Intelligent construction decision-making method and device based on meteorological data and medium

Abstract

The invention discloses an intelligent construction decision-making method and equipment based on meteorological data and a medium, belongs to the technical field of building engineering management technologies, and aims to solve the problems that an existing construction project lacks an effective method which can comprehensively consider various meteorological parameters and automatically make a construction decision according to the parameters, the improvement of construction efficiency is not facilitated, and the construction efficiency is poor. And safety accidents caused by weather reasons are easily caused. The method comprises the following steps: carrying out multi-source data integration on field acquisition meteorological data and professional meteorological service data of a construction area; performing time synchronization and data standardization processing on the multi-source meteorological data; carrying out influence evaluation of related meteorological parameters on each construction work type; carrying out the training processing of an influence relation model between the multi-source meteorological data and the construction work types, and constructing a meteorological-construction influence evaluation model; carrying out comprehensive evaluation processing on the current construction operation; and automatically evaluating the comprehensive influence index of the current construction operation to obtain a construction guidance strategy.

Description

Technical Field

[0001] This application relates to the technical field of construction project management, and particularly to an intelligent construction decision-making method, device, and medium based on meteorological data. Background Art

[0002] In construction project management, weather conditions have a crucial impact on construction progress and quality. However, most current construction projects lack an effective tool that can comprehensively consider multiple meteorological parameters and automatically make construction decisions based on them. Traditional weather forecasts only provide basic meteorological information and cannot conduct a detailed assessment of the weather impact for specific construction operation types. In addition, the complexity and variability of the construction site require more refined management methods to address construction challenges under different weather conditions. For example, under harsh weather conditions such as high temperature, high humidity, or strong wind, certain construction operations (such as concrete pouring and high-altitude operations) may need to be suspended or the plan adjusted to ensure safety and quality.

[0003] Therefore, there is an urgent need for an intelligent construction decision-making support system based on real-time meteorological data, which can accurately evaluate the specific impact of different weather conditions on various construction operations and automatically generate corresponding construction suggestions or warning messages, thereby improving construction efficiency and reducing delays and safety accidents caused by weather reasons. The aim is to provide an efficient, safe, and easy-to-implement construction management solution for the construction industry. Summary of the Invention

[0004] Embodiments of this application provide an intelligent construction decision-making method, device, and medium based on meteorological data to solve the following technical problems: Existing construction projects lack an effective method that can comprehensively consider multiple meteorological parameters and automatically make construction decisions based on them, which is not conducive to improving construction efficiency and is likely to cause safety accidents due to weather reasons.

[0005] Embodiments of this application adopt the following technical solutions:

[0006] On the one hand, an embodiment of the present application provides an intelligent construction decision-making method based on meteorological data, including: integrating multi-source data of on-site collected meteorological data in the construction area and professional meteorological service data to obtain multi-source meteorological data; performing time synchronization and data standardization processing on the multi-source meteorological data to obtain high-quality meteorological data; evaluating the influence of relevant meteorological parameters for each construction operation type to obtain influence level parameters; based on the influence level parameters, performing training processing on the influence relationship model between the multi-source meteorological data and the construction operation types to construct a meteorology-construction influence evaluation model; through the meteorology-construction influence evaluation model, performing comprehensive evaluation processing on the current construction operation to obtain a comprehensive influence index; through a preset construction decision support system, automatically evaluating the comprehensive influence index of the current construction operation to obtain a construction guidance strategy.

[0007] Through real-time synchronization and standardization processing of meteorological data, the embodiment of the present application can more accurately predict and evaluate the impact of meteorological conditions on construction, so as to reasonably arrange the construction plan and avoid construction delays caused by bad weather. By evaluating the impact level of different meteorological parameters on construction, potential risks can be identified in advance, and corresponding preventive measures can be taken to reduce construction accidents and losses caused by weather changes. The intelligent decision-making system can reasonably allocate human resources and construction materials according to meteorological data and the construction influence evaluation model, improving the resource utilization efficiency. The decision-making process based on data and models is more scientific and objective than traditional experience-based decision-making, reducing the subjectivity and uncertainty of decision-making. By comprehensively considering the impact of meteorological conditions on construction, safer construction measures can be taken to ensure the safety of construction personnel. Intelligent construction decision-making helps to reduce the negative impact on the environment caused by construction activities under unsuitable weather conditions. Conducting construction under suitable meteorological conditions can ensure construction quality and reduce rework and repairs caused by weather reasons.

[0008] In a feasible implementation manner, integrating multi-source data of on-site collected meteorological data in the construction area and professional meteorological service data to obtain multi-source meteorological data specifically includes: monitoring various meteorological parameters in the area through a sensor network deployed in the construction area to obtain local meteorological data; wherein, each sensor in the sensor network at least includes: a temperature sensor, a humidity sensor, a wind direction and wind speed sensor, a precipitation sensor, a visibility sensor, a barometric pressure sensor, a solar radiation sensor, and a soil humidity sensor; transmitting the local meteorological data to the IOT platform and generating the on-site collected meteorological data; obtaining the professional meteorological service data of the location of the construction area through a professional meteorological service API; performing hierarchical integrated storage of hierarchical data sets between the on-site collected meteorological data and the professional meteorological service data to determine the multi-source meteorological data.

[0009] In a feasible implementation manner, time synchronization and data standardization processing are performed on the multi-source meteorological data to obtain high-quality meteorological data, which specifically includes: removing outliers from the multi-source meteorological data, and performing unified formatting processing on the data item attributes in the multi-source meteorological data; synchronizing and matching the execution time between the first execution time axis of the on-site collected meteorological data and the second execution time axis of the professional meteorological service data in the multi-source meteorological data to obtain multi-source meteorological data under the same time axis; according to the structured data storage method of the retrieval database, performing data storage processing under data standardization on the multi-source meteorological data under the same time axis to obtain the high-quality meteorological data.

[0010] In a feasible implementation manner, an impact assessment of each construction operation type on relevant meteorological parameters is performed to obtain impact level parameters, which specifically includes: through an expert scoring system, performing an impact assessment on each of the construction operation types in the historical record dataset within a specific meteorological level range based on each of the meteorological parameters to obtain the level impact scores of each construction operation type within each specific meteorological level range; wherein, a specific meteorological level range includes several of the same meteorological parameters; performing multi-dimensional weighted calculation on the level impact scores of different construction operation types under the same meteorological parameter to obtain the meteorological impact weight of each meteorological parameter; performing correlation calculation on the level impact scores of each construction operation type and the meteorological impact weight under the principal component analysis to obtain the impact level parameters between the construction operation type and the meteorological parameter.

[0011] In a feasible implementation manner, based on the impact level parameters, training processing of an impact relationship model is performed between the multi-source meteorological data and the construction operation type to construct a meteorological-construction impact assessment model, which specifically includes: defining labels for the target variables of the impact level parameters to determine the label definition data for model training; determining both the meteorological data characteristics in the historical multi-source meteorological data and the historical record data of the impacts of different construction operation types as the input quantities for model training; through a preset random forest model, and based on the label definition data and the input quantities, performing training processing on a diverse decision tree set between the multi-source meteorological data and the construction operation type in the historical dataset to obtain the trained meteorological-construction impact assessment model.

[0012] In a feasible implementation, through the meteorological-construction impact assessment model, a comprehensive assessment process is carried out on the current construction operation to obtain a comprehensive impact index, which specifically includes: collecting the current multi-source meteorological data of the current construction area; inputting the current multi-source meteorological data into the meteorological-construction impact assessment model, and outputting the impact index of each construction operation type within a specific meteorological grade range; performing a comprehensive weighted calculation on the impact indexes of each construction operation type within the specific meteorological grade range to obtain the comprehensive impact index of each construction operation type; based on the operation types included in the current construction operation, screening each construction operation type to determine the comprehensive impact index of the current construction operation.

[0013] In a feasible implementation, through a preset construction decision support system, the comprehensive impact index of the current construction operation is automatically evaluated to obtain a construction guidance strategy, which specifically includes: determining first rule information based on the safety threshold of each meteorological parameter; performing grade division on the specific meteorological grade range under each meteorological parameter to determine second rule information; determining the comprehensive impact index of each construction operation type as third rule information; integrating the first rule information, the second rule information, and the third rule information to obtain a rule evaluation set; automatically evaluating the comprehensive impact index of the current construction operation through the rule evaluation set to obtain evaluation result information; retrieving corresponding strategies for the evaluation result information through the construction decision support system to determine the construction guidance strategy.

[0014] In a feasible implementation, after automatically evaluating the comprehensive impact index of the current construction operation through a preset construction decision support system to obtain a construction guidance strategy, the method further includes: performing color marking processing on each construction operation in the construction guidance strategy; where green indicates suitable for construction, yellow indicates that construction needs to be carefully noted, and red indicates not suitable for construction; performing specific impact interpretation analysis on the comprehensive impact indexes of construction operations under different color markings to obtain interpretation information; mounting the interpretation information on the construction operations under the corresponding color markings and performing visual display.

[0015] In a second aspect, an embodiment of the present application further provides an intelligent construction decision-making device based on meteorological data, where the device includes: at least one processor; and a memory communicatively connected to the at least one processor; where the memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute a method for intelligent construction decision-making based on meteorological data according to any of the above embodiments.

[0016] In a third aspect, an embodiment of the present application also provides a non-volatile computer storage medium. The storage medium is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program. Each program includes instructions that, when executed by a terminal, cause the terminal to execute an intelligent construction decision-making method based on meteorological data according to any one of the above embodiments.

[0017] The present application provides an intelligent construction decision-making method, device, and medium. Compared with the prior art, the embodiments of the present application have the following beneficial technical effects:

[0018] 1. Improve construction efficiency: By synchronizing and standardizing meteorological data in real time, it is possible to more accurately predict and evaluate the impact of meteorological conditions on construction, thereby reasonably arranging the construction plan and avoiding construction delays caused by bad weather.

[0019] 2. Reduce construction risks: By evaluating the impact level of different meteorological parameters on construction, potential risks can be identified in advance, and corresponding preventive measures can be taken to reduce construction accidents and losses caused by weather changes.

[0020] 3. Optimize resource allocation: The intelligent decision-making system can reasonably allocate human resources and construction materials according to meteorological data and the construction impact evaluation model, improving resource utilization efficiency.

[0021] 4. Enhance the scientific nature of decision-making: The decision-making process based on data and models is more scientific and objective than traditional experience-based decision-making, reducing the subjectivity and uncertainty of decision-making.

[0022] 5. Improve construction safety: By comprehensively considering the impact of meteorological conditions on construction, safer construction measures can be taken to ensure the safety of construction personnel.

[0023] 6. Reduce environmental impact: Intelligent construction decision-making helps reduce the negative impact on the environment caused by construction activities under unsuitable weather conditions.

[0024] 7. Improve construction quality: Construction carried out under suitable meteorological conditions can ensure construction quality and reduce rework and repairs caused by weather reasons.

[0025] 8. Save costs: By avoiding construction under unsuitable weather conditions, additional cost expenditures caused by weather changes can be reduced.

[0026] 9. Enhance adaptability: The system can adapt to meteorological changes in different regions and different seasons, providing flexible construction decision-making support.

[0027] 10. Improve the level of modern management: The application of the intelligent construction decision-making method reflects the trend of modern construction management and helps to improve the overall management level of construction enterprises. Description of the Drawings

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings described below are only some embodiments recorded in the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings:

[0029] Figure 1 It is a flowchart of an intelligent construction decision-making method based on meteorological data provided by an embodiment of the present application;

[0030] Figure 2 It is a schematic structural diagram of an intelligent construction decision-making device based on meteorological data provided by an embodiment of the present application. Detailed Embodiments

[0031] In order to enable those skilled in the art to better understand the technical solutions in the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0032] It should be noted that this application integrates a meteorological data collection module, a multi-level operation type classification mechanism, a data analysis and processing engine, and a user interaction interface to achieve accurate assessment and intelligent management of the weather conditions at the construction site. The system uses professional meteorological service APIs (such as OpenWeatherMap and the official interface of the Meteorological Bureau) and on-site sensor networks to obtain key meteorological parameters such as temperature, humidity, wind speed, precipitation, visibility, etc. in real time, and sets detailed meteorological impact assessment criteria according to the specific requirements of foundation engineering, structural engineering, decoration and finishing, installation engineering, and special engineering. Machine learning algorithms are used to analyze historical data and real-time inputs, calculate the comprehensive impact index of each operation type, and automatically generate construction suggestions or warning messages for specific operation types. The system also provides an intuitive operation interface, enabling users to conveniently view the current weather conditions and their specific impacts on different operation types, and receive customized notification reminders. The present invention is applicable to new construction projects, infrastructure construction, and other construction scenarios that need to consider weather factors, helping project managers and on-site workers quickly determine whether certain types of construction operations are suitable, improving work efficiency, reducing delays and safety accidents caused by bad weather, and ensuring the safety and continuity of the construction process.

[0033] An embodiment of this application provides an intelligent construction decision-making method based on meteorological data, as Figure 1 shown, the intelligent construction decision-making method based on meteorological data specifically includes steps S101 - S106:

[0034] S101. Integrate multi-source data of on-site collected meteorological data in the construction area and professional meteorological service data to obtain multi-source meteorological data.

[0035] Specifically, a sensor network deployed in the construction area is used to monitor various meteorological parameters in the area to obtain local meteorological data. Among them, various sensors in the sensor network at least include: temperature sensors, humidity sensors, wind direction and wind speed sensors, precipitation sensors, visibility sensors, barometric pressure sensors, solar radiation sensors, and soil humidity sensors.

[0036] In one embodiment, a sensor network deployed on-site can monitor various meteorological parameters in real time to ensure accurate local meteorological data is obtained. Temperature sensor: Used to measure the air temperature at the construction site, usually in degrees Celsius (°C). Humidity sensor: Monitors the relative humidity in the air, usually expressed as a percentage (%). Wind direction and speed sensor: Measures the speed and direction of the wind. The speed is usually in meters per second (m / s), and the direction is expressed in angles or azimuths. Precipitation sensor: Records the rainfall, usually in millimeters (mm), and can also distinguish different precipitation forms such as rain and snow. Visibility sensor: Used to measure the atmospheric transparency, usually in kilometers (km). Barometric pressure sensor: Monitors the atmospheric pressure, usually in hectopascals (hPa). Solar radiation sensor: Measures the solar radiation intensity, usually in watts per square meter (W / m 2 ). Soil moisture sensor: Used to measure the water content in the soil, which helps to understand the earthwork conditions.

[0037] Furthermore, the local meteorological data is transmitted to the IOT platform, and the on-site collected meteorological data is generated. The data of these sensors is transmitted to the IOT platform through wireless or wired communication technologies for further processing and analysis of the local meteorological data, that is, the on-site collected meteorological data is generated.

[0038] Furthermore, through the professional meteorological service API, the professional meteorological service data of the location of the construction area is obtained.

[0039] As a feasible implementation, using professional meteorological service APIs (such as OpenWeatherMap, the official interface of the China Meteorological Administration) can obtain a wide range of meteorological data. The following are the types of data that these APIs can usually provide: Current weather conditions: air temperature, humidity, wind speed, wind direction, precipitation, visibility, atmospheric pressure, sunrise and sunset times. Hourly forecast: Meteorological data for each hour, weather data for the next few hours. Daily forecast: Meteorological data for each day, maximum and minimum temperatures, expected precipitation, daytime and nighttime weather descriptions, etc. Air quality index: Concentrations of particulate matters such as PM2.5 and PM10; Concentrations of pollutants such as nitrogen dioxide, sulfur dioxide, and ozone. Historical meteorological data: Meteorological records within a certain period in the past, which can be used for long-term meteorological analysis and pattern recognition. Extreme weather warnings: Warning information for extreme weather such as heavy rain, strong wind, and heavy snow.

[0040] Furthermore, hierarchical integrated storage of hierarchical datasets is performed between the on-site collected meteorological data and the professional meteorological service data to determine multi-source meteorological data. By combining on-site sensor data and API interface data, the system can obtain comprehensive and accurate meteorological information, thereby better evaluating the impacts of various construction operations under different weather conditions and providing corresponding construction suggestions or warning information. This method of multi-source data integration not only improves the reliability and accuracy of the data but also enhances the adaptability and practicality of the system.

[0041] S102. Perform time synchronization and data standardization processing on the multi-source meteorological data to obtain high-quality meteorological data.

[0042] Specifically, perform outlier removal processing on the multi-source meteorological data and perform format unification processing on the data item attributes in the multi-source meteorological data.

[0043] Furthermore, perform synchronization matching of the execution times between the first execution time axis of the on-site collected meteorological data and the second execution time axis of the professional meteorological service data in the multi-source meteorological data to obtain multi-source meteorological data under the same time axis.

[0044] Furthermore, according to the structured data storage method of the retrieval database, perform data storage processing under data standardization on the multi-source meteorological data under the same time axis to obtain high-quality meteorological data.

[0045] As a feasible implementation method, to ensure the accuracy and effectiveness of subsequent analysis, the collected original meteorological data needs to go through strict cleaning and preprocessing steps. First, the system will perform a preliminary check on the data to remove obvious errors or outliers, such as temperature readings outside the reasonable range or unreasonable humidity levels. Then, perform format unification processing on the information from different data sources to ensure that all data follows the same format standard for subsequent processing and analysis. In addition, the system will also perform time synchronization operations to ensure that data from different sensors and APIs are consistent on the time axis and avoid data mismatch problems caused by time deviations. Finally, the data will be stored in a structured database for quick retrieval and efficient use. This series of data cleaning and preprocessing measures lay a solid foundation for subsequent machine learning algorithm analysis and decision support. Through these steps, the system can provide high-quality meteorological data to more accurately evaluate the impacts of weather conditions on various construction operations.

[0046] S103. Perform impact assessment on each type of construction operation work type regarding meteorological parameters to obtain impact level parameters.

[0047] Specifically, through an expert scoring system, the impact of each type of construction operation in the historical record dataset is evaluated based on the specific meteorological grade ranges under each meteorological parameter, and the grade impact scores of each construction operation in each specific meteorological grade range are obtained. Among them, each meteorological parameter includes several specific meteorological grade ranges.

[0048] Table 1

[0049] First-level classification Second-level classification Foundation engineering Earth excavation, integrated pouring Structural engineering Steel bar binding, formwork installation, concrete pouring Decoration and finishing Internal and external wall plastering, paint brushing Installation engineering Electrical safety, water supply and drainage pipeline installation Special engineering High-altitude operation, blasting operation

[0050] As a feasible implementation method, Table 1 is a classification table of construction operation types. Foundation engineering: such as earth excavation and foundation pouring, will be greatly affected under extreme low temperature or heavy rain conditions, which may lead to problems such as soil freezing or waterlogging. Structural engineering: Steel bar binding, formwork installation and concrete pouring will affect the material properties and the operation efficiency of workers under extreme temperatures (too cold or too hot). Decoration and finishing: Plastering and painting of interior and exterior walls will not work well under high humidity or strong wind conditions, which may affect the surface quality and drying time. Installation engineering: Electrical installation and water supply and drainage pipeline installation need special attention to safety protection measures in a humid environment to avoid short circuits or other safety hazards. Special engineering: The risks of high-altitude operations and blasting operations increase significantly under strong wind, low visibility or extreme temperature conditions, and additional safety measures must be taken or the operations must be suspended.

[0051] Furthermore, multi-dimensional weighted calculations are performed on the grade impact scores of different construction operations under the same meteorological parameter to obtain the meteorological impact weights of each meteorological parameter.

[0052] Table 2

[0053]

[0054] As a feasible implementation method, Table 2 shows the meteorological impact weights of each meteorological parameter, and specific meteorological grade ranges such as: extreme low temperature (<0°C), light rain (<10 mm / day), 40%-60%.

[0055] Furthermore, correlation calculations are performed on the grade impact scores of each construction operation type and the meteorological impact weights under the principal component analysis to obtain the impact grade parameters between the construction operation types and meteorological parameters.

[0056] S104. Based on the impact grade parameters, training processing is performed on the impact relationship model between multi-source meteorological data and construction operation types to construct a meteorological-construction impact assessment model.

[0057] Specifically, label definitions of target variables are made for the impact grade parameters to determine the label definition data for model training.

[0058] Furthermore, both the meteorological data features in the historical multi-source meteorological data and the historical record data affected by different construction operation types are determined as the input quantities for model training.

[0059] Furthermore, through a preset random forest model, and based on the labeled data and the input quantities, training processing is performed on the multi-source meteorological data and construction operation types in the historical dataset for a diverse decision tree ensemble, to obtain a trained meteorological-construction impact assessment model.

[0060] As a feasible implementation, in order to accurately evaluate the impact of different meteorological conditions on construction operations, the present invention uses the Random Forest algorithm to establish a relationship model between meteorological parameters and construction operation impacts. Implementation process:

[0061] 1. Data preparation

[0062] Historical data collection: Collect historical records containing meteorological parameters (such as temperature, humidity, wind speed, precipitation, visibility, etc.) and their impacts on different operation types from past projects, that is, historical multi-source meteorological data and historical record data of the impacts of different construction operation types.

[0063] Feature definition: Determine which meteorological parameters and construction operation impact historical records are used as the input features of the model. For example, temperature, humidity, wind speed, precipitation, and visibility, etc.

[0064] Label definition: Define a target variable for each record, that is, the suitability score or impact level (impact level parameter) of a specific operation type under this meteorological condition.

[0065] 2. Data preprocessing

[0066] Missing value processing: Check and fill or delete records with a large number of missing values to ensure the integrity and consistency of the data.

[0067] Standardization / normalization: For numerical features, it may be necessary to perform standardization or normalization processing to ensure that all features are on the same scale and avoid some features affecting the model performance due to overly large or small numerical values.

[0068] Partition the training set and the test set: Usually divide the data into the training set and the test set according to a ratio of 70:30 or 80:20 for subsequent model verification.

[0069] 3. Model training

[0070] Initialize the random forest model: Set hyperparameters such as the number of trees (n_estimators), maximum depth (max_depth), minimum number of samples for splitting (min_samples_split), etc. These hyperparameters can be adjusted according to specific requirements to optimize the model performance.

[0071] Train the model: Use the training set data to train the random forest model. Each decision tree will be trained based on different random subsets, thus forming a diverse set of decision trees. By integrating the results of multiple decision trees, the overfitting problem that may occur in a single model can be effectively reduced, and the prediction accuracy can be improved.

[0072] 4. Model evaluation

[0073] Performance evaluation: Use the test set to evaluate the performance of the model. Commonly used evaluation metrics include accuracy, precision, recall, and F1-score. These metrics can help evaluate the effectiveness of the model in practical applications.

[0074] Cross-validation: To more robustly evaluate the model performance, the k-fold cross-validation method can be adopted. This method divides the dataset for training and testing multiple times, which can provide a more reliable performance estimate. Finally, after training the multi-source meteorological data and construction operation types in the historical dataset for the diverse set of decision trees, a trained meteorological-construction impact assessment model is obtained.

[0075] S105. Through the meteorological-construction impact assessment model, conduct a comprehensive evaluation of the current construction operation to obtain a comprehensive impact index.

[0076] Specifically, collect the current multi-source meteorological data of the current construction area.

[0077] Furthermore, input the current multi-source meteorological data into the meteorological-construction impact assessment model, and output the impact index of each construction operation type within the specific meteorological grade range.

[0078] Furthermore, conduct a comprehensive weighted calculation of the impact indices of the construction operation types within the specific meteorological grade range to obtain the comprehensive impact index of each construction operation type.

[0079] Furthermore, based on the operation types included in the current construction operation, screen each construction operation type to determine the comprehensive impact index of the current construction operation.

[0080] In one embodiment, to comprehensively consider multiple meteorological parameters and their impacts on different types of operations, the system uses a weighted average formula to calculate the comprehensive impact index of each operation type. The formula is as follows:

[0081] Among them, \(w_i\) represents the weight of the \(i\)-th meteorological parameter, reflecting its importance for a specific operation type. \(I_i\) represents the influence level of the \(i\)-th meteorological parameter, which is determined according to the aforementioned table. For example: Assume the current weather data is as follows: temperature: \(35^{\circ}C\), humidity: \(70\%\), wind speed: \(12m / s\), precipitation: light rain (\(5mm / day\)), visibility: \(3km\). Determine the influence levels of each meteorological parameter according to the above table, and calculate the comprehensive influence index of earth excavation (foundation engineering).

[0082] Table 3

[0083] Meteorological parameters Level description Influence level (earth excavation) Weight Air temperature Higher temperature (30 - 40°C) 2 0.1 Humidity 60％-80％ 2 0.1 Wind speed 10 - 15 m / s 3 0.2 Precipitation Light rain (<10 mm / day) 2 0.3 Visibility 2 - 5 km 2 0.1

[0084] In one embodiment, Table 3 is for the comprehensive weighted calculation of the influence index of the earth excavation work type within a specific meteorological grade range, and the influence index = \((0.1\times2)+(0.1\times2)+(0.2\times3)+(0.3\times2)+(0.1\times2)\)

[0085] \(=0.2 + 0.2+0.6 + 0.6+0.2=0.2 + 0.2+0.6 + 0.6+0.2 = 1.8 = 1.8\). This means that under the current conditions, earth excavation is affected to a certain extent, but generally construction can still continue. Then, according to the above method, each construction operation type is screened, and finally the comprehensive influence index of the current construction operation is determined.

[0086] S106. Through a preset construction decision support system, automatically evaluate the comprehensive influence index of the current construction operation to obtain a construction guidance strategy.

[0087] Specifically, based on the safety threshold of each meteorological parameter, determine the first rule information. Divide the specific meteorological grade range under each meteorological parameter to determine the second rule information. Determine the comprehensive influence index of each construction operation type as the third rule information. Integrate the first rule information, the second rule information, and the third rule information to obtain a rule evaluation set.

[0088] In one embodiment, the rule set defines: Safety threshold setting (first rule information): Set safety thresholds for each meteorological parameter (such as temperature, humidity, wind speed, precipitation, visibility, etc.). For example, for high-altitude operations, a wind speed exceeding 10 m / s may be considered unsafe. Impact level classification (second rule information): According to the impact level table of meteorological parameters, classify the impacts of different meteorological conditions on various construction operations into three levels: low, medium, and high. For example, when the temperature is between 30 - 40 °C, the impact level on painting operations is medium. Comprehensive impact index calculation (third rule information): Use the weighted average formula to calculate the comprehensive impact index for each construction operation type, and determine whether it is suitable to carry out a specific operation based on a preset threshold.

[0089] Furthermore, through the rule evaluation set, automatically evaluate the comprehensive impact index of the current construction operation to obtain the evaluation result information.

[0090] In one embodiment, the automated evaluation process includes: Real-time data input: The system obtains real-time meteorological data from the meteorological data collection module and inputs it into the trained machine learning model. Model prediction: The model outputs the comprehensive impact index for each operation type based on the input meteorological data. Rule matching and evaluation: The system matches the comprehensive impact index with the predefined rule set to evaluate the safety and suitability of various construction operations under the current conditions. If the comprehensive impact index of a certain operation exceeds the safety threshold, the system will mark this operation as "not suitable for carrying out" or "additional measures are required".

[0091] Furthermore, through the construction decision support system, retrieve and process the corresponding strategies for the evaluation result information to determine the construction guidance strategy. Among them, the Decision Support System (DSS) is the core component of the intelligent construction management system based on real-time meteorological data analysis. It helps project managers and on-site workers make scientific and reasonable construction arrangements through automated evaluation and customized notification functions, improving work efficiency and reducing delays and safety accidents caused by bad weather, that is, the construction guidance strategy. That is, based on the evaluation result information, the system automatically generates construction suggestions or warning information for specific operation types. For example, if the current wind speed is high, the system may recommend suspending high-altitude operations; if the humidity is high, the system may recommend postponing painting operations.

[0092] As a feasible implementation method, color label each construction operation in the construction guidance strategy. Among them, green indicates suitable for construction, yellow indicates that construction needs to be carefully noted, and red indicates not suitable for construction. Conduct specific impact interpretation analysis on the comprehensive impact index of construction operations under different color labels to obtain the interpretation information. Mount the interpretation information to the construction operations under the corresponding color labels and perform visual display.

[0093] As a feasible implementation method, personalized configuration can also be carried out: User preference settings: Users can set the types and frequencies of notifications they wish to receive in the system interface. For example, a project manager can choose to receive only notifications related to high-altitude work and foundation engineering, while ignoring notifications for other types of work. Notification method selection: The system supports multiple notification methods, including text messages, emails, in-app notifications, etc. Users can choose the most suitable receiving method according to their own needs.

[0094] As a feasible implementation method, a notification triggering mechanism can also be carried out: Real-time monitoring: The system continuously monitors the real-time meteorological data at the construction site and automatically evaluates the safety and suitability of each operation according to a predefined rule set. Automatic notification triggering: When the system detects a change in the safety or suitability of a certain operation, it will automatically trigger the corresponding notification. For example, if the wind speed suddenly increases, the system will immediately send a warning message about high-altitude work to the relevant person in charge. Customized content: The notification content not only includes basic meteorological information but also provides specific construction suggestions or warnings. For example, "The current wind speed is 12m / s, and there are significant risks in high-altitude work. Please suspend the operation and take necessary protective measures."

[0095] As a feasible implementation method, notification examples can also be carried out: Text message notification: "[Project name] The current wind speed has reached 12m / s, and there are significant risks in high-altitude work. Please suspend the operation and take necessary protective measures." Email notification: "Dear [User name], Hello! According to the latest meteorological data, the temperature is expected to reach 35°C today. It is recommended to adjust the painting operation time and avoid construction during high-temperature periods." In-app notification: "[Project name] Reminder: The current humidity is 80%, and the interior and exterior wall plastering operations may be affected. Please pay attention to the construction quality and drying time." Through this automated evaluation and customized notification function, the system can effectively help the construction team cope with complex and changeable weather conditions, ensuring the safety and continuity of the construction process. In addition, personalized notification settings enable each user to receive the most important information related to their responsibilities, improving communication efficiency and response speed, and further enhancing the practicality and user experience of the system.

[0096] As a feasible implementation method, user interaction can also be carried out. That is, in order to provide an easy-to-understand and powerful operation interface to help users view the current weather conditions and their specific impacts on different work types, the system has designed an intuitive and user-friendly interface. The following is a detailed description of the main components of this interface and the display methods:

[0097] 1. Main dashboard

[0098] Real-time Weather Overview: Display a real-time weather overview of the current construction site at the top or prominent position of the interface, including key meteorological parameters such as temperature, humidity, wind speed, precipitation, and visibility.

[0099] Visualization: Use icons, charts, or progress bars to visually display this data. For example, temperature can be shown in the form of a thermometer, wind speed can be represented by a wind vane, and precipitation can be marked with a raindrop icon.

[0100] Overview of Comprehensive Impact Index: Immediately following is an overview of the comprehensive impact index for various types of operations. It quickly reflects the safety and suitability of each operation type through color coding (e.g., green indicates suitable for construction, yellow indicates attention required, and red indicates not suitable for construction).

[0101] 2. Details Panel for Operation Types

[0102] Categorized Display: Subdivide construction operations into categories such as foundation works, structural works, decoration works, installation works, and special works, and list the specific items for each operation type (such as earth excavation, steel bar binding, interior and exterior wall plastering, etc.).

[0103] Specific Impact Analysis: After clicking on a certain operation type, expand a detailed meteorological impact analysis page to show the impact level of various parameters and the comprehensive impact index of this operation under the current meteorological conditions.

[0104] Detailed Explanation: Provide a short text description for each impact level to explain why this operation is affected specifically under these conditions. For example, "The current high humidity may cause the paint drying time to be extended."

[0105] 3. Personalized Notification Settings

[0106] Subscription Management: Allow users to customize the types and frequencies of notifications received. Users can choose to follow notifications for specific operation types, such as high-altitude operations, blasting operations, etc.

[0107] Notification Method Selection: Provide multiple notification method options, including text messages, emails, in-app notifications, etc., and allow users to configure according to their personal preferences.

[0108] 4. Historical Data Analysis and Trend Prediction

[0109] Historical Data Comparison: Provide a historical data comparison module that enables users to view the weather change trends and their impacts on construction over a past period of time, helping to identify potential risk patterns.

[0110] Future Weather Forecast: Integrate weather forecast information for the next few days to help users plan construction arrangements in advance and avoid delays caused by bad weather.

[0111] With this intuitive and user-friendly operation interface design, users can easily view the current weather conditions and their specific impacts on different types of operations, obtain important information in a timely manner, make scientific and reasonable construction decisions, thereby improving work efficiency and reducing delays and safety accidents caused by bad weather.

[0112] In addition, the embodiment of the present application also provides an intelligent construction decision-making device based on meteorological data, such as Figure 2 shown, the intelligent construction decision-making device 200 based on meteorological data specifically includes:

[0113] At least one processor 201. And, a memory 202 communicatively connected to the at least one processor 201. Among them, the memory 202 stores instructions that can be executed by the at least one processor 201, so that the at least one processor 201 can execute:

[0114] Integrate the on-site collected meteorological data of the construction area with the professional meteorological service data to obtain multi-source meteorological data;

[0115] Perform time synchronization and data standardization processing on the multi-source meteorological data to obtain high-quality meteorological data;

[0116] Evaluate the impact of meteorological parameters on each type of construction operation to obtain impact level parameters;

[0117] Based on the impact level parameters, perform training processing on the impact relationship model between the multi-source meteorological data and the construction operation types to construct a meteorological-construction impact evaluation model;

[0118] Through the meteorological-construction impact evaluation model, perform comprehensive evaluation processing on the current construction operation to obtain a comprehensive impact index;

[0119] Through the preset construction decision support system, automatically evaluate the comprehensive impact index of the current construction operation to obtain a construction guidance strategy.

[0120] By synchronizing and standardizing meteorological data in real time, the embodiments of the present application can more accurately predict and evaluate the impact of meteorological conditions on construction, thereby reasonably arranging the construction plan and avoiding construction delays caused by bad weather. By evaluating the impact levels of different meteorological parameters on construction, potential risks can be identified in advance, and corresponding preventive measures can be taken to reduce construction accidents and losses caused by weather changes. The intelligent decision-making system can reasonably allocate human resources and construction materials according to meteorological data and the construction impact evaluation model, improving the resource utilization efficiency. The decision-making process based on data and models is more scientific and objective than traditional experience-based decision-making, reducing the subjectivity and uncertainty of decision-making. By comprehensively considering the impact of meteorological conditions on construction, safer construction measures can be taken to ensure the safety of construction personnel. Intelligent construction decision-making helps to reduce the negative impact on the environment caused by construction activities under unsuitable weather conditions. Conducting construction under suitable meteorological conditions can ensure the construction quality and reduce rework and repairs caused by weather reasons.

[0121] The embodiments in the present application are all described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device and the medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0122] The device and the medium provided by the embodiments of the present application correspond one by one to the method. Therefore, the device and the medium also have beneficial technical effects similar to those of the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and the medium will not be elaborated here.

[0123] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0125] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0127] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0128] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0129] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0130] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0131] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the specification of the present application.

Claims

1. An intelligent construction decision-making method based on meteorological data, characterized in that, The method includes: Integrating multi-source data of on-site collected meteorological data and professional meteorological service data in the construction area to obtain multi-source meteorological data; Performing time synchronization and data standardization processing on the multi-source meteorological data to obtain high-quality meteorological data; Evaluating the influence of each construction operation type on relevant meteorological parameters to obtain influence level parameters; Based on the influence level parameters, training the influence relationship model between the multi-source meteorological data and the construction operation types to construct a meteorology-construction influence evaluation model; Through the meteorology-construction influence evaluation model, comprehensively evaluating the current construction operation to obtain a comprehensive influence index; Through a preset construction decision support system, automatically evaluating the comprehensive influence index of the current construction operation to obtain a construction guidance strategy.

2. The intelligent construction decision-making method based on meteorological data according to claim 1, characterized in that Integrating multi-source data of on-site collected meteorological data and professional meteorological service data in the construction area to obtain multi-source meteorological data, specifically including: Monitoring various meteorological parameters in the area through a sensor network deployed in the construction area to obtain local meteorological data; wherein, each sensor in the sensor network at least includes: a temperature sensor, a humidity sensor, a wind direction and wind speed sensor, a precipitation sensor, a visibility sensor, a barometric pressure sensor, a solar radiation sensor, and a soil humidity sensor; Transmitting the local meteorological data to the IOT platform and generating the on-site collected meteorological data; Obtaining professional meteorological service data of the location of the construction area through a professional meteorological service API; Performing hierarchical integrated storage of hierarchical data sets between the on-site collected meteorological data and the professional meteorological service data to determine the multi-source meteorological data.

3. The intelligent construction decision-making method based on meteorological data according to claim 1, wherein, Performing time synchronization and data standardization processing on the multi-source meteorological data to obtain high-quality meteorological data, specifically including: Removing outliers from the multi-source meteorological data and unifying the format of the data item attributes in the multi-source meteorological data; Performing synchronous matching of the execution time between the first execution time axis of the on-site collected meteorological data and the second execution time axis of the professional meteorological service data in the multi-source meteorological data to obtain multi-source meteorological data under the same time axis; According to the structured data storage method of the retrieval database, performing data storage processing under data standardization on the multi-source meteorological data under the same time axis to obtain the high-quality meteorological data.

4. The intelligent construction decision-making method based on meteorological data according to claim 1, characterized in that Evaluating the influence of each construction operation type on relevant meteorological parameters to obtain influence level parameters, specifically including: Through an expert scoring system, evaluating the influence of each construction operation type in the historical record data set on the specific meteorological grade range under each meteorological parameter to obtain the grade influence score of each construction operation type in each specific meteorological grade range; wherein, each meteorological parameter includes several specific meteorological grade ranges; Performing multi-dimensional weighted calculation on the grade influence scores of different construction operation types under the same meteorological parameter to obtain the meteorological influence weight of each meteorological parameter; Perform correlation calculations on the grade influence scores of each of the construction operation types and the meteorological influence weights under principal component analysis to obtain the influence grade parameters between the construction operation types and the meteorological parameters.

5. The intelligent construction decision-making method based on meteorological data according to claim 1, wherein Based on the influence grade parameters, perform training processing on the influence relationship model between the multi-source meteorological data and the construction operation types to construct a meteorology-construction influence assessment model, specifically including: Define labels for the target variables of the influence grade parameters to determine the label definition data for model training; Determine both the meteorological data characteristics in the historical multi-source meteorological data and the historical record data affected by different construction operation types as the input quantities for model training; Through a preset random forest model, and based on the label definition data and the input quantities, perform training processing on the diverse decision tree sets between the multi-source meteorological data and the construction operation types in the historical dataset to obtain the trained meteorology-construction influence assessment model.

6. The intelligent construction decision-making method based on meteorological data according to claim 1, characterized in that, Through the meteorology-construction influence assessment model, perform comprehensive assessment processing on the current construction operation to obtain a comprehensive influence index, specifically including: Collect the current multi-source meteorological data of the current construction area; Input the current multi-source meteorological data into the meteorology-construction influence assessment model to output the influence index of each construction operation type within a specific meteorological grade range; Perform comprehensive weighted calculation on the influence indexes of each construction operation type within a specific meteorological grade range to obtain the comprehensive influence index of each construction operation type; Based on the operation types included in the current construction operation, perform screening processing on each construction operation type to determine the comprehensive influence index of the current construction operation.

7. A method for intelligent construction decision-making based on meteorological data according to claim 1, characterized in that Through a preset construction decision support system, perform automated evaluation on the comprehensive influence index of the current construction operation to obtain a construction guidance strategy, specifically including: Based on the safety thresholds of each meteorological parameter, determine the first rule information; Perform grade division on the specific meteorological grade ranges under each meteorological parameter to determine the second rule information; Determine the comprehensive influence index of each construction operation type as the third rule information; Integrate the first rule information, the second rule information, and the third rule information to obtain a rule evaluation set; Through the rule evaluation set, perform automated evaluation on the comprehensive influence index of the current construction operation to obtain evaluation result information; Through the construction decision support system, perform retrieval processing on corresponding strategies for the evaluation result information to determine the construction guidance strategy.

8. The intelligent construction decision-making method based on meteorological data according to claim 1, wherein After performing automated evaluation on the comprehensive influence index of the current construction operation through a preset construction decision support system to obtain a construction guidance strategy, the method further includes: Perform color marking processing on each construction operation in the construction guidance strategy; where green indicates suitable for construction, yellow indicates that construction needs to be carefully noted, and red indicates not suitable for construction; Perform specific influence interpretation analysis on the comprehensive influence indexes of construction operations under different color markings to obtain interpretation information; Mount the interpretation information to the construction operations under the corresponding color markings and perform visual display.

9. An intelligent construction decision-making device based on meteorological data, characterized in that, The device includes: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, enabling the at least one processor to execute a method for intelligent construction decision-making based on meteorological data according to any one of claims 1-8.

10. A non-volatile computer storage medium, characterized in that, The storage medium is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program, each program including instructions that, when executed by a terminal, cause the terminal to execute a method for intelligent construction decision-making based on meteorological data according to any one of claims 1-8.

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