Dynamic detection and evaluation method and device for hydraulic engineering quality and computer equipment

Abstract

The invention relates to a dynamic detection and evaluation method and device for hydraulic engineering quality and computer equipment. The method comprises the following steps: acquiring current concrete freeze-thaw data acquired by intelligent freeze-thaw equipment, and identifying freeze-thaw process data of concrete based on the current concrete freeze-thaw data; based on the freeze-thaw process data of the concrete, identifying quality safety data of each quality safety evaluation type of the concrete through a data detection model, and based on the quality safety data of each quality safety evaluation type, identifying current quality abnormal information of the concrete; and generating current maintenance data of the concrete based on the current quality abnormal information of the concrete, and generating a current detection and evaluation result of the water conservancy project quality based on the current maintenance data of the concrete and the current quality abnormal information of the concrete. By adopting the method, the detection accuracy of quality safety detection of the water conservancy project can be improved.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things and data analysis technology, and in particular to a method, device and computer equipment for dynamic detection and evaluation of water conservancy project quality. Background Art

[0002] Water conservancy project quality and safety testing involves qualified testing organizations inspecting and measuring the construction quality, materials, and products of water conservancy projects. This testing also involves comparing the effectiveness of materials applied to determine whether the project's quality meets standards. Therefore, water conservancy project quality and safety testing is fundamental to ensuring and maintaining the quality and safety of water conservancy projects. Among these testing areas, concrete safety and quality testing is a key component. Therefore, improving concrete safety and quality testing is central to water conservancy project safety and quality testing.

[0003] Among the existing concrete safety detection methods, the core drilling method is mainly used to detect concrete quality and safety. This method has high detection accuracy, but it is also destructive and not continuous. Moreover, this concrete quality detection method mainly relies on manual detection and manual analysis, which makes the analysis results subject to subjectivity, bias, and limitations, resulting in low detection accuracy for water conservancy project quality and safety detection. Summary of the Invention

[0004] Based on this, it is necessary to provide a dynamic detection and evaluation method, device and computer equipment for water conservancy project quality to address the above technical problems.

[0005] In a first aspect, the present application provides a dynamic detection and evaluation method for water conservancy project quality, comprising: Acquire current concrete freeze-thaw data collected by the intelligent freeze-thaw device, and identify freeze-thaw process data of the concrete based on the current concrete freeze-thaw data; Based on the freeze-thaw process data of the concrete, identifying quality and safety data of each quality and safety assessment type of the concrete through a data detection model, and identifying current quality abnormality information of the concrete based on the quality and safety data of each quality and safety assessment type; Based on the current quality abnormality information of the concrete, the current maintenance data of the concrete is generated, and based on the current maintenance data of the concrete and the current quality abnormality information of the concrete, the current detection and evaluation result of the water conservancy project quality is generated.

[0006] Optionally, identifying freeze-thaw process data of concrete based on the current concrete freeze-thaw data includes: Splitting the current concrete freeze-thaw data into freezing process data and melting process data; identifying first parameter distribution information of each concrete parameter type in the freezing process data, and identifying second parameter distribution information of each concrete parameter type in the dissolving process data; The first parameter distribution information of each concrete parameter type and the second parameter distribution information of each concrete parameter type are used as freeze-thaw process data of the concrete.

[0007] Optionally, the data detection model includes a freezing process detection program and a dissolution process detection program. Based on the freeze-thaw process data of the concrete, identifying the quality and safety data of each quality and safety assessment type of the concrete through the data detection model includes: Based on the first parameter distribution information of each concrete parameter type, identifying detection data of each first detection type of the concrete during the freezing process through a freezing process detection program; and based on the second parameter distribution information of each concrete parameter type, identifying detection data of each second detection type of the concrete during the dissolving process through a dissolving process detection program; Based on the detection data of each first detection type and the detection data of each second detection type, quality and safety data of each quality and safety assessment type of the concrete are identified through a quality and safety identification network.

[0008] Optionally, the identifying of current abnormal quality information of the concrete based on the quality safety data of each quality safety assessment type includes: Based on the quality safety data of each quality safety assessment type, querying the first abnormality information of the direct abnormality type of the concrete and the second abnormality information of the indirect abnormality type of the concrete through a quality abnormality database; The first abnormality information of each direct abnormality type and the second abnormality information of each indirect abnormality type are used as the current quality abnormality information of the concrete.

[0009] Optionally, generating current curing data of the concrete based on the current quality abnormality information of the concrete includes: Obtaining the current usage time of the concrete and the current environmental data of the concrete, and querying an abnormality maintenance database based on each of the direct abnormality types and each of the indirect abnormality types to identify an abnormality maintenance plan corresponding to the concrete; generating abnormal curing information of each abnormal curing method of the concrete based on the current usage time of the concrete, the current environmental data of the concrete, the first abnormality information of each direct abnormality type, and the second abnormality information of each indirect abnormality type, through the abnormal curing plan; The abnormal curing information of each abnormal curing method is used as the current curing data of the concrete.

[0010] Optionally, generating a current inspection and evaluation result of the water conservancy project quality based on the current maintenance data of the concrete and the current quality abnormality information of the concrete includes: Obtaining a test and evaluation report of the concrete, and identifying a reporting area corresponding to each reporting data type in the test and evaluation report; Based on the current curing data of the concrete and the current quality abnormality information of the concrete, identifying the target reporting data content of each reporting data type; The target reporting data content of each reporting data type is filled into the reporting area corresponding to each reporting data type to obtain the current detection and evaluation results of the water conservancy project quality.

[0011] In a second aspect, the present application further provides a dynamic detection and evaluation device for water conservancy project quality, comprising: An acquisition module is used to acquire current concrete freeze-thaw data collected by the intelligent freeze-thaw device, and identify freeze-thaw process data of the concrete based on the current concrete freeze-thaw data; an identification module for identifying, based on freeze-thaw process data of the concrete and a data detection model, quality and safety data of each quality and safety assessment type of the concrete, and identifying current quality abnormality information of the concrete based on the quality and safety data of each quality and safety assessment type; A generation module is used to generate current maintenance data of the concrete based on the current quality abnormality information of the concrete, and to generate a current detection and evaluation result of the quality of the water conservancy project based on the current maintenance data of the concrete and the current quality abnormality information of the concrete.

[0012] Optionally, the acquisition module is specifically configured to: Splitting the current concrete freeze-thaw data into freezing process data and melting process data; identifying first parameter distribution information of each concrete parameter type in the freezing process data, and identifying second parameter distribution information of each concrete parameter type in the dissolving process data; The first parameter distribution information of each concrete parameter type and the second parameter distribution information of each concrete parameter type are used as freeze-thaw process data of the concrete.

[0013] Optionally, the identification module is specifically configured to: Based on the first parameter distribution information of each concrete parameter type, identifying detection data of each first detection type of the concrete during the freezing process through a freezing process detection program; and based on the second parameter distribution information of each concrete parameter type, identifying detection data of each second detection type of the concrete during the dissolving process through a dissolving process detection program; Based on the detection data of each first detection type and the detection data of each second detection type, quality and safety data of each quality and safety assessment type of the concrete are identified through a quality and safety identification network.

[0014] Optionally, the identification module is specifically configured to: Based on the quality safety data of each quality safety assessment type, querying the first abnormality information of the direct abnormality type of the concrete and the second abnormality information of the indirect abnormality type of the concrete through a quality abnormality database; The first abnormality information of each direct abnormality type and the second abnormality information of each indirect abnormality type are used as the current quality abnormality information of the concrete.

[0015] Optionally, the generating module is specifically configured to: Obtaining the current usage time of the concrete and the current environmental data of the concrete, and querying an abnormality maintenance database based on each of the direct abnormality types and each of the indirect abnormality types to identify an abnormality maintenance plan corresponding to the concrete; generating abnormal curing information of each abnormal curing method of the concrete based on the current usage time of the concrete, the current environmental data of the concrete, the first abnormality information of each direct abnormality type, and the second abnormality information of each indirect abnormality type, through the abnormal curing plan; The abnormal curing information of each abnormal curing method is used as the current curing data of the concrete.

[0016] Optionally, the generating module is specifically configured to: Obtaining a test and evaluation report of the concrete, and identifying a reporting area corresponding to each reporting data type in the test and evaluation report; Based on the current curing data of the concrete and the current quality abnormality information of the concrete, identifying the target reporting data content of each reporting data type; The target reporting data content of each reporting data type is filled into the reporting area corresponding to each reporting data type to obtain the current detection and evaluation results of the water conservancy project quality.

[0017] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0019] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0020] The above-mentioned dynamic detection and assessment method, device, and computer equipment for water conservancy project quality include: obtaining current concrete freeze-thaw data collected by intelligent freeze-thaw equipment, and identifying concrete freeze-thaw process data based on the current concrete freeze-thaw data; based on the concrete freeze-thaw process data, identifying quality and safety data of each quality and safety assessment type of the concrete through a data detection model, and identifying current quality anomaly information of the concrete based on the quality and safety data of each quality and safety assessment type; generating current concrete curing data based on the current quality anomaly information of the concrete, and generating current detection and assessment results of the water conservancy project quality based on the current concrete curing data and the current quality anomaly information of the concrete. This solution, through the intelligent freeze-thaw equipment designed by the inventor, collects current concrete freeze-thaw data in real time, eliminating the need for actual data collection and testing of the actual water conservancy project, thus preventing damage and impact to the water conservancy project, and can comprehensively, accurately, continuously, and efficiently detect the quality and safety of the water conservancy project. Then, during the analysis and assessment process, this solution uses a data detection model to identify quality and safety data for each type of concrete quality and safety assessment. It also uses intelligent analysis strategies to identify abnormalities in the concrete's current quality, thereby generating current concrete maintenance data. This not only avoids the subjectivity, bias, and limitations of manual analysis, but also improves the analysis and identification of concrete maintenance needs while achieving accuracy in concrete quality and safety assessments. The resulting current inspection and assessment results for water conservancy project quality include both concrete quality assessment information and concrete safety maintenance data, improving assessment accuracy and making safety maintenance recommendations more intuitive for engineering personnel. This improves the accuracy of water conservancy project quality and safety testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 1 is a flow chart of a method for dynamic detection and evaluation of water conservancy project quality in one embodiment; Figure 2 A schematic diagram of a flow chart of an example of dynamic detection and evaluation of water conservancy project quality in one embodiment; Figure 3 1 is a structural block diagram of a dynamic detection and evaluation device for water conservancy project quality in one embodiment; Figure 4 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0024] The dynamic monitoring and assessment method for water conservancy project quality provided in the embodiments of this application can be applied in the context of a dynamic monitoring and assessment system for water conservancy project quality. The dynamic monitoring and assessment system for water conservancy project quality includes a fully automatic, rapid, intelligent freeze-thaw test instrument for concrete (i.e., intelligent freeze-thaw equipment) and a concrete accelerated curing device, among other equipment. The freezing and thawing processes of concrete specimens are both performed within the same chamber. The fully automatic, rapid, intelligent freeze-thaw test instrument utilizes a dual-slot motion method, consisting of a freezing slot and a thawing slot, with specimens completing the freezing and thawing processes separately in each slot. This method does not damage or impact water conservancy projects and can comprehensively, accurately, continuously, and efficiently assess the quality and safety of water conservancy projects. The system can be applied to a terminal, a server, or a system comprising both a terminal and a server, and is implemented through interaction between the two. The terminal can be, but is not limited to, various personal computers, laptops, and the like. The terminal uses the intelligent freeze-thaw device designed by the inventor to collect current concrete freeze-thaw data in real time. This eliminates the need for actual data collection and testing of the water conservancy project, preventing damage or impact to the project. Furthermore, it can comprehensively, accurately, continuously, and efficiently monitor the quality and safety of the water conservancy project. During the analysis and assessment process, this solution uses a data detection model to identify quality and safety data for each type of concrete quality and safety assessment. Furthermore, through intelligent analysis strategies, it identifies current concrete quality anomalies, thereby generating current concrete maintenance data. This approach not only avoids the subjectivity, bias, and limitations of manual analysis, but also improves the analysis and identification of concrete maintenance needs while achieving higher accuracy in concrete quality and safety assessments. The resulting current water conservancy project quality assessment results contain both concrete quality assessment information and concrete safety maintenance data, improving assessment accuracy and making safety maintenance recommendations more intuitive for engineering personnel. This improves the accuracy of water conservancy project quality and safety testing.

[0025] In an exemplary embodiment, Figure 1 As shown, a dynamic detection and evaluation method for water conservancy project quality is provided, which is described by taking the application of the method to a terminal as an example, and includes the following steps S101 to S103. Step S101: obtaining current concrete freeze-thaw data collected by the intelligent freeze-thaw equipment, and identifying freeze-thaw process data of the concrete based on the current concrete freeze-thaw data.

[0026] In this embodiment, engineers collect concrete specimens for water conservancy projects. They then test the freezing and thawing processes of these specimens using a fully automatic, rapid, intelligent freeze-thaw test instrument (i.e., an intelligent freeze-thaw device) developed by the inventors of this solution. Sensors in the intelligent freeze-thaw device collect relevant data from these freezing and thawing processes to obtain current concrete freeze-thaw data. This relevant data includes, but is not limited to, freeze / thaw time data, freeze / thaw rate data, freeze / thaw temperature data, freeze / thaw condition data, and freeze / thaw environment data. The terminal then identifies the freeze-thaw process data based on the current concrete freeze-thaw data. This freeze-thaw process data includes parameter distribution information for various concrete parameter types. This parameter distribution information is obtained by sorting the data in chronological order. These concrete parameter types include, but are not limited to, strength parameters, cover thickness parameters, position change parameters, hardening performance parameters, and mixture performance parameters. The specific identification process will be described in detail later.

[0027] Step S102 : Based on the freeze-thaw process data of the concrete, the quality and safety data of each quality and safety assessment type of the concrete are identified through a data detection model, and based on the quality and safety data of each quality and safety assessment type, the current quality abnormality information of the concrete is identified.

[0028] In this embodiment, the terminal uses a data detection model based on concrete freeze-thaw process data to identify quality and safety data for each quality and safety assessment type of concrete. Furthermore, based on the quality and safety data for each quality and safety assessment type, it identifies current quality anomalies in the concrete. The data detection model includes a freezing process detection program and a melting process detection program. These programs detect concrete quality indicator data during the freezing and melting processes, respectively. This indicator data includes, but is not limited to, cement type, strength grade, stability, and setting time; sand and gravel particle size, mud content, and mud lump content; admixture dosage and activity index of fly ash, silica fume, and other admixtures; concrete mix workability (fluidity, cohesion, and water retention); compressive strength, flexural strength, and durability; and axis dimensions, elevation, and flatness.

[0029] Step S103: generating current concrete curing data based on the current concrete quality abnormality information, and generating current inspection and evaluation results of the water conservancy project quality based on the current concrete curing data and the current concrete quality abnormality information.

[0030] In this embodiment, the terminal generates current concrete maintenance data based on the current concrete quality abnormality information, and generates current inspection and evaluation results of the water conservancy project quality based on the current concrete maintenance data and the current concrete quality abnormality information.

[0031] Based on the above scheme, the intelligent freeze-thaw equipment designed by the inventor collects current concrete freeze-thaw data in real time, eliminating the need for actual data collection and testing of the water conservancy project, preventing damage and impact to the water conservancy project, and enabling comprehensive, accurate, continuous, and efficient testing of the quality and safety of the water conservancy project. Subsequently, during the analysis and assessment process, this scheme uses a data detection model to identify quality and safety data for each quality and safety assessment type of concrete, and uses an intelligent analysis strategy to identify current quality anomaly information of concrete, thereby generating current maintenance data for the concrete. This not only avoids the subjectivity, bias, and limitations of manual analysis, but also improves the analysis and identification of concrete maintenance needs while achieving the accuracy of concrete quality and safety assessment. The generated current test and assessment results for the water conservancy project quality include both quality assessment information and safety maintenance data for concrete, improving the accuracy of the assessment and the intuitiveness of safety maintenance recommendations for engineering personnel, thereby comprehensively improving the accuracy of water conservancy project quality and safety testing.

[0032] Optionally, based on the current concrete freeze-thaw data, identifying the freeze-thaw process data of the concrete, including: splitting the current concrete freeze-thaw data into freezing process data and dissolution process data; identifying first parameter distribution information of each concrete parameter type in the freezing process data, and identifying second parameter distribution information of each concrete parameter type in the dissolution process data; using the first parameter distribution information of each concrete parameter type and the second parameter distribution information of each concrete parameter type as the freeze-thaw process data of the concrete.

[0033] In this embodiment, the terminal splits the current concrete freeze-thaw data into freezing process data and melting process data. The terminal then uses a pre-configured conversion process between the parameter data for each concrete parameter type and the freezing process data to identify first parameter distribution information for each concrete parameter type in the freezing process data. Similarly, the terminal identifies second parameter distribution information for each concrete parameter type in the melting process data.

[0034] Finally, the terminal uses the first parameter distribution information of each concrete parameter type and the second parameter distribution information of each concrete parameter type as freeze-thaw process data of the concrete.

[0035] Based on the above scheme, by splitting the freezing process data and the dissolution process data, and then identifying the parameter distribution information of different concrete parameter types in the freezing process and the dissolution process respectively, the comprehensiveness and accuracy of the performance detection of concrete in different processes are improved.

[0036] Optionally, the data detection model includes a freezing process detection program and a dissolution process detection program. Based on the freeze-thaw process data of the concrete, the data detection model is used to identify the quality and safety data of each quality and safety assessment type of the concrete, including: based on the first parameter distribution information of each concrete parameter type, the freezing process detection program is used to identify the detection data of each first detection type of the concrete in the freezing process, and based on the second parameter distribution information of each concrete parameter type, the dissolution process detection program is used to identify the detection data of each second detection type of the concrete in the dissolution process; based on the detection data of each first detection type and the detection data of each second detection type, the quality and safety identification network is used to identify the quality and safety data of each quality and safety assessment type of the concrete.

[0037] In this embodiment, the terminal uses a freezing process detection program to identify test data of each first test type during the freezing process based on first parameter distribution information for each concrete parameter type. It also uses a dissolving process detection program to identify test data of each second test type during the dissolving process based on second parameter distribution information for each concrete parameter type. Then, based on the test data of each first test type and each second test type, the terminal uses a quality and safety identification network to identify quality and safety data for each quality and safety assessment type of the concrete. The first test types for the freezing process differ from the second test types for the dissolving process, and are respectively required for testing during the freezing process and the dissolving process. For example, test types required for the freezing process include, but are not limited to, setting time, strength grade, and stability, while test types required for the dissolving process include, but are not limited to, flowability, cohesion, and water retention. The quality and safety identification network is a deep learning-based classifier neural network that identifies quality and safety data for each quality and safety assessment type based on test data of different test types. Quality and safety assessment types include, but are not limited to, structural safety assessment, structural durability assessment, and material performance assessment. Among them, different quality and safety assessment types correspond to one or more different detection types. The quality and safety data of each quality and safety assessment type is the data obtained after data conversion of the detection data of each detection type corresponding to the quality and safety assessment type. The terminal trains the quality and safety identification network, and then inputs the detection data of each detection type into the quality and safety identification network to obtain the quality and safety data of each quality and safety assessment type.

[0038] Based on the above scheme, the quality and safety identification network designed by this scheme combines the detection data of different detection types to identify the quality and safety data of different quality and safety assessment types, thereby improving the recognition accuracy and efficiency of quality and safety data of different quality and safety assessment types.

[0039] Optionally, based on the quality safety data of each quality safety assessment type, the current quality abnormality information of the concrete is identified, including: based on the quality safety data of each quality safety assessment type, querying the first abnormality information of the direct abnormality type of the concrete and the second abnormality information of the indirect abnormality type of the concrete through the quality abnormality database; using the first abnormality information of each direct abnormality type and the second abnormality information of each indirect abnormality type as the current quality abnormality information of the concrete.

[0040] In this embodiment, based on the quality and safety data of each quality and safety assessment type, the terminal queries a quality anomaly database for first anomaly information of a direct anomaly type of concrete and second anomaly information of an indirect anomaly type of concrete. The quality anomaly database is a database constructed based on association information (including explicit and implicit association information) between quality and safety data of different quality and safety assessment types of each quality anomaly information. This allows the terminal to directly adapt the anomaly information of different anomaly types within the association map based on the quality and safety data range to which the quality and safety data of different quality and safety assessment types belong. The terminal then uses the anomaly information adapted by the explicit association information as the first anomaly information of the direct anomaly type, and the anomaly information adapted by the implicit association information as the second anomaly information of the indirect anomaly type.

[0041] Finally, the terminal uses the first abnormality information of each direct abnormality type and the second abnormality information of each indirect abnormality type as the current quality abnormality information of the concrete.

[0042] Based on the above scheme, the association map containing various quality abnormality information constructed by this scheme can identify the current quality abnormality information of concrete, thereby improving the comprehensiveness and accuracy of the identification of the current quality abnormality information of concrete.

[0043] Optionally, based on the current quality abnormality information of the concrete, the current maintenance data of the concrete is generated, including: obtaining the current usage time of the concrete and the current environmental data of the concrete, and based on each direct abnormality type and each indirect abnormality type, querying the abnormal maintenance database to identify the abnormal maintenance plan corresponding to the concrete; based on the current usage time of the concrete, the current environmental data of the concrete, the first abnormality information of each direct abnormality type, and the second abnormality information of each indirect abnormality type, generating abnormal maintenance information of each abnormal maintenance method of the concrete through the abnormal maintenance plan; and using the abnormal maintenance information of each abnormal maintenance method as the current maintenance data of the concrete.

[0044] In this embodiment, the terminal obtains the current usage time of the concrete and its current environmental data. Based on the direct and indirect abnormality types, the terminal queries the abnormality maintenance database to identify the corresponding abnormal maintenance plan for the concrete. The abnormality maintenance database includes a correspondence between each abnormal maintenance plan and each abnormality type. By querying this correspondence, the terminal identifies the corresponding abnormal maintenance plan for the concrete. The current environmental data includes, but is not limited to, the humidity, temperature, climate, and environmental disturbance data (such as vehicle type, personnel usage, animal movement, and geological disaster data).

[0045] Then, based on the current usage time of the concrete, the current environmental data of the concrete, the first abnormality information of each direct abnormality type, and the second abnormality information of each indirect abnormality type, the terminal generates abnormal curing information for each abnormal curing method of the concrete through an abnormal curing plan. The abnormal curing plan includes abnormal curing data corresponding to different abnormal curing methods, such as natural curing, steam curing, and vacuum dehydration curing. Different abnormal curing methods correspond to different usage time and environmental data of the concrete. The terminal then adapts the abnormal curing method to the concrete based on the current usage time and environmental data of the concrete.

[0046] Each abnormal curing method includes various curing parameters (e.g., curing frequency, curing duration, curing materials, and curing procedures). Each curing parameter is adapted to a different abnormal information range, meaning that different abnormal information ranges correspond to different curing parameters. Therefore, based on the first abnormality information for each direct abnormality type and the second abnormality information for each indirect abnormality type, the terminal identifies the curing parameters for the abnormal curing method adapted for the concrete and obtains the concrete's current curing data.

[0047] Based on the above scheme, by combining the usage time, environmental data, and abnormal information of the concrete, the current maintenance data of the concrete is comprehensively generated, thereby improving the adaptability to abnormal maintenance of the concrete and the abnormal maintenance effect.

[0048] Optionally, based on the current maintenance data of concrete and the current quality abnormality information of concrete, the current inspection and evaluation results of the water conservancy project quality are generated, including: obtaining the inspection and evaluation report of concrete, and identifying the reporting areas corresponding to each reporting data type in the inspection and evaluation report; based on the current maintenance data of concrete and the current quality abnormality information of concrete, identifying the target reporting data content of each reporting data type; filling the target reporting data content of each reporting data type into the reporting area corresponding to each reporting data type, to obtain the current inspection and evaluation results of the water conservancy project quality.

[0049] In this embodiment, the terminal obtains a concrete inspection and evaluation report and identifies the reporting areas corresponding to each reporting data type in the inspection and evaluation report. The terminal then identifies the target reporting data content for each reporting data type based on the current concrete curing data and current concrete quality abnormality information. The reporting data types include, but are not limited to, abnormality information of different abnormality types or current curing data for each abnormal curing method.

[0050] Then, the terminal fills the target reporting data content of each reporting data type into the reporting area corresponding to each reporting data type to obtain the current detection and evaluation results of the water conservancy project quality.

[0051] Based on the above scheme, by filling in the data of the automated inspection and evaluation report, the comprehensiveness and intuitiveness of the inspection and evaluation information on the quality of concrete in water conservancy projects are improved, and the generated inspection and evaluation results are more accurate, avoiding the subjectivity, one-sidedness and limitations of manual inspection and evaluation, thereby comprehensively improving the inspection accuracy of water conservancy project quality and safety inspection.

[0052] This application also provides an example of dynamic detection and evaluation of water conservancy project quality, such as Figure 2 As shown, the specific processing process includes the following steps: Step S201: obtaining current concrete freeze-thaw data collected by intelligent freeze-thaw equipment.

[0053] Step S202: split the current concrete freeze-thaw data into freezing process data and melting process data.

[0054] Step S203: identifying first parameter distribution information of each concrete parameter type in the freezing process data, and identifying second parameter distribution information of each concrete parameter type in the dissolving process data.

[0055] Step S204: using the first parameter distribution information of each concrete parameter type and the second parameter distribution information of each concrete parameter type as freeze-thaw process data of the concrete.

[0056] Step S205: Based on the first parameter distribution information of each concrete parameter type, the freezing process detection program is used to identify the detection data of each first detection type of concrete during the freezing process; and based on the second parameter distribution information of each concrete parameter type, the dissolution process detection program is used to identify the detection data of each second detection type of concrete during the dissolution process.

[0057] Step S206 : Based on the detection data of each first detection type and the detection data of each second detection type, quality and safety data of each quality and safety assessment type of concrete are identified through a quality and safety identification network.

[0058] Step S207 : Based on the quality safety data of each quality safety assessment type, query the quality abnormality database for first abnormality information of a direct abnormality type of concrete and second abnormality information of an indirect abnormality type of concrete.

[0059] Step S208: The first abnormality information of each direct abnormality type and the second abnormality information of each indirect abnormality type are used as the current quality abnormality information of the concrete.

[0060] Step S209: obtaining the current usage time of the concrete and the current environmental data of the concrete, and querying the abnormality maintenance database based on each direct abnormality type and each indirect abnormality type to identify the abnormal maintenance plan corresponding to the concrete.

[0061] Step S210 , based on the current usage time of concrete, the current environmental data of concrete, the first abnormality information of each direct abnormality type, and the second abnormality information of each indirect abnormality type, abnormal maintenance information of each abnormal maintenance method of concrete is generated through an abnormal maintenance plan.

[0062] Step S211: The abnormal curing information of each abnormal curing method is used as the current curing data of the concrete.

[0063] Step S212: Obtain the concrete inspection and evaluation report, and identify the reporting areas corresponding to the various reporting data types in the inspection and evaluation report.

[0064] Step S213 : Based on the current curing data of the concrete and the current abnormal quality information of the concrete, the target reporting data content of each reporting data type is identified.

[0065] Step S214 , filling the target reporting data content of each reporting data type into the reporting area corresponding to each reporting data type, and obtaining the current detection and evaluation results of the water conservancy project quality.

[0066] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0067] Based on the same inventive concept, the embodiments of the present application also provide a dynamic detection and assessment device for water conservancy project quality for implementing the dynamic detection and assessment method for water conservancy project quality involved above. The implementation solution provided by this device is similar to the implementation solution described in the above method. Therefore, the specific limitations in the embodiments of one or more dynamic detection and assessment devices for water conservancy project quality provided below can be found in the limitations of the dynamic detection and assessment method for water conservancy project quality provided above, and will not be repeated here.

[0068] In an exemplary embodiment, Figure 3 As shown, a dynamic detection and evaluation device for water conservancy project quality is provided, including: an acquisition module 310, an identification module 320 and a generation module 330, wherein: An acquisition module 310 is configured to acquire current concrete freeze-thaw data collected by an intelligent freeze-thaw device, and identify freeze-thaw process data of the concrete based on the current concrete freeze-thaw data; an identification module 320 for identifying, based on the freeze-thaw process data of the concrete and using a data detection model, quality and safety data of each quality and safety assessment type of the concrete, and identifying current quality abnormality information of the concrete based on the quality and safety data of each quality and safety assessment type; The generation module 330 is used to generate the current maintenance data of the concrete based on the current quality abnormality information of the concrete, and generate the current detection and evaluation results of the water conservancy project quality based on the current maintenance data of the concrete and the current quality abnormality information of the concrete.

[0069] Optionally, the acquisition module 310 is specifically configured to: Splitting the current concrete freeze-thaw data into freezing process data and melting process data; identifying first parameter distribution information of each concrete parameter type in the freezing process data, and identifying second parameter distribution information of each concrete parameter type in the dissolving process data; The first parameter distribution information of each concrete parameter type and the second parameter distribution information of each concrete parameter type are used as freeze-thaw process data of the concrete.

[0070] Optionally, the identification module 320 is specifically configured to: Based on the first parameter distribution information of each concrete parameter type, identifying detection data of each first detection type of the concrete during the freezing process through a freezing process detection program; and based on the second parameter distribution information of each concrete parameter type, identifying detection data of each second detection type of the concrete during the dissolving process through a dissolving process detection program; Based on the detection data of each first detection type and the detection data of each second detection type, quality and safety data of each quality and safety assessment type of the concrete are identified through a quality and safety identification network.

[0071] Optionally, the identification module 320 is specifically configured to: Based on the quality safety data of each quality safety assessment type, querying the first abnormality information of the direct abnormality type of the concrete and the second abnormality information of the indirect abnormality type of the concrete through a quality abnormality database; The first abnormality information of each direct abnormality type and the second abnormality information of each indirect abnormality type are used as the current quality abnormality information of the concrete.

[0072] Optionally, the generating module 330 is specifically configured to: Obtaining the current usage time of the concrete and the current environmental data of the concrete, and querying an abnormality maintenance database based on each of the direct abnormality types and each of the indirect abnormality types to identify an abnormality maintenance plan corresponding to the concrete; generating abnormal curing information of each abnormal curing method of the concrete based on the current usage time of the concrete, the current environmental data of the concrete, the first abnormality information of each direct abnormality type, and the second abnormality information of each indirect abnormality type, through the abnormal curing plan; The abnormal curing information of each abnormal curing method is used as the current curing data of the concrete.

[0073] Optionally, the generating module 330 is specifically configured to: Obtaining a test and evaluation report of the concrete, and identifying a reporting area corresponding to each reporting data type in the test and evaluation report; Based on the current curing data of the concrete and the current quality abnormality information of the concrete, identifying the target reporting data content of each reporting data type; The target reporting data content of each reporting data type is filled into the reporting area corresponding to each reporting data type to obtain the current detection and evaluation results of the water conservancy project quality.

[0074] Each module in the above-mentioned dynamic detection and assessment device for water conservancy project quality can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0075] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 4 As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be implemented via Wi-Fi, a mobile cellular network, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a dynamic detection and assessment method for the quality of water conservancy projects. The display unit of the computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0076] Those skilled in the art will understand that Figure 4The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0077] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements steps corresponding to the dynamic detection and evaluation method for water conservancy project quality when executing the computer program.

[0078] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the computer program implements the steps corresponding to the dynamic detection and evaluation method of water conservancy project quality.

[0079] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements steps corresponding to the method for dynamic detection and evaluation of water conservancy project quality.

[0080] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0081] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0082] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0083] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A dynamic detection and evaluation method for water conservancy project quality, characterized in that: The method comprises: Acquire current concrete freeze-thaw data collected by the intelligent freeze-thaw device, and identify freeze-thaw process data of the concrete based on the current concrete freeze-thaw data; Based on the freeze-thaw process data of the concrete, identifying quality and safety data of each quality and safety assessment type of the concrete through a data detection model, and identifying current quality abnormality information of the concrete based on the quality and safety data of each quality and safety assessment type; Based on the current quality abnormality information of the concrete, the current maintenance data of the concrete is generated, and based on the current maintenance data of the concrete and the current quality abnormality information of the concrete, the current detection and evaluation result of the water conservancy project quality is generated.

2. The method according to claim 1, characterized in that The identifying freeze-thaw process data of concrete based on the current concrete freeze-thaw data includes: Splitting the current concrete freeze-thaw data into freezing process data and melting process data; identifying first parameter distribution information of each concrete parameter type in the freezing process data, and identifying second parameter distribution information of each concrete parameter type in the dissolving process data; The first parameter distribution information of each concrete parameter type and the second parameter distribution information of each concrete parameter type are used as freeze-thaw process data of the concrete.

3. The method according to claim 2, characterized in that The data detection model includes a freezing process detection program and a dissolution process detection program. Based on the freeze-thaw process data of the concrete, the data detection model is used to identify the quality and safety data of each quality and safety assessment type of the concrete, including: Based on the first parameter distribution information of each concrete parameter type, identifying detection data of each first detection type of the concrete during the freezing process through a freezing process detection program; and based on the second parameter distribution information of each concrete parameter type, identifying detection data of each second detection type of the concrete during the dissolving process through a dissolving process detection program; Based on the detection data of each first detection type and the detection data of each second detection type, quality and safety data of each quality and safety assessment type of the concrete are identified through a quality and safety identification network.

4. The method according to claim 1, wherein The identifying of current abnormal quality information of the concrete based on the quality safety data of each quality safety assessment type includes: Based on the quality safety data of each quality safety assessment type, querying the first abnormality information of the direct abnormality type of the concrete and the second abnormality information of the indirect abnormality type of the concrete through a quality abnormality database; The first abnormality information of each direct abnormality type and the second abnormality information of each indirect abnormality type are used as the current quality abnormality information of the concrete.

5. The method according to claim 4, characterized in that The generating of current curing data of the concrete based on the current quality abnormality information of the concrete includes: Obtaining the current usage time of the concrete and the current environmental data of the concrete, and querying an abnormality maintenance database based on each of the direct abnormality types and each of the indirect abnormality types to identify an abnormality maintenance plan corresponding to the concrete; generating abnormal curing information of each abnormal curing method of the concrete based on the current usage time of the concrete, the current environmental data of the concrete, the first abnormality information of each direct abnormality type, and the second abnormality information of each indirect abnormality type, through the abnormal curing plan; The abnormal curing information of each abnormal curing method is used as the current curing data of the concrete.

6. The method according to claim 1, characterized in that The generating of the current inspection and evaluation result of the water conservancy project quality based on the current maintenance data of the concrete and the current quality abnormality information of the concrete includes: Obtaining a test and evaluation report of the concrete, and identifying a reporting area corresponding to each reporting data type in the test and evaluation report; Based on the current curing data of the concrete and the current quality abnormality information of the concrete, identifying the target reporting data content of each reporting data type; The target reporting data content of each reporting data type is filled into the reporting area corresponding to each reporting data type to obtain the current detection and evaluation results of the water conservancy project quality.

7. A dynamic detection and evaluation device for water conservancy project quality, characterized in that: The device comprises: An acquisition module is used to acquire current concrete freeze-thaw data collected by the intelligent freeze-thaw device, and identify freeze-thaw process data of the concrete based on the current concrete freeze-thaw data; an identification module for identifying, based on the freeze-thaw process data of the concrete and using a data detection model, quality and safety data of each quality and safety assessment type of the concrete, and identifying current quality abnormality information of the concrete based on the quality and safety data of each quality and safety assessment type; A generation module is used to generate current maintenance data of the concrete based on the current quality abnormality information of the concrete, and to generate a current detection and evaluation result of the quality of the water conservancy project based on the current maintenance data of the concrete and the current quality abnormality information of the concrete.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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