk materials, it is determined that there is a potential risk; Generating and sending risk warning information to the client.

[0006] Further, the construction records corresponding to the construction project are collected, and information correlation processing is performed on the execution information in the construction records during the process flow to generate process records associated with process types, execution subjects, environmental conditions, and material use information, including: Obtaining process flow information, execution subject information, environmental condition information, and material use information from multiple data sources, and uniformly formatting the data into a preset format; According to the construction project identifier, the data after format unification is cross-data source associated to obtain process records associated with process types, execution subjects, environmental conditions, and material use information.

[0007] Further, a plurality of process construction combinations are established according to the process records, and the process interval duration reference interval of each process construction combination is determined and updated, including: In response to the historical data volume of a process construction combination accumulating to a preset minimum sample size, the arithmetic mean and standard deviation of the interval duration of all completed processes under the process construction combination are calculated; The calculation results obtained by adding and subtracting twice the standard deviation from the arithmetic mean are respectively taken as the upper limit and lower limit of the process interval duration reference interval corresponding to the process construction combination; In response to new process construction combination data being added to the combination, the process interval duration reference interval is updated using a rolling average strategy; the rolling average strategy includes only considering the latest several process construction combinations corresponding to the same process type to perform the above calculation of the arithmetic mean and standard deviation of the interval duration of all completed processes under the process construction combination.

[0008] Further, the process interval duration reference interval of each process construction combination is determined and updated, further including: For each process construction combination, the actual interval duration of the processes under the process construction combination is continuously obtained; The first difference between the actual interval duration and the center value of the process interval duration reference interval is calculated; when the first difference has the same sign in a preset number of consecutive process records, it is determined that the actual interval duration and the process interval duration reference interval have a persistent deviation; According to the direction of the persistent deviation, the center value of the process interval duration reference interval is adjusted; according to the magnitude of the persistent deviation, the range of the process interval duration reference interval is adjusted, thereby updating the process interval duration reference interval.

[0009] Further, it is determined that there is a potential risk, including: The second difference between the actual interval duration and the lower limit of the process interval duration reference interval is calculated, and the deviation degree of the actual interval duration and the process interval duration reference interval is determined according to the proportional relationship between the second difference and the width of the process interval duration reference interval; According to the deviation degree, the risk level of the specific risk material, and the risk factor of the environmental condition, a comprehensive risk score is calculated; The comprehensive risk score is compared with a score threshold, and when the comprehensive risk score exceeds the score threshold, it is determined that there is a potential risk; the score threshold is set according to historical data analysis, industry standards, expert experience or risk tolerance factors.

[0010] Further, the risk factor of the environmental condition is determined in the following manner: A plurality of environmental parameters and a reference database are obtained, and the influence weight of each environmental parameter on risk is determined by matching the reference database according to the plurality of environmental parameters; The risk factor of the environmental condition is obtained by weighted combination of each environmental parameter according to the plurality of environmental parameters and the influence weight.

[0011] Through the above scheme, the influence of the environmental condition on the risk can be quantified scientifically, and the comprehensiveness of the risk assessment is improved.

[0012] To perfect the solution, the application further proposes that the risk level of the specific risk material is determined in the following manner: Laboratory performance testing is performed on the specific risk material to obtain performance test results; According to the performance test results, the physical and chemical stability of the specific risk material under simulated accelerated process conditions is evaluated to obtain physical and chemical stability evaluation results; According to the physical and chemical stability evaluation results, the risk level is determined.

[0013] Further, when the execution subject is a temporary construction team, and the historical data volume of the process construction combination corresponding to the temporary construction team fails to accumulate to a preset minimum sample size; According to the process record, a plurality of process construction combinations are established, and the process interval time length reference interval of each process construction combination is determined and updated, including: Determine the key post personnel in the temporary construction team, and obtain the process interval time length reference interval of the process type and environmental condition corresponding to the team where the key post personnel originally belongs, and determine the initial interval; According to the number ratio of non-key post personnel to key post personnel in the temporary construction team, an efficiency influence coefficient is determined; According to the efficiency influence coefficient, the initial interval is adjusted to obtain the temporary interval time length reference interval.

[0014] Further, according to the process record, a plurality of process construction combinations are established, and the process interval time length reference interval of each process construction combination is determined and updated, further including: In response to the increase in the number of times that the temporary construction team completes the process, the efficiency influence coefficient is gradually reduced, the temporary interval length reference interval converges to the initial interval, and the temporary interval length reference interval is updated.

[0015] In addition, the application also provides a construction engineering quality detection system, the system comprises: The acquisition and processing module is configured to acquire construction records corresponding to the construction engineering, and perform information association processing on execution information in the construction records to generate process records associated with process types, execution subjects, environmental conditions, and material usage information. The determination and update module is configured to establish a plurality of process construction combinations according to the process records, and determine and update process interval length reference intervals of each process construction combination; the process construction combination comprises a process type, an execution subject, and an environmental condition. The risk determination module is configured to compare the actual interval length of the current process with the process interval length reference interval, and determine that there is a potential risk when the actual interval length is lower than the lower limit of the process interval length reference interval and the material usage information associated with the current process includes a specific risk material. The sending module is configured to generate and send risk warning information to the client.

[0016] In summary, the construction engineering quality detection method and system provided by the application can effectively identify hidden quality risks caused by short process interval length and specific risk materials by performing information association processing on construction records, establishing process construction combinations, dynamically updating process interval length reference intervals, combining actual interval length and specific risk material information, intelligently determining and warning potential risks, and effectively identifying hidden quality risks caused by short process interval length and specific risk materials, thereby overcoming the shortcomings of the prior art in complex multi-factor associated risk identification. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0018] Figure 1 is a flowchart of the steps of the construction engineering quality detection method disclosed by the embodiments of the application; Figure 2 is a structural schematic diagram of the construction engineering quality detection system disclosed by the embodiments of the application. DETAILED DESCRIPTION

[0019] The technical solutions in the present application will be described clearly and completely in the present application in combination with the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0020] The present application provides a construction project quality detection method, as shown in the method comprises: Figure 1 The method comprises the following steps: S101, collecting construction records corresponding to the construction project, and performing information association processing on the process flow in the construction records to generate process records associated with process type, execution subject, environmental conditions and material use information; S102, establishing a plurality of process construction combinations according to the process records, and determining and updating the process interval time reference interval of each process construction combination; the process construction combination comprises process type, execution subject and environmental conditions; S103, comparing the actual interval time of the current process with the process interval time reference interval, and when the actual interval time is lower than the lower limit of the process interval time reference interval and the material use information associated with the current process includes a specific risk material, it is determined that there is a potential risk; S104, generating and sending risk warning information to the client.

[0021] In the construction quality detection method, the understanding of some core concepts is crucial for the implementation of the scheme. Among them, the execution information correlation processing during the process flow refers to the integration and logical connection of the process events occurring at different time points in the construction record, which can be realized by data cleaning, data standardization, data fusion, etc. For example, by matching through unified time stamp, engineering identifier or process number, its main purpose is to convert scattered construction data into structured and analyzable process records, providing a complete data basis for subsequent risk assessment. Process record refers to the structured data unit containing process type, execution subject, environmental conditions and material use information after information correlation processing, which can be represented as a record in the database, a data object or a data file, its main purpose is to comprehensively describe the construction background and key elements of a specific process, so as to conduct fine risk analysis. Process construction combination refers to a construction scene classification defined by specific process type, execution subject and environmental conditions (such as high temperature, high humidity, etc.), which can be automatically generated according to preset rules or historical data analysis, such as "concrete pouring-A team-high temperature and humidity environment" or "waterproof laying-B team-rainy day", its main purpose is to classify processes with similar construction background, so as to establish the evaluation benchmark of process interval length for them. Process interval length reference interval refers to the reasonable range of time interval between the completion of previous process and the start of current process for a specific process construction combination, which can be dynamically calculated and updated according to historical construction data through statistical methods or machine learning models, its main purpose is to provide a quantifiable and dynamic benchmark for judging whether the actual interval length of the current process deviates from the normal range, so as to identify potential risks. Specific risk materials refer to materials that may have a negative impact on engineering quality under specific process or environmental conditions, which can be pre-defined and identified according to the physical and chemical properties of materials, historical quality accident data or expert experience. Specifically, the principle of determining specific risk materials is that some materials in the specific process of construction engineering, especially in the context of shortened construction period and accelerated construction process, their physical and chemical properties may interact with the close connection between processes and environmental conditions, thereby inducing potential quality problems in a very short time window. These problems are not caused by obvious construction errors, but by the intrinsic reaction of materials under non-standard or accelerated conditions. Therefore, it is necessary to identify these materials and their risk performance under specific conditions through a special evaluation process.

[0022] The process for determining specific risk materials includes the following steps: First, laboratory performance tests are conducted on the potential specific risk materials. The purpose of this step is to obtain basic performance data of the materials. For example, for concrete admixtures, tests can be conducted to evaluate their effects on cement hydration rate, setting time, and early strength development; for waterproofing membranes, tests can be conducted to evaluate their bond strength, tensile strength, heat resistance, etc. These test results provide basic physical and chemical property data of the materials under standard or ideal conditions.

[0023] Second, based on the laboratory performance test results, further evaluate the physical and chemical stability of the specific risk materials under simulated accelerated construction process conditions. This step is crucial for identifying specific risk materials. Instead of simply repeating standard tests, it simulates the specific environmental stresses that materials may be subjected to according to the accelerated construction process scenarios that may occur in actual construction. For example, if the early strength water reducer mentioned in the background art releases additional heat of hydration when accelerating cement hydration, then when evaluating waterproofing membranes, it is necessary to simulate the scenario where the waterproof layer is covered by high-temperature concrete shortly after installation, and observe the changes in bond strength, microstructure damage, or chemical stability of the membrane under rapid temperature rise and stress. Through this simulation, performance degradation or potential defects of the material in actual accelerated construction process can be revealed. The physical and chemical stability evaluation results will quantify the performance of the material under these non-ideal conditions, such as the degree of bond strength reduction, the occurrence of microcracks, or the abnormal acceleration of chemical reactions.

[0024] Finally, based on the physical and chemical stability evaluation results, determine the risk level of the material. This step converts the quantitative evaluation results mentioned above into an operational risk classification or score. For example, if the material exhibits significant performance degradation or stability problems under simulated accelerated construction process conditions, it can be classified as a high-risk material; if the performance impact is small, it may be classified as medium or low risk. This risk level will serve as an important input for subsequent calculation of the comprehensive risk score, enabling the system to more accurately determine whether there is a potential quality risk. Through the above process, the potential hazards of specific risk materials in construction projects can be objectively and systematically identified and quantified.

[0025] As a specific implementation, assume that in a large underground structure construction project, it is required to pour concrete immediately after the waterproof layer is accepted. The system collects construction records about waterproof layer laying, acceptance, and subsequent concrete pouring by integrating engineering management information systems, on-site sensor networks, and material supply chain data. These records include the type of waterproof layer acceptance process, the information of the supervisor who performed the acceptance, the environmental conditions at the time, such as temperature and humidity, and the list of materials used in the concrete mix. The system processes the raw data to generate structured process records, such as a record that may contain the "waterproof layer acceptance" process type, performed by "supervisor Li", in an environment of "25 degrees Celsius, relative humidity 70%", and the subsequent "concrete pouring" process uses "polycarboxylate early strength water reducing agent" information. Then, the system identifies and establishes multiple process construction combinations based on these process records. For example, a combination may be "waterproof layer acceptance followed by concrete pouring by a specific construction team in a high temperature environment". The system continuously collects historical construction data for this combination and dynamically calculates and updates the process interval time reference interval for this combination based on these data. For example, by analyzing a large amount of historical data, the system may determine that the reasonable interval time reference interval between "waterproof layer acceptance" and "concrete pouring" is 4 to 8 hours for a specific team and in a high temperature environment. When the actual construction is completed, the waterproof layer acceptance is completed, and the concrete pouring process begins. The system will obtain the actual interval time of the current process, for example, if the waterproof layer is accepted at 10:30 am and the concrete is poured at 1:00 pm, the actual interval time is 2.5 hours. The system compares this 2.5 hours with the previously determined process interval time reference interval (e.g. 4 to 8 hours). At the same time, the system checks the material usage information of the current concrete pouring process and finds that it contains "polycarboxylate early strength water reducing agent", which has been pre-set as a specific risk material because it may generate additional heat in the accelerated hydration process, which may adversely affect the adhesion of the waterproof layer that has not yet fully solidified. Since the actual interval time of 2.5 hours is lower than the lower limit of the reference interval of 4 hours, and the specific risk material is used, the system immediately determines that there is a potential risk. Once it is determined that there is a potential risk, the system automatically generates a risk warning message, such as "Warning: the interval between the basement floor waterproof layer and the concrete pouring is too short, and early strength agent is used, which may cause micro-damage to the waterproof layer, please check or take remedial measures immediately", and sends this information to the project manager, quality supervisor and other relevant clients through SMS, email or engineering management APP, so that they can understand the situation in time and take appropriate measures to avoid potential quality problems.

[0026] Further, the construction records corresponding to the construction project are collected, and information association processing is performed on the execution information in the construction records when the work procedures flow, to generate work procedure records associated with work procedure types, execution subjects, environmental conditions, and material use information. Obtain work procedure flow information, execution subject information, environmental condition information, and material use information from multiple data sources, and unify the formats into a preset format; According to the construction project identifier, the data after format unification is cross-data-source associated to obtain work procedure records associated with work procedure types, execution subjects, environmental conditions, and material use information.

[0027] Among them, the multiple data sources refer to different storage or management systems carrying various information in the construction process of the construction project, which can be realized by project management information system, Internet of Things sensor data platform, material supplier database, labor management system, site supervision log system, etc., and the purpose is to collect construction related data scattered in different systems comprehensively; among them, the preset format refers to the pre-defined, unified data structure and data type specification before data processing, which can be realized by standard data formats such as JSON, XML, CSV, or custom database table structure, and the purpose is to eliminate the data format difference between different data sources, and ensure the consistency and processability of the data; among them, the construction project identifier refers to the code or name that uniquely identifies a specific construction project, which can be realized by using unique fields such as project number, contract number, and project name, and the purpose is to serve as a common key value for associating different data sources, ensuring the accuracy of data association; among them, the cross-data-source association refers to the process of logically connecting and integrating originally independent data records from different data sources using a common identifier (such as the construction project identifier), which can be realized by using database JOIN operation, data warehouse ETL (extraction, transformation, loading) process, or association analysis based on graph database, and the purpose is to gather scattered construction information into complete and comprehensive work procedure records.

[0028] The scheme of the present application solves the problem of inconsistent formats of original data from multiple sources by obtaining process flow information, execution subject information, environmental condition information and material use information from multiple data sources and unifying their formats into a preset format. The difference in data formats of different data sources is the main obstacle to information association. Through mandatory format unification, all data to be processed can be ensured to be consistent in structure, laying a foundation for subsequent data integration. On this basis, according to the construction engineering identifier, the data after format unification is associated across data sources to obtain process records associated with process type, execution subject, environmental condition and material use information. As a unique identifier shared between different data sources, the construction engineering identifier can accurately match various types of information related to the same construction project that are originally scattered in different systems, forming a logically complete and comprehensive information process record. This processing method enables information from different sources such as construction logs, sensor data, material purchase orders and personnel attendance records to be effectively integrated, avoiding information silos and ensuring the accuracy and completeness of the process record. Because of providing accurate and complete process records, the scheme can provide high-quality data input for subsequent process construction combination establishment, process interval duration reference interval determination and potential risk judgment. When the process record information is incomplete or inaccurate, subsequent analysis and judgment will face challenges, which may lead to misjudgment or missed judgment. Through the data preprocessing and association mechanism of the present scheme, it is ensured that each process record contains process type, execution subject, environmental condition and material use information, which are the basis for building process construction combination and risk assessment. Therefore, the implementation of the present scheme enables the entire construction engineering quality detection method to be based on more reliable and comprehensive data, thereby improving the accuracy of risk identification and the timeliness of early warning, effectively solving the problem of hidden risks that are difficult to find due to data fragmentation and inconsistency.

[0029] In some preferred embodiments, the present application is implemented as follows: assuming a large construction project, the construction data of which is sourced from multiple independent systems. For example, the process flow information can be stored in a project management information system (such as Primavera P6 or Microsoft Project), the execution subject information can come from a labor attendance system or a human resource management system, the environmental condition information can be collected in real time by Internet of Things sensors deployed on site and stored in a dedicated database, and the material usage information can come from the enterprise's material procurement and inventory management system (such as SAP ERP). In order to generate process records associated with process type, execution subject, environmental condition, and material usage information, first, a data integration module can be configured to periodically obtain raw data from the above-mentioned multiple data sources. For example, the module can obtain process start / end time, process name from the project management information system through API interface, database connection or file import, etc.; obtain construction team ID, personnel information from the labor attendance system; obtain temperature, humidity, wind speed and other environmental parameters in a certain time period from the sensor database; and obtain material batch, usage, material type and other information from the ERP system. After obtaining these data, the data integration module will start the data format unification process. Specifically, a series of data conversion scripts (for example, written in Python or Java) can be developed to convert all the heterogeneous data into a preset unified JSON format. For example, each record can contain the fields of "project ID", "process name", "start time", "end time", "execution team ID", "environmental temperature", "environmental humidity", "material name", "material batch", etc. Subsequently, the data after format unification is cross-sourced according to the construction engineering identifier (for example, the "project number" field contained in each data source). For example, a relational database management system (such as PostgreSQL or MySQL) can be used to import these uniformly formatted data into different temporary tables. Then, by executing the JOIN operation of SQL, taking "project number" and "timestamp" as the association key, the data rows from different temporary tables are matched and merged. For example, the process flow information of a certain process in a certain time period, the team information of the execution of the process, the environmental condition information in the time period, and the material information used by the process are associated through the common "project number" and the similar "timestamp". Finally, these associated data are integrated and stored as a complete process record, each record contains process type, execution subject, environmental condition, and material usage information, providing a comprehensive data basis for subsequent quality detection and risk assessment.

[0030] Further, the step of establishing a plurality of process construction combinations according to the process records, determining and updating the process interval duration reference interval of each process construction combination comprises: in response to the historical data amount of a process construction combination accumulating to a preset minimum sample amount, calculating the arithmetic mean and the standard deviation of the interval duration of all completed processes under the process construction combination; the calculation results of adding and subtracting twice the standard deviation from the arithmetic mean are respectively taken as the upper limit and the lower limit of the process interval duration reference interval corresponding to the process construction combination; in response to new process construction combination data being added to the combination, the process interval duration reference interval is updated using a rolling average strategy; the rolling average strategy includes only considering the latest several process construction combinations corresponding to the same process type to perform the above calculation of the arithmetic mean and the standard deviation of the interval duration of all completed processes under the process construction combination.

[0031] The preset minimum sample size refers to the minimum number of data records that the system requires the combination to accumulate before statistical analysis is performed on the historical process interval duration data of the process construction combination. The purpose is to ensure that the data used to calculate the process interval duration reference interval has sufficient statistical representativeness, thereby avoiding large deviations in the calculation results due to insufficient data, and ensuring the reliability of the reference interval. The arithmetic mean refers to the sum of all values in a set of data divided by the number of data, which reflects the central tendency or average level of the process interval duration. The standard deviation refers to the dispersion of a set of data from its arithmetic mean, which reflects the fluctuation range or stability of the process interval duration. The purpose is to comprehensively quantify and describe the distribution characteristics of the process interval duration under a specific process construction combination through these two statistical quantities. Adding and subtracting twice the standard deviation from the arithmetic mean refers to extending two standard deviations on both sides of the arithmetic mean, thereby determining a statistical interval. The purpose is to construct a reference range that can cover most normal process interval duration fluctuations, while to some extent excluding the influence of extreme outliers, so that the determined process interval duration reference interval has reasonable inclusiveness and discrimination. The rolling average strategy is a data processing and updating method that does not consider all historical data, but only calculates statistical quantities based on a recent period or a certain number of data, and as new data is continuously added, old data is gradually eliminated. The purpose is to make the process interval duration reference interval timely and sensitively reflect the actual situation and trend of the current construction, avoid being disturbed by outdated data, and thereby improve the timeliness and accuracy of the reference interval. Considering only the recent number of process construction combinations of the same process type is a specific implementation of the rolling average strategy, which means that when updating the process interval duration reference interval, the system only selects a limited number of process records with the same process type as the current process construction combination and completed recently for calculation. The purpose is to focus on recent and highly relevant data, thereby more accurately capturing the influence of factors such as current construction technology, team efficiency or environmental conditions on the process interval duration, so that the reference interval can more accurately reflect the latest construction normality.

[0032] In some preferred embodiments, the application is implemented as follows. Assume there is a construction process combination, e.g. “concrete pouring-standard curing-tensioning”, whose historical data are continuously collected and stored in a data management module. When the data management module detects that the number of historical records of the “concrete pouring-standard curing-tensioning” construction process combination reaches a preset minimum sample size, e.g. 30, a data analysis module is triggered. The data analysis module extracts the 30 interval duration data of completed processes from the data management module. Specifically, the data analysis module invokes the built-in statistical calculation function to calculate the arithmetic mean and standard deviation of the 30 interval duration data. For example, if the arithmetic mean is calculated to be 48 hours and the standard deviation is calculated to be 2 hours, then the data analysis module sets 48 hours plus twice the standard deviation (i.e. 48 + 2*2 = 52 hours) as the upper limit of the process interval duration reference interval of the construction process combination, and sets 48 hours minus twice the standard deviation (i.e. 48 - 2*2 = 44 hours) as the lower limit of the process interval duration reference interval of the construction process combination. Thus, the initial process interval duration reference interval of the construction process combination is determined to be [44 hours, 52 hours]. Further, in order to ensure the timeliness of the reference interval, when new “concrete pouring-standard curing-tensioning” construction process combination data, e.g. the 31st, 32nd, etc., are continuously added to the data management module, the data analysis module uses a rolling average strategy to dynamically update the reference interval. Specifically, the rolling average strategy can be set to consider only the last 20 construction process combination data corresponding to the same process type. This means that when the 31st data is added, the system will exclude the earliest data, then recalculate the arithmetic mean and standard deviation of the last 20 data (i.e. the 12th to the 31st data), and update the upper and lower limits of the reference interval according to the new statistical results. For example, if the arithmetic mean of the new 20 data is calculated to be 47 hours and the standard deviation is calculated to be 1.8 hours, then the new reference interval is updated to [47 - 2*1.8, 47 + 2*1.8], i.e. [43.4 hours, 50.6 hours]. In this way, the process interval duration reference interval can continuously reflect the latest construction efficiency and fluctuation, thereby providing a more accurate benchmark for subsequent risk assessment.

[0033] By the technical solution, the application can effectively solve the problem of inaccurate reference interval caused by insufficient historical data of process construction combination, and the problem that simple statistical methods cannot adapt to construction changes. By setting a preset minimum sample size, it is ensured that the calculation of the reference interval is only performed when the data volume is sufficient, thereby improving the reliability of the reference interval. At the same time, the rolling average strategy is used to update the process interval duration reference interval, so that the reference interval can timely reflect the changes such as process improvement and material replacement in the construction process, thereby improving the accuracy and adaptability of the reference interval. This makes the system more accurately identify potential construction risks and improve the effectiveness of risk warning.

[0034] Further, the step of determining and updating the process interval duration reference interval of each process construction combination comprises: For each process construction combination, the actual interval duration of the process under the process construction combination is continuously obtained; A first difference value between the actual interval duration and the center value of the process interval duration reference interval is calculated; when the first difference value keeps consistent in sign in a preset number of continuous process records, it is determined that the actual interval duration and the process interval duration reference interval exist continuous deviation; According to the direction of the continuous deviation, the center value of the process interval duration reference interval is adjusted; according to the amplitude of the continuous deviation, the range of the process interval duration reference interval is adjusted, thereby updating the process interval duration reference interval.

[0035] Wherein, the preset number of continuous process records refers to the number of continuous process records observed by the system when judging whether the actual interval duration and the process interval duration reference interval exist continuous deviation, which can be represented by an integer value, such as 5, 10 or 20, etc. The purpose is to filter out random fluctuations by observing continuous data over a period of time, identify trend deviations, and avoid misjudgment due to single or small amount of abnormal data.

[0036] The scheme of the present application solves the problem that the reference interval of the process interval duration may not timely reflect the continuous deviation in the construction process by introducing a dynamic adjustment mechanism. Specifically, first, for each process construction combination, the system continuously obtains the actual interval duration of the process under the process construction combination, which provides a real-time data basis for subsequent deviation analysis. Then, the system calculates the first difference between the actual interval duration and the center value of the process interval duration reference interval, quantifying the deviation between the actual value and the reference value. The key is that the system further determines whether the first difference is consistent in sign in a preset number of consecutive process records. This judgment mechanism can identify whether there is a continuous deviation trend in the process interval duration, rather than just occasional fluctuations, thereby avoiding false positives. Once it is determined that there is a continuous deviation, the system will adjust the center value of the process interval duration reference interval according to the direction of the deviation, ensuring that the reference interval can follow the overall trend of the actual process duration; at the same time, according to the amplitude of the continuous deviation, the range of the process interval duration reference interval is adjusted, so that the width of the reference interval can adapt to the degree of actual fluctuations. Through this adaptive adjustment, the process interval duration reference interval can dynamically track the actual situation on the construction site, maintaining its guiding significance and accuracy.

[0037] This dynamic adjustment mechanism, combined with the previous method of establishing the process interval duration reference interval, enables the reference interval to be determined and updated based on historical data and recent trends, and also allows for fine-tuning in the event of continuous, trend changes during construction. This combination ensures that the reference interval can always reflect the actual state of the current construction, thereby improving the accuracy and timeliness of risk warnings, enabling the system to detect quality problems earlier and more accurately, such as abnormal acceleration of the process due to project compression or the introduction of new technologies, thereby addressing the risk of concealed engineering omissions mentioned in the background art.

[0038] In some preferred embodiments, the present application is implemented as follows: assuming that there is a process construction combination in a construction project, such as the "concrete pouring-maintenance" process, whose process interval duration reference interval center value is currently 24 hours. The system will continuously obtain the actual interval duration from the completion of each concrete pouring to the end of maintenance under this process construction combination. For example, if the actual interval duration of this process for the last five times is 22 hours, 21.5 hours, 22.5 hours, 21 hours, and 20.5 hours, the system will calculate the first difference between each actual interval duration and the current center value of 24 hours. These differences are -2 hours, -2.5 hours, -1.5 hours, -3 hours, and -3.5 hours. Since the signs of the first differences of the last five consecutive records are all negative (i.e., the actual duration is less than the center value), the system will determine that there is a continuous deviation in the actual interval duration and the deviation direction is shortening.

[0039] At this time, the system will adjust the center value of the process interval duration reference interval downward, for example, from 24 hours to 22 hours, according to the direction of the persistent deviation. At the same time, the system will adjust the range of the reference interval to increase its sensitivity according to the magnitude of the persistent deviation, for example, if the deviation is large and stable, the range of the reference interval may be reduced; if the deviation is small but persistent, only the center value may be adjusted without significantly changing the range. In this way, the process interval duration reference interval can adaptively adapt to changes in the construction site process rhythm, for example, when construction efficiency improves and the process interval duration is generally shortened, the reference interval can be adjusted downward in time to reduce false positives while maintaining sensitivity to extensions.

[0040] Further, the step of determining the potential risk in the present application comprises: calculating a second difference value between the actual interval duration and the lower limit of the process interval duration reference interval, and determining the deviation degree of the actual interval duration from the process interval duration reference interval according to the proportional relationship between the second difference value and the width of the process interval duration reference interval; calculating a comprehensive risk score according to the deviation degree, the risk level of the specific risk material and the risk factor of the environmental condition; comparing the comprehensive risk score with a score threshold, and determining that there is a potential risk when the comprehensive risk score exceeds the score threshold.

[0041] Wherein the second difference refers to the time difference between the actual interval duration and the lower limit of the process interval duration reference interval, which can be a positive value, indicating that the actual interval duration is shorter than the lower limit of the reference interval. Wherein the deviation degree refers to the proportional relationship of the second difference between the actual interval duration and the lower limit of the process interval duration reference interval relative to the width of the process interval duration reference interval, which can be a dimensionless value, used to quantify the degree of deviation of the actual interval duration from the normal range. Wherein the risk level of the specific risk material refers to the severity or possibility of the specific risk material causing quality problems under specific process and environmental conditions, which can be determined according to the physical and chemical properties of the material, historical performance data, expert evaluation or simulation test results. Wherein the risk factor of the environmental condition refers to the quantitative index of the current environmental condition on the negative impact on the process quality or material performance, which can be evaluated according to the temperature, humidity, wind speed, illumination and other environmental parameters and their influence on the construction process, or can be predicted by consulting the environmental risk database or using machine learning model. Wherein the comprehensive risk score refers to the quantitative evaluation value of the potential risk after considering multiple dimensions such as time deviation, material risk and environmental impact, which can be the result of weighted sum, product or other composite calculation model, used to reflect the overall severity of the risk. Wherein the score threshold refers to the critical value of the comprehensive risk score for judging whether there is potential risk, which can be set according to historical data analysis, industry standards, expert experience or risk tolerance, etc. When the comprehensive risk score exceeds the threshold, it is considered that there is potential risk that needs to be concerned.

[0042] In some preferred embodiments, taking the laying of the waterproofing layer and concrete pouring of the underground structure as an example, assuming the waterproofing layer acceptance completion time is 10:30 and the concrete pouring start time is 13:00, the actual interval is 2.5 hours. Assuming that based on historical data and the combination of construction procedures, the lower limit of the reference interval for the interval is 4 hours and the width of the reference interval is 2 hours, the system first calculates the second difference between the actual interval of 2.5 hours and the lower limit of the reference interval of 4 hours, i.e., 4 - 2.5 = 1.5 hours. Then, based on the ratio of this second difference of 1.5 hours to the width of the reference interval of 2 hours, the deviation is determined to be 1.5 / 2 = 0.75. This value of 0.75 quantifies the degree to which the actual interval deviates from the lower limit. Subsequently, the system calculates a comprehensive risk score based on the deviation of 0.75, the risk level of the specific risky material, and the risk factors of the environmental conditions. For example, if a polycarboxylate-based early-strength water-reducing agent was used in this concrete pouring, its risk level is assessed as 0.6, and the current environmental conditions are high temperature and high humidity, with an environmental risk factor of 0.8. The system can use a weighted summation model to calculate the comprehensive risk score, for example: Comprehensive Risk Score = (Deviation × Weight 1) + (Material Risk Level × Weight 2) + (Environmental Risk Factor × Weight 3). Assuming weights 1, 2, and 3 are 0.4, 0.3, and 0.3 respectively, then the comprehensive risk score = (0.75 × 0.4) + (0.6 × 0.3) + (0.8 × 0.3) = 0.3 + 0.18 + 0.24 = 0.72. Here, weights 1, 2, and 3 are empirical values ​​for those skilled in the art. Finally, the system compares the calculated comprehensive risk score of 0.72 with a scoring threshold. If the scoring threshold is set to 0.70, since 0.72 exceeds 0.70, the system will determine that there is a potential risk and can trigger the corresponding risk warning process, such as sending a risk warning message to the project manager, indicating that there may be hidden quality problems caused by the combination of short process intervals, material characteristics and environmental factors.

[0043] Furthermore, the steps for determining the risk factors of environmental conditions include: Obtain multiple environmental parameters and a reference database, and determine the impact weight of each environmental parameter on the risk by matching the reference database with the multiple environmental parameters; Based on multiple environmental parameters and their impact weights, the environmental parameters are weighted and combined to obtain the risk factors of environmental conditions.

[0044] In some preferred embodiments, the risk factors of environmental conditions can be determined in the following manner. Specifically, a plurality of environmental parameters can be acquired in real-time by a sensor network deployed at the construction site, for example, the temperature, humidity, wind speed, rainfall, etc. of the current construction area can be acquired. The data of these environmental parameters can be transmitted to a data processing unit for preliminary processing and storage. Subsequently, the influence weight of each environmental parameter on the risk can be determined according to these acquired environmental parameters. For example, for the concrete pouring process, in a high temperature and high humidity environment, the influence of humidity on the setting speed and strength development of concrete can be greater than that of temperature, therefore, according to historical construction data analysis or combined with industry expert experience, the influence weight of humidity parameter can be set to 0.5, the influence weight of temperature parameter can be set to 0.3, and the influence weight of wind speed parameter can be set to 0.2. These weights can be stored in a reference database and can be dynamically adjusted according to different process types, material properties or seasonal changes. In addition, the influence weight of each environmental parameter on the risk stored in the reference database can be set according to different historical construction data analysis or combined with industry expert experience. Among them, for expert experience evaluation. In this method, experts with rich construction experience will be invited, such as senior engineers, project managers or material scientists. These experts score or rank the importance of each environmental parameter (such as temperature, humidity, wind speed, rainfall, etc.) according to their deep understanding of how different environmental parameters affect the quality of specific processes and the performance of materials, as well as the lessons learned from past projects. Through the aggregation, averaging or negotiation of the evaluation results of multiple experts, a set of preliminary influence weights can be obtained. This method is particularly suitable when there is a lack of a large amount of historical data or when facing new construction processes.

[0045] Another way is to conduct statistical analysis based on historical data. By conducting statistical analysis on historical data, such as regression analysis or correlation analysis, the strength of the relationship between each environmental parameter and quality risk can be quantified. For example, if it is found that there is a significant statistical correlation between a certain environmental parameter (such as high temperature) and a certain specific quality defect (such as concrete cracking), and its change has a greater impact on the probability of defect occurrence, then this environmental parameter will be given a higher influence weight. Through this data-driven method, the actual influence of environmental parameters can be extracted from objective historical performance.

[0046] In addition, machine learning model training can also be used to determine the impact weight. By building a prediction model such as a decision tree, random forest or neural network, and training it with environmental parameters as input features and quality risks or defect occurrences as output targets. After the model is trained, the feature importance or contribution of each environmental parameter can be extracted from the model, which can be used as the weight of its impact on risk. This method can handle complex relationships with multiple variables and non-linearities, resulting in more detailed and accurate impact weights.

[0047] Finally, based on the obtained multiple environmental parameters and the determined impact weights, the risk factor of the environmental condition can be obtained by weighted combination. For example, if the current temperature is T, the humidity is H, and the wind speed is W, the risk factor of the environmental condition can be calculated as: risk factor = (T * 0.3) + (H * 0.5) + (W * 0.2). This calculation result is the quantified environmental condition risk factor, which can comprehensively reflect the potential impact of the current environment on construction quality, and be used in subsequent comprehensive risk score calculation.

[0048] The present application further proposes a method for determining the risk level of a specific risk material, which comprises: performing laboratory performance tests on the specific risk material to obtain performance test results; evaluating the physical and chemical stability of the specific risk material under simulated accelerated process conditions based on the performance test results to obtain a physical and chemical stability evaluation result; determining the risk level based on the physical and chemical stability evaluation result.

[0049] The performance test results refer to the quantitative data or qualitative observations obtained through laboratory performance tests, which can include material compressive strength, tensile strength, permeability coefficient, pH value change or microcrack development, and the purpose is to objectively reflect the inherent properties of the material under standard conditions. Simulated accelerated process conditions refer to experimental methods that reproduce or accelerate harsh environmental factors and process effects that the material may experience in actual engineering construction in a short period of time, which can include high temperature and humidity environment, immersion in specific chemical solutions, cyclic freezing and thawing, or simulated stress loading, and the purpose is to more accurately predict the performance of the material in actual engineering. The risk level refers to the classification or quantitative representation of the degree of potential risk that a specific risk material may cause in a specific application scenario, which can be divided into high risk, medium risk, low risk or corresponding numerical scores, and the purpose is to provide quantitative input for subsequent comprehensive risk scoring.

[0050] In some preferred embodiments, polycarboxylate early strength water reducing agent and high molecular self-adhesive waterproof coiled material used in the construction of the bottom plate of underground structures are used as examples for illustration.

[0051] First, laboratory performance tests are conducted on the high polymer self-adhesive waterproofing membrane, which can specifically include testing the adhesive strength of the back adhesive to the base concrete, the tensile strength of the membrane, and the dimensional stability at different temperatures. At the same time, the hydration heat release curve of the polycarboxylate early strength water reducing agent is tested to obtain the corresponding performance test results.

[0052] Then, based on the performance test results, the physical and chemical stability of the high polymer self-adhesive waterproofing membrane under simulated accelerated process conditions is evaluated. Specifically, a simulated pouring environment can be constructed, the waterproofing membrane is laid on the simulated base layer, and concrete with early strength water reducing agent is poured on top, simulating the temperature and chemical environment that the membrane will be subjected to within a short period of time (e.g. 2.5 hours) after concrete pouring in actual construction. Under this simulated environment, the temperature change at the membrane-concrete interface is continuously monitored, and after the simulation time ends, the adhesive strength, surface integrity, and internal microstructure of the membrane are retested to obtain the physical and chemical stability evaluation results. For example, it can be found that the adhesive strength of the membrane has decreased by a certain percentage or micro-tears have occurred.

[0053] Finally, based on the physical and chemical stability evaluation results, the risk level is determined. For example, if the evaluation results show that the waterproofing membrane has a significant decrease in adhesive strength or micro-damage under simulated accelerated process conditions, it can be determined that the risk level of the high polymer self-adhesive waterproofing membrane combined with the polycarboxylate early strength water reducing agent is high risk; if the damage is slight, it may be medium risk or low risk. In this way, the potential risk of a specific risk material under a specific process combination can be objectively quantified.

[0054] Further, in the present application, when the execution subject is a temporary construction team, and the historical data volume of the process construction combination corresponding to the temporary construction team fails to accumulate to a preset minimum sample size, a plurality of process construction combinations are established according to the process records, and the process interval duration reference interval of each process construction combination is determined and updated, including: Determine the key post personnel in the temporary construction team, and obtain the process interval duration reference interval of the process type and environmental conditions corresponding to the team where the key post personnel originally belong, which is determined as the initial interval; According to the number ratio of non-key post personnel to key post personnel in the temporary construction team, determine the efficiency influence coefficient; According to the efficiency influence coefficient, adjust the initial interval to obtain the temporary interval duration reference interval.

[0055] wherein the key position personnel refers to the core technical or management personnel in the construction team who have a decisive influence on the efficiency, quality or safety of the process execution, such as team leader, technical supervisor, specific equipment operator, etc., whose experience and skill level directly affect the completion of the process. The initial interval refers to the process interval duration reference interval used as a benchmark or starting point when determining the process interval duration reference interval of the temporary construction team, which is derived from the past work data of the key position personnel in other teams. The efficiency influence coefficient refers to the adjustment factor used to quantify the overall efficiency of the temporary construction team relative to the original team efficiency of the key position personnel, which can be calculated based on the composition ratio of different position personnel in the team, such as the number ratio of non-key position personnel to key position personnel, to reflect the proficiency and collaboration level of the team as a whole. The temporary interval duration reference interval refers to the process interval duration reference range obtained by adjusting the initial interval after considering the specific personnel composition and efficiency influence of the temporary construction team, which aims to more accurately reflect the actual process execution efficiency of the temporary construction team under insufficient data conditions.

[0056] In some preferred embodiments, assuming a construction project, when performing a certain specific process, such as pouring of mass concrete, a temporary construction team is formed by the project party due to the temporary allocation of conventional construction teams. Since the temporary construction team is newly formed, the amount of historical data of the corresponding process construction combination has not yet accumulated to the preset minimum sample size, for example, the preset minimum sample size is 30 process records, while the temporary team has only completed 5 records. In this case, the system first determines the key position personnel in the temporary construction team, for example, identifying the team leader of the team as the key position personnel, because he has a decisive influence on the quality and efficiency of concrete pouring. Then, the system obtains the historical process records of the team leader's original team (e.g., "Team X") in the execution of the same process type (concrete pouring) and similar environmental conditions (e.g., high temperature in summer, humid environment), and extracts the process interval duration reference interval corresponding to "Team X" from them, for example, the interval is [120 minutes, 180 minutes]. This interval is determined as the initial interval. Then, the system determines the efficiency influence coefficient according to the number ratio of non-key position personnel to key position personnel in the temporary construction team. The specific determination process is as follows: First of all, it is necessary to clarify the number of key position personnel and non-key position personnel in the temporary construction team. Key position personnel usually refers to core technical or management personnel who have a decisive role in the quality and efficiency of process execution, such as team leader, senior technician, etc. Non-key position personnel refers to other auxiliary or ordinary operating personnel.

[0057] The calculation method of the embodiment is to establish a function relationship based on the proportion. For example, a baseline efficiency coefficient (usually 1.0, representing the same efficiency as an experienced team) can be set, and then the baseline coefficient is corrected according to the proportion of non-key personnel to key personnel. For example, if the number of non-key personnel to key personnel is N:K, the efficiency influence coefficient can be represented as f(N / K). When the value of N / K is larger, the value of f(N / K) is also larger, usually greater than 1.0, indicating that the efficiency is reduced and the process interval time needs to be extended.

[0058] Another implementation is to use a piecewise function or a lookup table method. According to different ranges of the number of non-key personnel to key personnel, the corresponding efficiency influence coefficient values can be preset. For example: when the proportion is less than a certain threshold, the efficiency influence coefficient is set to 1.05; when the proportion is in a certain intermediate range, the efficiency influence coefficient is set to 1.15; when the proportion is greater than a certain higher threshold, the efficiency influence coefficient is set to 1.25. These specific coefficient values and proportion ranges can be determined based on historical project data analysis, industry experience summary or expert evaluation.

[0059] For example, if a temporary construction team consists of 1 key personnel and 4 non-key personnel, the number of non-key personnel to key personnel is 4:1. According to the preset algorithm, this proportion may correspond to an efficiency influence coefficient, for example, 1.2. This means that the efficiency of this temporary team is considered to be lower than that of the team where the key personnel originally belongs, so when adjusting the process interval time reference interval, the initial interval needs to be multiplied by 1.2 to get a longer temporary interval time reference interval, so as to reflect that it may need more time to complete the process to ensure quality.

[0060] The efficiency influence coefficient calculated in this way can provide a quantitative efficiency evaluation for temporary construction teams that lack sufficient historical data, making the determination of the process interval time reference interval more reasonable, thereby improving the accuracy of risk warning.

[0061] The present application further proposes to establish a plurality of process construction combinations according to the process records, determine and update the process interval time reference interval of each process construction combination, and further comprises: In response to the increasing number of times that the temporary construction team completes the process, gradually reduce the efficiency influence coefficient, so that the temporary interval time reference interval converges to the initial interval, thereby updating the temporary interval time reference interval.

[0062] The convergence refers to that the value range of the temporary interval time length reference interval gradually approaches the state of the initial interval in the dynamic adjustment process, and specifically can be simulated by a mathematical model such as linear interpolation, exponential decay or a segmented function, and the purpose is to reflect the objective law that the construction efficiency of the temporary construction team gradually approaches the level of the experienced team after the proficiency of the temporary construction team is improved.

[0063] The scheme of the present application solves the problem that the temporary interval time length reference interval cannot be updated in time after the proficiency of the temporary construction team is improved by introducing a dynamic adjustment mechanism. Specifically, when the system detects that the number of times of completing the process by the temporary construction team increases, the efficiency influence coefficient is gradually reduced. This efficiency influence coefficient is initially used to adjust the initial interval of the team where the key post personnel originally belongs to the temporary interval time length reference interval when there is a lack of historical data, so as to reflect the efficiency difference between the temporary team and the experienced team. With the accumulation of the number of times of completing the process by the temporary construction team, the familiarity and team cooperation of the temporary construction team for a specific process will naturally improve, and the construction efficiency will also improve, gradually approaching or even reaching the level of the experienced team. Therefore, by gradually reducing the efficiency influence coefficient, the temporary interval time length reference interval can smoothly approach the initial interval, that is, converge to a more stable and more representative historical data interval. This dynamic convergence process ensures that the temporary interval time length reference interval can accurately reflect the actual construction proficiency and efficiency level of the temporary construction team in real time. This dynamic adjustment mechanism is combined with the method of determining the initial interval according to the key post personnel data in the previous scheme to form a more perfect and adaptive risk early warning system. In the initial stage of the temporary construction team, the system can provide preliminary risk judgment based on limited information; with the accumulation of team experience, the system can automatically optimize its judgment basis, so that the process interval time length reference interval is more in line with the actual situation, thereby significantly improving the accuracy and reliability of risk early warning, avoiding misjudgment or omission caused by the improvement of team proficiency, and ensuring the effectiveness of construction engineering quality detection.

[0064] In some preferred embodiments, the application is implemented as follows: assuming that a temporary construction team is assigned to carry out a specific concrete pouring process. In the initial stage, due to insufficient historical data of the team, the system will determine an initial process interval duration reference interval based on the historical data of the team where the key personnel (e.g. experienced team leader or core technical worker) originally belonged. At the same time, the system can refer to the above implementation to calculate an initial efficiency impact coefficient according to the proportion of non-key personnel to key personnel in the temporary construction team. For example, if the proportion of non-key personnel is high, the efficiency impact coefficient may be set to 1.3, indicating that its efficiency may be 30% lower than that of the formal team. According to this efficiency impact coefficient, the initial interval is adjusted to obtain a temporary interval duration reference interval. For example, if the initial interval is [10 hours, 12 hours], it may be adjusted to [13 hours, 15.6 hours] after adjustment. As the temporary construction team gradually increases the number of times of completing the concrete pouring process, for example, every 5 times of completing the process, the system will respond to this increase in number to gradually reduce the previously calculated efficiency impact coefficient. For example, the efficiency impact coefficient may be reduced from 1.3 to 1.25 at the first adjustment; after completing 5 times, it may be reduced to 1.2, and so on. This gradual reduction can be achieved using a pre-set decay curve or piecewise function, for example, a function f(N) = 1 + (K-1) * exp(-a*N) can be defined, where N is the number of times of completing the process, K is the initial efficiency impact coefficient, and a is the decay rate constant. In this way, the temporary interval duration reference interval will gradually converge to the initial interval. For example, when the efficiency impact coefficient is reduced to 1.2, the temporary interval duration reference interval may be [12 hours, 14.4 hours], further approaching the initial [10 hours, 12 hours]. This dynamic update ensures that the reference interval used by the system to evaluate the process interval duration of the temporary construction team can reflect the improvement of its proficiency in real time, thereby making the risk warning more accurate and timely.

[0065] In addition, the application further proposes a construction project quality detection system, as shown in Figure 2 The system comprises: The acquisition and processing module 201 is configured to acquire the construction records corresponding to the construction project, and perform information association processing on the execution information in the construction records to generate process records associated with process type, execution subject, environmental conditions and material use information; The determination and update module 202 is configured to establish a plurality of process construction combinations according to the process records, and determine and update the process interval duration reference interval of each process construction combination; the process construction combination comprises process type, execution subject and environmental conditions; The risk determining module 203 is configured to compare the actual interval duration of the current process with the process interval duration reference interval, and determine that there is potential risk when the actual interval duration is lower than the lower limit of the process interval duration reference interval and the material use information associated with the current process includes the specific risk material; The sending module 204 is configured to generate and send the risk warning information to the client.

[0066] The above merely illustrates the embodiments of the present application and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for detecting the quality of a construction work, characterized by, The method comprises: Collecting construction records corresponding to the construction project, and performing information association processing on the execution information in the process flow to generate process records associated with process type, execution subject, environmental conditions, and material use information; According to the process records, a plurality of process construction combinations are established, and the process interval duration reference interval of each process construction combination is determined and updated; the process construction combination includes process type, execution subject, and environmental conditions; Compare the actual interval duration of the current process with the process interval duration reference interval. When the actual interval duration is lower than the lower limit of the process interval duration reference interval, and the material use information associated with the current process includes a specific risk material, it is determined that there is a potential risk; Generate and send risk warning information to the client.

2. The method for detecting the quality of a construction project according to claim 1, wherein, The collection of construction records corresponding to the construction project, and the execution information association processing on the process flow in the construction records to generate process records associated with process type, execution subject, environmental conditions, and material use information, comprises: Obtain process flow information, execution subject information, environmental condition information, and material use information from multiple data sources, and unify the formats to a preset format; According to the preset construction project identifier, the cross-data-source association of the data after format unification is performed to obtain process records associated with process type, execution subject, environmental conditions, and material use information.

3. The construction quality detection method according to claim 1, wherein, According to the process records, a plurality of process construction combinations are established, and the process interval duration reference interval of each process construction combination is determined and updated, comprising: In response to the historical data volume of a process construction combination accumulating to a preset minimum sample size, the arithmetic mean and standard deviation of the interval duration of all completed processes under the process construction combination are calculated; The calculation results obtained by adding and subtracting twice the standard deviation from the arithmetic mean are respectively used as the upper limit and lower limit of the process interval duration reference interval corresponding to the process construction combination; In response to new process construction combination data being added to the combination, the process interval duration reference interval is updated using a rolling average strategy; the rolling average strategy includes only considering the latest several process construction combinations corresponding to the same process type to perform the above calculation of the arithmetic mean and standard deviation of the interval duration of all completed processes under the process construction combination.

4. The construction quality detection method according to claim 3, wherein The determination and update of the process interval duration reference interval of each process construction combination further comprises: For each process construction combination, the actual interval duration of the process under the process construction combination is continuously obtained; Calculate the first difference between the actual interval duration and the center value of the process interval duration reference interval; when the first difference has the same sign in a preset number of consecutive process records, it is determined that the actual interval duration and the process interval duration reference interval have a persistent deviation; According to the direction of the persistent deviation, the center value of the process interval duration reference interval is adjusted; according to the amplitude of the persistent deviation, the range of the process interval duration reference interval is adjusted, thereby updating the process interval duration reference interval.

5. The construction quality detection method according to claim 1, wherein The determination of the potential risk comprises: a second difference between the actual interval duration and the lower limit of the process interval duration reference interval is calculated, and a degree of deviation of the actual interval duration from the process interval duration reference interval is determined according to a proportional relationship between the second difference and a width of the process interval duration reference interval; a comprehensive risk score is calculated according to the degree of deviation, a risk level of the specific risk material, and a risk factor of the environmental condition; a score threshold is preset, and the comprehensive risk score is compared with the score threshold, and when the comprehensive risk score exceeds the score threshold, it is determined that there is a potential risk; the score threshold is set according to historical data analysis, industry standards, expert experience or risk tolerance factors.

6. A method of detecting the quality of a construction work according to claim 5, wherein The risk factor of the environmental condition is determined in the following way: a plurality of environmental parameters and a reference database are obtained, and the influence weight of each environmental parameter on risk is determined by matching the reference database according to the plurality of environmental parameters; The risk factor of the environmental condition is determined in the following way:

7. The method for detecting the quality of a construction project according to claim 5, wherein, The risk level of the specific risk material is determined in the following way: Laboratory performance tests are performed on the specific risk material to obtain performance test results; According to the performance test results, the physical and chemical stability of the specific risk material under simulated accelerated process conditions is evaluated to obtain the physical and chemical stability evaluation results; According to the physical and chemical stability evaluation results, the risk level is determined.

8. The construction quality detection method according to claim 3, wherein, When the execution subject is a temporary construction team, and the historical data volume of the process construction combination corresponding to the temporary construction team fails to accumulate to a preset minimum sample size; The process interval duration reference interval of each process construction combination is determined and updated according to the process record, including: Determine the key post personnel in the temporary construction team, and obtain the process interval duration reference interval of the process type and the environmental condition corresponding to the team where the key post personnel originally belong to, and determine the initial interval; According to the number ratio of non-key post personnel to key post personnel in the temporary construction team, an efficiency influence coefficient is determined; According to the efficiency influence coefficient, the initial interval is adjusted to obtain the temporary interval duration reference interval.

9. A method of construction quality inspection according to claim 8, wherein, The process interval duration reference interval of each process construction combination is determined and updated according to the process record, including: In response to an increase in the number of times the temporary construction team completes the process, the efficiency influence coefficient is gradually reduced, the temporary interval duration reference interval converges to the initial interval, and the temporary interval duration reference interval is updated.

10. A construction work quality detection system characterized by comprising: The system comprises: a collection and processing module for collecting construction records corresponding to construction projects, and for correlating and processing execution information in the process flow of the construction records to generate process records associated with process types, execution subjects, environmental conditions and material use information; A determining and updating module is configured to establish a plurality of process construction combinations according to the process record, and determine and update a process interval duration reference interval of each process construction combination; the process construction combination comprises a process type, an execution subject and an environmental condition; A risk determining module is configured to compare an actual interval duration of a current process with the process interval duration reference interval, and determine that there is a potential risk when the actual interval duration is lower than a lower limit of the process interval duration reference interval and a specific risk material is included in material use information associated with the current process; A sending module is configured to generate and send risk early warning information to a client.

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