Engineering building construction quality analysis system and method based on data processing

Abstract

The invention discloses an engineering building construction quality analysis system and method based on data processing, and relates to the technical field of construction quality analysis, the system comprises an index and model building unit, a path planning and data acquisition unit, a model training and optimization unit and a real-time evaluation and quality analysis unit; wherein the index and model construction unit is used for determining a quality detection index of a building main body and constructing a quality detection model; the path planning and data acquisition unit is used for optimizing a cruise path by using a guide search algorithm and controlling the unmanned aerial vehicle to acquire construction site data; the model training and optimizing unit is used for optimizing the quality detection model by utilizing a game chess algorithm; and the real-time evaluation and quality analysis unit is used for evaluating the acquired data in real time by using the optimized model and generating a construction quality analysis report. The game chess algorithm is combined with the unmanned aerial vehicle technology, and the speed and accuracy of processing complex construction data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of construction quality analysis, and in particular to a system and method for analyzing the quality of engineering construction based on data processing. Background Art

[0002] With the acceleration of urbanization and the development of the construction industry, the quality of construction has gradually become the focus of people's attention. Traditional construction quality analysis methods mainly include manual inspection and traditional equipment measurement. Manual inspection is the earliest construction quality monitoring method. It mainly relies on the experience and technical level of construction personnel to evaluate the quality of the main building through naked eye observation, touch, etc. This method is simple and intuitive, but it is difficult to establish a systematic quality indicator system, and the data collection and processing capabilities are limited; traditional equipment measurement uses various measuring equipment to monitor the quality of construction. Common equipment includes level, theodolite and rangefinder. These devices can measure the geometric dimensions and positional relationships of the main building, and provide more accurate data, but there are limitations in data integration and model construction, and it is difficult to achieve a comprehensive quality assessment model.

[0003] Although traditional construction quality monitoring methods can meet the needs of construction quality monitoring to a certain extent, they have problems such as low data accuracy, low efficiency, and poor real-time performance. The existence of these problems leads to inaccurate and untimely construction quality analysis, which easily misses the best time to deal with quality problems and increases the risk and cost of construction quality control. With the development of technology, although some technologies have begun to use robots and automated equipment for detection, these technologies are usually only applicable to specific environments or specific types of buildings. It is difficult to establish a universal quality indicator system and evaluation model, and cannot meet the complexity and diversity of construction sites. At the same time, existing technologies still have shortcomings in large-scale data processing, especially when faced with multi-dimensional and massive quality data. There are often problems of slow processing speed and low model accuracy, making it difficult to achieve efficient and accurate quality analysis.

[0004] In addition, the limited applicability of existing technologies also limits their scope of application on construction sites. Due to the differences in different construction site environments and requirements, traditional quality monitoring technologies are difficult to adapt to diverse quality indicator requirements, complex on-site data collection, and variable construction quality model construction. Therefore, there is an urgent need to explore and research new technical means and methods to flexibly define quality indicators, efficiently collect and process on-site data, and accurately construct and optimize quality assessment models, thereby improving the data processing efficiency and accuracy of construction quality monitoring and providing more reliable data support for ensuring the quality of construction.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In response to the problems in the related technology, the present invention proposes a system and method for engineering construction quality analysis based on data processing, which has the advantages of greatly improving the efficiency and quality of data collection, and optimizing the quality inspection model through game chess algorithms, thereby improving the speed and accuracy of the model in processing complex data, thereby solving the shortcomings of the existing technology in large-scale construction data processing, especially when faced with multi-dimensional and massive quality data, often resulting in slow processing speed and low model accuracy.

[0007] To this end, the specific technical solution adopted by the present invention is as follows:

[0008] According to one aspect of the present invention, there is provided an engineering construction quality analysis system based on data processing, the engineering construction quality analysis system based on data processing comprising an indicator and model building unit, a path planning and data collection unit, a model training and optimization unit, and a real-time evaluation and quality analysis unit;

[0009] Among them, the indicator and model building unit is used to determine the quality inspection indicators of the main building based on the building design plan and build a quality inspection model;

[0010] The path planning and data collection unit is used to plan the UAV cruising path according to the determined indicators, optimize the cruising path using the guided search algorithm, and control the UAV to collect construction site data;

[0011] Model training and optimization unit, used to train the quality detection model based on the collected data and optimize the quality detection model using chess-like algorithms;

[0012] The real-time evaluation and quality analysis unit is used to evaluate the construction site data collected by the drone in real time using the optimized model, and generate a construction quality analysis report based on the evaluation results.

[0013] Furthermore, the indicator and model building unit includes an architectural design scheme analysis module, a quality inspection indicator setting module and a quality inspection model building module;

[0014] Among them, the building design scheme analysis module is used to review the building construction drawings and design instructions, and analyze the quality control points and standards of the building body;

[0015] The quality inspection index setting module is used to determine the quality inspection index of the main building based on the analysis results;

[0016] The quality inspection model building module is used to build a quality inspection model according to the determined quality inspection indicators.

[0017] Furthermore, the path planning and data collection unit includes a cruise path planning module, an algorithm path optimization module and a construction site data collection module;

[0018] Among them, the cruise path planning module is used to identify the key monitoring areas in the construction site according to the determined indicators and plan the drone cruise path;

[0019] Algorithmic path optimization module, used to optimize the cruise path using guided search algorithm;

[0020] The construction site data acquisition module is used to control the flight of the drone and collect image and video data of the construction site.

[0021] Furthermore, the cruise path planning module includes a key monitoring area identification module, a flight parameter setting module and a cruise path establishment module;

[0022] Among them, the key monitoring area identification module is used to identify the key monitoring areas within the construction site according to the quality inspection indicators;

[0023] The flight parameter setting module is used to set the flight parameters of the drone and calculate the minimum flight distance to connect to the key monitoring area;

[0024] The cruise path establishment module is used to connect the identified key monitoring areas in sequence according to the calculation results to obtain the UAV cruise path.

[0025] Furthermore, the algorithm path optimization module includes an efficiency evaluation module, a strategic decision matrix module, a traversal record module and a path correction module;

[0026] Among them, the efficiency evaluation module is used to define the scoring criteria and evaluate the efficiency of the drone's cruising path;

[0027] The strategic decision matrix module is used to construct a strategic decision matrix for key monitoring areas according to the defined scoring criteria. The strategic decision matrix includes a main decision point and several sub-decision points. Each decision point represents a flight path of the drone. The main decision point represents the starting position of the drone, and each sub-decision point represents the path selection from the current position to the next key monitoring area.

[0028] The traversal record module is used to traverse the strategic decision matrix of the key monitoring area and record the scoring standard score of each node in the traversed strategic decision matrix;

[0029] The path correction module is used to select the decision point with the highest scoring standard as the correction direction to correct the UAV's cruising path.

[0030] Furthermore, the model training and optimization unit includes a training set construction module, a model training module, and a model optimization module;

[0031] Among them, the training set construction module is used to annotate the collected image and video data and construct the quality detection model training set;

[0032] Model training module, used to train the quality detection model based on the constructed training set;

[0033] The model optimization module is used to optimize the trained quality detection model using game-based algorithms.

[0034] Furthermore, the model optimization module includes an evaluation function module, a redundant node removal module, and an analysis strategy optimization module;

[0035] Among them, the evaluation function module is used to determine the optimization target and design a static evaluation function;

[0036] A redundant node removal module is used to remove redundant nodes in the quality detection model analysis strategy using a static evaluation function;

[0037] The analysis strategy optimization module is used to define scoring rules, select scoring nodes, and adjust the analysis strategy of the quality detection model based on the scoring results.

[0038] Furthermore, the redundant node removal module includes a node evaluation module, a node marking module and a node optimization module;

[0039] Among them, the node evaluation module is used to set the node threshold and use the static evaluation function to evaluate each analysis node in the quality detection model analysis strategy;

[0040] A node marking module is used to mark redundant nodes in the analysis node according to the evaluation results;

[0041] The node optimization module is used to remove redundant nodes and redistribute the computing power of the quality inspection model based on the remaining analysis nodes.

[0042] Furthermore, the analysis strategy optimization module includes a scoring rule module, a scoring node module and a strategy selection module;

[0043] Among them, the scoring rule module defines the scoring rules and evaluates the scores of the quality detection model analysis strategy;

[0044] The scoring node module is used to compare the scores of each analysis strategy and select the scoring nodes and the losing nodes;

[0045] The strategy selection module is used to select the analysis strategy with the highest score for each scoring node and the analysis strategy with the lowest score for each losing node.

[0046] According to another aspect of the present invention, a method for analyzing the quality of engineering and construction based on data processing is also provided. The method for analyzing the quality of engineering and construction based on data processing comprises the following steps:

[0047] S1. Determine the quality inspection index of the main building based on the architectural design plan and build a quality inspection model;

[0048] S2. Plan the UAV cruising path according to the determined indicators, optimize the cruising path using the guided search algorithm, and control the UAV to collect construction site data;

[0049] S3. Train the quality detection model based on the collected data, and use the game algorithm to optimize the quality detection model;

[0050] S4. Use the optimized model to evaluate the construction site data collected by the drone in real time, and generate a construction quality analysis report based on the evaluation results.

[0051] The beneficial effects of the present invention are:

[0052] (1) The present invention uses a game chess algorithm combined with drone technology and a guided search algorithm to analyze the construction quality of the main building on the construction site, thereby achieving high-efficiency and high-precision construction quality monitoring; based on the building design plan, key quality inspection indicators are determined and an initial quality inspection model is constructed, which lays a foundation for data collection and analysis. At the same time, by utilizing the high maneuverability of drones, the system can perform wide-area coverage and precise collection of details at the construction site, greatly improving the efficiency and quality of data collection; in addition, the quality inspection model is optimized through a game chess algorithm, which improves the speed and accuracy of the model in processing complex data and reduces the errors caused by manual judgment, thereby significantly improving the automation and intelligence level of building construction quality inspection and providing reliable technical support for construction quality control.

[0053] (2) Through the high-quality data collected in real time by drones and the optimized quality inspection model, the present invention can quickly perform data preprocessing and feature extraction, realize real-time monitoring and evaluation of the construction process, and enable the construction team to obtain feedback on key quality indicators in a timely manner, shortening the time from problem discovery to problem resolution, thereby reducing the risks and costs brought by potential defects; at the same time, the construction quality analysis report generated by the system is based on a large amount of real-time data and precise model analysis, enabling project managers to clearly grasp the overall status of construction quality and ensure the effective implementation of quality control measures; in addition, the application of game chess algorithms in model optimization improves the accuracy of data analysis and reduces the rework and maintenance costs caused by quality problems.

[0054] (3) The implementation of the present invention has further promoted the digital transformation of the construction industry. By introducing advanced drone technology and intelligent algorithms, a complete data-driven quality management system has been established. From the determination of quality inspection indicators, intelligent planning of data collection, to continuous optimization of models and real-time data analysis, the efficiency and accuracy of construction quality monitoring have been significantly improved, providing strong technical support for the sustainable development of the industry. At the same time, the promotion and application of the present invention will also drive the development of related industrial chains, such as drone manufacturing, artificial intelligence algorithm development, big data analysis, etc., thereby creating more employment opportunities and economic benefits, and promoting the entire construction industry to develop in a more intelligent and data-based direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 is a principle block diagram of a system for analyzing the quality of engineering construction based on data processing according to an embodiment of the present invention;

[0057] Figure 2 It is a flowchart of a method for analyzing construction quality of an engineering building based on data processing according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0059] According to an embodiment of the present invention, a system and method for analyzing the quality of engineering construction based on data processing are provided.

[0060] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a system for analyzing the quality of engineering and construction based on data processing is provided, and the system for analyzing the quality of engineering and construction based on data processing includes an indicator and model building unit 1, a path planning and data collection unit 2, a model training and optimization unit 3, and a real-time evaluation and quality analysis unit 4;

[0061] The indicator and model building unit 1 is used to determine the building main body quality inspection indicators based on the building design plan and build a quality inspection model;

[0062] The path planning and data collection unit 2 is used to plan the UAV cruising path according to the determined indicators, optimize the cruising path using the guided search algorithm, and control the UAV to collect construction site data;

[0063] Model training and optimization unit 3, used for training the quality detection model based on the collected data, and optimizing the quality detection model using a game algorithm;

[0064] The real-time evaluation and quality analysis unit 4 is used to use the optimized model to evaluate the construction site data collected by the drone in real time, and generate a construction quality analysis report based on the evaluation results.

[0065] Specifically, the real-time evaluation and quality analysis unit 4 includes a data preprocessing module 401, a comparison evaluation module 402 and a quality analysis report module 403;

[0066] The data preprocessing module 401 is used to preprocess the construction site data collected by the drone and extract features related to the quality inspection indicators;

[0067] A comparison and evaluation module 402 is used to compare the preset quality detection indicators with the extracted features, and use the optimized quality detection model to evaluate the comparison differences in real time;

[0068] The quality analysis report module 403 is used to generate a construction quality analysis report based on the evaluation results.

[0069] Specifically, in the data preprocessing module 401, the construction site data collected by the drone is first cleaned to remove noise and irrelevant information and retain valid data related to quality inspection indicators. Subsequently, the data is standardized to ensure data consistency and comparability. Next, feature extraction techniques, such as principal component analysis (PCA), are used to extract key features from the data to provide accurate input for subsequent quality assessment.

[0070] Specifically, the comparison and evaluation module 402 receives the pre-processed data features, matches them with the preset quality inspection indicators, and uses the quality inspection model optimized by the game chess algorithm to evaluate these features, identify the differences from the standard indicators, and thus discover potential construction quality problems.

[0071] Specifically, finally, the quality analysis report module 403 compiles and generates a detailed construction quality analysis report based on the evaluation results. The report not only contains detailed data of the quality evaluation, but also intuitively displays the problems in the form of charts, etc., so that project managers can quickly understand and take corresponding measures. The entire report generation process is automated, which improves efficiency and ensures the real-time and accuracy of quality monitoring. In this way, the engineering construction quality analysis system based on data processing can provide efficient and accurate quality monitoring solutions for the construction site.

[0072] In one embodiment, the indicator and model building unit 1 includes a building design solution analysis module 101, a quality inspection indicator setting module 102 and a quality inspection model building module 103;

[0073] The building design scheme analysis module 101 is used to review the building construction drawings and design instructions, and analyze the quality control points and standards of the building body;

[0074] The quality inspection index setting module 102 is used to determine the quality inspection index of the building body based on the analysis results;

[0075] The quality detection model building module 103 is used to build a quality detection model according to the determined quality detection indicators.

[0076] Specifically, review and analyze the construction drawings and design instructions, extract key information, including structural layout, material specifications and engineering standards, and use the data provided by the building design solution analysis module 101 to determine the quality inspection indicators of the main building. These indicators include concrete crack width, wall verticality deviation and component size tolerance. Finally, according to the determined quality inspection indicators, a preliminary quality inspection model is constructed based on the neural network model.

[0077] In one embodiment, the path planning and data collection unit 2 includes a cruise path planning module 201, an algorithm path optimization module 202, and a construction site data collection module 203;

[0078] Among them, the cruise path planning module 201 is used to identify the key monitoring area in the construction site according to the determined indicators and plan the cruise path of the drone;

[0079] An algorithmic path optimization module 202, for optimizing a cruise path using a guided search algorithm;

[0080] The construction site data acquisition module 203 is used to control the flight of the UAV and collect image and video data of the construction site.

[0081] In one embodiment, the cruise path planning module 201 includes a key monitoring area identification module 2011, a flight parameter setting module 2012 and a cruise path establishment module 2013;

[0082] Among them, the key monitoring area identification module 2011 is used to identify the key monitoring area in the construction site according to the quality inspection indicators;

[0083] The flight parameter setting module 2012 is used to set the flight parameters of the UAV and calculate the minimum flight distance to connect to the key monitoring area;

[0084] The cruise path establishing module 2013 is used to connect the marked key monitoring areas in sequence according to the calculation results to obtain the cruise path of the drone.

[0085] Specifically, according to the quality inspection indicators, including concrete cracks, wall verticality and component size, the key monitoring areas within the construction site are identified. These areas are the focus of drone cruising and data collection. Then the flight parameters of the drone are set, including flight altitude, speed and shooting angle. Based on these parameters, the minimum flight distance connecting the key monitoring areas is calculated to optimize coverage efficiency and data quality. Then, based on the calculation results, the preliminary cruising path of the drone is designed to connect all the identified key monitoring areas.

[0086] In one embodiment, the algorithm path optimization module 202 includes an efficiency evaluation module 2021, a strategic decision matrix module 2022, a traversal record module 2023 and a path correction module 2024;

[0087] Among them, the efficiency evaluation module 2021 is used to define the scoring criteria and evaluate the efficiency of the drone cruising path;

[0088] The strategic decision matrix module 222 is used to construct a strategic decision matrix for key monitoring areas according to the defined scoring criteria. The strategic decision matrix includes a main decision point and several sub-decision points. Each decision point represents a flight path of the UAV. The main decision point represents the starting position of the UAV, and each sub-decision point represents the path selection from the current position to the next key monitoring area.

[0089] The traversal recording module 223 is used to traverse the strategic decision matrix of the key monitoring area and record the scoring standard score of each node in the traversed strategic decision matrix;

[0090] The path correction module 2024 is used to select the decision point with the highest scoring standard score as the correction direction to correct the UAV cruising path.

[0091] Specifically, the guided search algorithm is a heuristic search algorithm of the Gobang algorithm. First, heuristic scoring criteria are defined to evaluate the efficiency of the UAV's cruise path. These criteria include path length, completeness of the coverage area, and quality of data collection. Then, a strategic decision matrix similar to Gobang is constructed, in which each decision point represents a possible flight path of the UAV. In this strategic decision matrix, the main decision point represents the starting position of the UAV, and each secondary decision point represents the path selection from the current position to the next key monitoring area. By traversing the strategic decision matrix, the score of each node according to the defined scoring criteria is recorded. In this process, the heuristic search strategy of the Gobang algorithm is used to guide the search process, giving priority to paths with higher scores, and selecting the decision point with the highest score as the correction direction of the UAV's cruise path, thereby evaluating the scores of each path and selecting the path that maximizes coverage efficiency and data quality.

[0092] In one embodiment, the model training and optimization unit 3 includes a training set construction module 301, a model training module 302 and a model optimization module 303;

[0093] The training set construction module 301 is used to annotate the collected image and video data and construct a quality detection model training set;

[0094] A model training module 302 is used to train a quality detection model based on the constructed training set;

[0095] The model optimization module 303 is used to optimize the trained quality detection model using a game algorithm.

[0096] Specifically, the image and video data collected by the drone are annotated in detail, which includes identifying and marking the specific location, type and severity of the quality problems. These annotated data are then used to construct a training set for a quality inspection model, ensuring that the data set contains sufficiently diverse examples to train the model to identify various quality problems. The quality inspection model is then trained using the supervised learning method using the training set constructed above, and the model is trained for sufficient iterations until the model can accurately identify and classify construction quality problems.

[0097] Specifically, the game algorithm is a Gobang algorithm. By determining the optimization goals, including improving the accuracy of the model, reducing the false positive rate and improving the processing speed, a static evaluation function is designed to evaluate each decision node in the model analysis strategy: first, the various states of the model decision are defined, similar to each chessboard layout in Gobang. Each state represents the decision of the quality inspection model under specific circumstances. Then a decision tree is constructed, in which each node corresponds to a possible decision of the model under a specific state, similar to the choice of each move in Gobang. Then the α-β pruning strategy of Gobang is introduced. In the decision tree search process of the model, α-β pruning is used to eliminate those paths that are unlikely to produce the optimal decision, that is, redundant nodes, which can effectively reduce the search space and improve the efficiency of model training and decision-making. An iterative deepening search strategy is implemented to find the optimal decision path within the current model capability by gradually increasing the search depth.

[0098] In one embodiment, the model optimization module 303 includes an evaluation function module 3031, a redundant node removal module 3032, and an analysis strategy optimization module 3033;

[0099] The evaluation function module 3031 is used to determine the optimization target and design a static evaluation function;

[0100] Specifically, first, the evaluation function module 3031 determines the optimization target by analyzing the needs of construction quality detection. These targets may include improving the accuracy of detection, reducing calculation time, reducing false alarm rate, etc. For example, the optimization target is set to minimize the detection error while keeping the detection speed within an acceptable range.

[0101] Specifically, then, the evaluation function module 3031 designs a static evaluation function, which combines multiple factors, such as detection accuracy, detection speed, resource consumption, etc. The evaluation function code in this embodiment is as follows:

[0102] defstatic_evaluation_function(accuracy,speed,resource_usage):

[0103] #Define weights, these weights can be adjusted according to actual conditions

[0104] accuracy_weight = 0.6;

[0105] speed_weight=0.3;

[0106] resource_usage_weight=0.1;

[0107] #Calculate the comprehensive score

[0108] score=(accuracy*accuracy_weight)+(speed*speed_weight)-(resource_usage*resource_usage_weight);

[0109] return score.

[0110] Specifically, in the above embodiment, accuracy, speed and resource_usage represent the accuracy, detection speed and resource consumption of the quality detection model respectively. The weight factors accuracy_weight, speed_weight and resource_usage_weight are used to adjust the importance of different factors in the evaluation function.

[0111] Specifically, the evaluation function evaluates the effects of different analysis strategies through weighted comprehensive scores. If a strategy performs better in accuracy but slower in speed, its comprehensive score will reflect this trade-off. In this way, the evaluation function module 3031 can provide a basis for subsequent redundant node removal and analysis strategy optimization.

[0112] The redundant node removal module 3032 is used to remove the redundant nodes in the quality detection model analysis strategy by using the static evaluation function;

[0113] The analysis strategy optimization module 3033 is used to define scoring rules, select scoring nodes, and adjust the analysis strategy of the quality detection model based on the scoring results.

[0114] In one embodiment, the redundant node removal module 3032 includes a node evaluation module 30321, a node marking module 30322, and a node optimization module 30323;

[0115] Among them, the node evaluation module 30321 is used to set the node threshold and use the static evaluation function to evaluate each analysis node in the quality detection model analysis strategy;

[0116] A node marking module 30322 is used to mark redundant nodes in the analysis node according to the evaluation result;

[0117] The node optimization module 30323 is used to remove redundant nodes and reallocate the computing power of the quality detection model based on the remaining analysis nodes.

[0118] Specifically, each analysis node in the quality detection model is first evaluated by the node evaluation module 30321, and the score of each node is calculated using a static evaluation function. These scores reflect the comprehensive performance of the node in terms of accuracy, speed, and resource consumption. Once the evaluation criteria are determined, such as setting a score threshold, the node evaluation module will identify those nodes whose scores are below the threshold.

[0119] Specifically, the node marking module 30322 then marks these low-scoring nodes as redundant based on the evaluation results. These redundant nodes may be because they contribute less to the model performance or because they have functional duplication with other nodes.

[0120] Specifically, finally, the node optimization module 30323 takes action to remove these redundant nodes from the model and reallocate computing resources to the remaining valid nodes. Such optimization not only reduces the complexity of the model, but also may improve the operating efficiency and detection accuracy of the model. In this way, the engineering construction quality analysis system based on data processing can ensure that the quality detection model avoids unnecessary computing overhead while maintaining performance, thereby improving the efficiency and reliability of the entire system.

[0121] In one embodiment, the analysis strategy optimization module 3033 includes a scoring rule module 30331, a scoring node module 30332, and a strategy selection module 30333;

[0122] Among them, the scoring rule module 30331 defines the scoring rules and evaluates the score of the quality detection model analysis strategy;

[0123] The scoring node module 30332 is used to compare the scores of each analysis strategy and select the scoring node and the losing node;

[0124] The strategy selection module 30333 is used to select the analysis strategy with the highest score for each scoring node, and select the analysis strategy with the lowest score for each losing node.

[0125] Specifically, first, a set of comprehensive scoring rules is defined through the scoring rule module 30331 to evaluate various analysis strategies of the quality detection model. These rules may include multiple dimensions such as detection accuracy, response time, resource utilization efficiency, etc. In this embodiment, a comprehensive scoring formula is set, in which the accuracy accounts for 60% of the weight, the response time accounts for 30% of the weight, and the resource utilization efficiency accounts for 10% of the weight. Such scoring rules can fully reflect the performance of each analysis strategy in actual applications.

[0126] Specifically, next, the scoring node module 30332 uses this set of scoring rules to score all analysis strategies in the quality detection model. By comparing the scores of different strategies, the module can identify "scoring nodes" with excellent performance and "losing nodes" with poor performance. Scoring nodes represent strategies that are particularly effective in the current construction quality analysis task, while losing nodes refer to those strategies that are ineffective or inefficient.

[0127] Specifically, finally, based on the above scoring results, the strategy selection module 30333 selects the highest scoring analysis strategy for each scoring node to give full play to its advantages. At the same time, for the nodes that lose points, the module will select the lowest scoring analysis strategy to minimize their negative impact on the overall performance while retaining the functions of these nodes. This differentiated strategy selection method enables the system to maximize overall performance while maintaining comprehensiveness.

[0128] Specifically, in this way, the analysis strategy optimization module 3033 can dynamically adjust the analysis strategy of the quality inspection model to better adapt it to different construction environments and quality inspection requirements. This not only improves the adaptability and efficiency of the engineering construction quality analysis system based on data processing, but also ensures that the system can continue to provide accurate and timely quality analysis results in a complex and changing construction environment.

[0129] like Figure 2 As shown, according to another embodiment of the present invention, a method for analyzing the quality of engineering and building construction based on data processing is also provided. The method for analyzing the quality of engineering and building construction based on data processing comprises the following steps:

[0130] S1. Determine the quality inspection index of the main building based on the architectural design plan and build a quality inspection model;

[0131] S2. Plan the UAV cruising path according to the determined indicators, optimize the cruising path using the guided search algorithm, and control the UAV to collect construction site data;

[0132] S3. Train the quality detection model based on the collected data, and use the game algorithm to optimize the quality detection model;

[0133] S4. Use the optimized model to evaluate the construction site data collected by the drone in real time, and generate a construction quality analysis report based on the evaluation results.

[0134] In order to facilitate understanding of the above technical solutions of the present invention, the working principle or operation mode of the present invention in the actual process is described in detail below.

[0135] In actual application, taking the construction site of a high-rise building project in City A as an example, the construction quality analysis system based on drones proposed in the present invention is applied in construction quality analysis: first, according to the architectural design plan, the system first determines key quality inspection indicators such as concrete cracks, wall verticality and component size, and then, based on these indicators, the system constructs a quality inspection model; then, based on the quality inspection indicators, a specific cruising path is planned for the drone, and by setting parameters such as the drone's flight altitude, speed and shooting angle, the system calculates the minimum flight distance connecting key monitoring areas, thereby forming an efficient and accurate cruising path. The drone flies along the planned path and collects image and video data of the construction site in real time, which are then used to train and optimize the quality inspection model; Then, by annotating the collected data, the system constructed a training set and optimized the model using game chess algorithms, improving the speed and accuracy of the model in processing complex data; finally, the system used the optimized model to evaluate the construction site data collected by the drone in real time. By comparing the actual data with the predicted results of the quality inspection model, the system was able to quickly discover potential quality problems. Based on the evaluation results, the system generated a detailed construction quality analysis report, providing the construction team with timely and accurate quality feedback, thereby greatly improving the efficiency and accuracy of data collection, and reducing errors through model optimization, realizing real-time monitoring of construction quality, which not only shortened the time from problem discovery to solution, reduced the risks and costs brought by potential defects, but also provided reliable technical support for construction quality control.

[0136] In summary, with the help of the above-mentioned technical scheme of the present invention, the present invention analyzes the construction quality of the main building on the construction site by adopting a game chess algorithm combined with drone technology and a guided search algorithm, thereby realizing high-efficiency and high-precision construction quality monitoring; based on the architectural design plan, key quality inspection indicators are determined, and an initial quality inspection model is constructed, which lays a foundation for data collection and analysis. At the same time, by utilizing the high maneuverability of drones, the system can perform wide-area coverage and precise collection of details at the construction site, greatly improving the efficiency and quality of data collection; in addition, the quality inspection model is optimized by using a game chess algorithm, which improves the speed and accuracy of the model in processing complex data, reduces the errors caused by manual judgment, thereby significantly improving the automation and intelligence level of construction quality inspection, and providing reliable technical support for construction quality control; through the high-quality data collected in real time by drones and the optimized quality inspection model, the present invention can quickly perform data preprocessing and feature extraction, and achieve With the real-time monitoring and evaluation of the construction process, the construction team can obtain timely feedback on key quality indicators, shortening the time from problem discovery to solution, thereby reducing the risks and costs brought by potential defects; at the same time, the construction quality analysis report generated by the system is based on a large amount of real-time data and precise model analysis, so that project managers can clearly grasp the overall situation of construction quality and ensure the effective implementation of quality control measures; in addition, the application of game algorithms in model optimization improves the accuracy of data analysis and reduces the rework and maintenance costs caused by quality problems; the implementation of the present invention further promotes the digital transformation of the construction industry. By introducing advanced drone technology and intelligent algorithms, a complete data-driven quality management system has been established. From the determination of quality inspection indicators, intelligent planning of data collection, to continuous optimization of models and real-time data analysis, the efficiency and accuracy of construction quality monitoring have been significantly improved, providing strong data support for the sustainable development of the industry.

[0137] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. The engineering construction quality analysis system based on data processing is characterized by: The engineering construction quality analysis system based on data processing comprises an index and model building unit (1), a path planning and data collection unit (2), a model training and optimization unit (3) and a real-time evaluation and quality analysis unit (4); The indicator and model building unit (1) is used to determine the building main body quality inspection indicators based on the building design plan and build a quality inspection model; The path planning and data collection unit (2) is used to plan the cruising path of the drone according to the determined indicators, optimize the cruising path using a guided search algorithm, and control the drone to collect construction site data; The model training and optimization unit (3) is used to train the quality detection model based on the collected data and optimize the quality detection model using a game algorithm; The real-time evaluation and quality analysis unit (4) is used to use the optimized model to evaluate the construction site data collected by the drone in real time, and generate a construction quality analysis report based on the evaluation results.

2. The engineering construction quality analysis system based on data processing according to claim 1 is characterized in that: The index and model building unit (1) comprises a building design scheme analysis module (101), a quality inspection index setting module (102) and a quality inspection model building module (103); The building design scheme analysis module (101) is used to review the building construction drawings and design instructions, and analyze the quality control points and standards of the building body; The quality inspection index setting module (102) is used to determine the quality inspection index of the building body based on the analysis result; The quality detection model building module (103) is used to build a quality detection model according to the determined quality detection index.

3. The engineering construction quality analysis system based on data processing according to claim 1 is characterized in that: The path planning and data collection unit (2) comprises a cruise path planning module (201), an algorithm path optimization module (202) and a construction site data collection module (203); The cruise path planning module (201) is used to identify the key monitoring area in the construction site according to the determined indicators and plan the cruise path of the drone; The algorithm path optimization module (202) is used to optimize the cruise path using a guided search algorithm; The construction site data acquisition module (203) is used to control the flight of the drone and to collect image and video data of the construction site.

4. The engineering construction quality analysis system based on data processing according to claim 3 is characterized in that: The cruise path planning module (201) comprises a key monitoring area identification module (2011), a flight parameter setting module (2012) and a cruise path establishment module (2013); Wherein, the key monitoring area identification module (2011) is used to identify the key monitoring area in the construction site according to the quality inspection index; The flight parameter setting module (2012) is used to set the flight parameters of the UAV and calculate the minimum flight distance to connect the key monitoring area; The cruise path establishing module (2013) is used to connect the identified key monitoring areas in sequence according to the calculation results to obtain the cruise path of the drone.

5. The engineering construction quality analysis system based on data processing according to claim 4 is characterized in that: The algorithm path optimization module (202) includes an efficiency evaluation module (2021), a strategic decision matrix module (2022), a traversal record module (2023) and a path correction module (2024); The efficiency evaluation module (2021) is used to define a scoring standard and evaluate the efficiency of the UAV cruising path; The strategic decision matrix module (2022) is used to construct a strategic decision matrix for key monitoring areas according to a defined scoring standard, wherein the strategic decision matrix includes a main decision point and a plurality of sub-decision points, each decision point representing a flight path of a UAV, the main decision point representing the starting position of the UAV, and each sub-decision point representing a path selection from the current position to the next key monitoring area; The traversal recording module (223) is used to traverse the strategic decision matrix of the key monitoring area and record the scoring standard score of each node in the traversed strategic decision matrix; The path correction module (2024) is used to select the decision point with the highest scoring standard score as the correction direction to correct the UAV cruising path.

6. The engineering construction quality analysis system based on data processing according to claim 1 is characterized in that: The model training and optimization unit (3) comprises a training set construction module (301), a model training module (302) and a model optimization module (303); The training set construction module (301) is used to annotate the collected image and video data and construct a quality detection model training set; The model training module (302) is used to train the quality detection model based on the constructed training set; The model optimization module (303) is used to optimize the trained quality detection model using a game algorithm.

7. The engineering construction quality analysis system based on data processing according to claim 6 is characterized in that: The model optimization module (303) includes an evaluation function module (3031), a redundant node removal module (3032) and an analysis strategy optimization module (3033); Wherein, the evaluation function module (3031) is used to determine the optimization target and design a static evaluation function; The redundant node removal module (3032) is used to remove redundant nodes in the quality detection model analysis strategy using a static evaluation function; The analysis strategy optimization module (3033) is used to define scoring rules, select scoring nodes, and adjust the analysis strategy of the quality detection model based on the scoring results.

8. The engineering construction quality analysis system based on data processing according to claim 7 is characterized in that: The redundant node removal module (3032) includes a node evaluation module (30321), a node marking module (30322) and a node optimization module (30323); Wherein, the node evaluation module (30321) is used to set the node threshold and evaluate each analysis node in the quality detection model analysis strategy using a static evaluation function; The node marking module (30322) is used to mark redundant nodes in the analysis node according to the evaluation result; The node optimization module (30323) is used to remove redundant nodes and reallocate the computing power of the quality detection model based on the remaining analysis nodes.

9. The engineering construction quality analysis system based on data processing according to claim 8 is characterized in that: The analysis strategy optimization module (3033) includes a scoring rule module (30331), a scoring node module (30332) and a strategy selection module (30333); Wherein, the scoring rule module (30331) defines the scoring rule to evaluate the score of the quality detection model analysis strategy; The scoring node module (30332) is used to compare the scores of each analysis strategy and select scoring nodes and losing nodes; The strategy selection module (30333) is used to select the analysis strategy with the highest score for each scoring node, and select the analysis strategy with the lowest score for each losing node.

10. A method for analyzing the quality of engineering and construction based on data processing, used to implement a system for analyzing the quality of engineering and construction based on data processing as claimed in any one of claims 1 to 9, characterized in that: The engineering construction quality analysis method based on data processing comprises the following steps: S1. Determine the quality inspection index of the main building based on the architectural design plan and build a quality inspection model; S2. Plan the UAV cruising path according to the determined indicators, optimize the cruising path using the guided search algorithm, and control the UAV to collect construction site data; S3. Train the quality detection model based on the collected data, and use the game algorithm to optimize the quality detection model; S4. Use the optimized model to evaluate the construction site data collected by the drone in real time, and generate a construction quality analysis report based on the evaluation results.

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