Construction safety gridding management optimization method and system based on artificial intelligence

Abstract

The invention discloses a construction safety gridding management optimization method and system based on artificial intelligence, and belongs to the technical field of construction safety management, and the method comprises the steps of grid region division, data preprocessing, environmental risk prediction, accident risk prediction and construction safety management optimization. According to the method, the self-attention multi-scale convolution long-short-term memory model is adopted for environment risk prediction, information of different scales in environment related data is comprehensively captured, attention to key moments is enhanced, and therefore important risk signals are accurately captured; a dual-path multi-modal deep neural network model is adopted to perform accident risk prediction, various factors influencing the accident risk are comprehensively captured, the accuracy of a prediction result is enhanced, and different types of data are processed through dual paths, so that the model calculation efficiency is improved; the two steps of environmental risk prediction and accident risk prediction are adopted to assist grid construction safety management optimization, and the precision, response speed and visualization effect of construction safety management are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of construction safety management, and specifically refers to an artificial intelligence-based construction safety grid management optimization method and system. Background Art

[0002] The grid management optimization of construction safety combines artificial intelligence technology and grid management concepts to conduct dynamic risk analysis of construction sites, aiming to provide accurate risk predictions for construction sites, improve the scientific nature and efficiency of construction safety management, thereby promoting the refined and intelligent development of construction safety management and effectively reducing the accident rate.

[0003] However, in the existing environmental risk prediction process, there are data changes such as temperature, humidity, and dust concentration in the environment that often have trends on different time scales. Traditional environmental prediction models only consider single-scale features, ignore the information complementarity between different scales, and cannot effectively capture multi-scale environmental changes, resulting in technical problems such as poor prediction accuracy; in the existing accident risk prediction process, there are accident risks at construction sites that need to be analyzed by comprehensively considering environmental changes, historical safety accidents, and real-time monitoring, while traditional accident risk prediction usually relies on a single data source and cannot fully consider information from multiple dimensions, resulting in technical problems such as reduced prediction accuracy; in the existing construction safety grid management optimization process, there is the existing construction site management process, which makes it difficult to accurately predict and intervene in potential risks in different areas of the construction site, and the main safety hazards at the construction site come from environmental changes and construction site accidents, which leads to the technical problem that traditional safety management lacks regionalization, precise prediction and real-time adjustment. Summary of the invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an optimization method and system for construction safety grid management based on artificial intelligence. In view of the fact that in the existing environmental risk prediction process, the changes in data such as temperature, humidity, dust concentration in the environment often have trends on different time scales, and the traditional environmental prediction model only considers single-scale features, ignores the information complementarity between different scales, and cannot effectively capture multi-scale environmental changes, resulting in poor prediction accuracy. The present invention creatively adopts a self-attention multi-scale convolutional long short-term memory model for environmental risk prediction, comprehensively captures different scale information in environmental-related data, effectively handles long-term dependencies, and enhances attention to critical moments, thereby accurately capturing important risk signals, improving prediction accuracy and sensitivity, and helping to improve the overall effectiveness and reliability of the method; In view of the fact that in the existing accident risk prediction process, the accident risk at the construction site needs to be analyzed by comprehensively considering environmental changes, historical safety accidents and real-time monitoring, while traditional accident risk prediction usually relies on a single data source and cannot fully consider information in multiple dimensions, resulting in reduced prediction accuracy. The present invention creatively adopts a self-attention multi-scale convolutional long short-term memory model for environmental risk prediction, comprehensively captures different scale information in environmental-related data, effectively handles long-term dependencies, and enhances attention to critical moments, thereby accurately capturing important risk signals, improving prediction accuracy and sensitivity, and helping to improve the overall effectiveness and reliability of the method; In view of the fact that in the existing accident risk prediction process, the accident risk at the construction site needs to be analyzed by comprehensively considering environmental changes, historical safety accidents and real-time monitoring, and the traditional accident risk prediction usually relies on a single data source and cannot fully consider information in multiple dimensions, resulting in reduced prediction accuracy. The present invention creatively adopts The dual-path multimodal deep neural network model predicts accident risks, effectively processes multimodal data related to accident risks, comprehensively captures various factors affecting accident risks, enhances the accuracy of prediction results, and processes different types of data through dual paths, which improves the calculation efficiency of the model and helps to timely discover potential safety hazards at the construction site. In the process of optimizing the existing grid management of construction safety, it is difficult to accurately predict and intervene in the potential risks in different areas of the construction site in the existing construction site management process. The main safety hazards at the construction site come from environmental changes and construction site accidents, which leads to the technical problem that traditional safety management lacks regionalization, precise prediction and real-time adjustment. This solution creatively adopts two steps of environmental risk prediction and accident risk prediction to assist in the optimization of grid construction safety management. Through the prediction of environmental risk and accident risk at two levels, it can realize dynamic safety assessment and targeted inspection of each area under the grid management framework, thereby optimizing resource allocation and inspection efficiency, reducing safety hazards, improving the accuracy, response speed and visualization effect of construction safety management, and ensuring that the safety management of each grid area is more scientific and efficient.

[0005] The technical solution adopted by the present invention is as follows: The construction safety grid management optimization method based on artificial intelligence provided by the present invention comprises the following steps:

[0006] Step S1: grid area division;

[0007] Step S2: data preprocessing;

[0008] Step S3: Environmental risk prediction;

[0009] Step S4: accident risk prediction;

[0010] Step S5: Optimization of construction safety management.

[0011] Further, in step S1, the grid area division is used to divide the grid area and collect data, specifically, the construction site is divided into N grid areas according to the distribution of the work area, and the historical safety accident data of the construction site is collected, and the environmental data and video monitoring data in the grid area are collected by sensors;

[0012] The historical safety accident data, including accident date, location, category and level;

[0013] The environmental data includes temperature, humidity, light, noise and dust concentration.

[0014] Furthermore, in step S2, the data preprocessing is used to optimize the data quality, including the following steps:

[0015] Step S21: preprocessing the historical safety accident data, specifically, performing data cleaning and normalization on the historical safety accident data, and label encoding the type data in the historical safety accident data to obtain historical safety accident optimized data;

[0016] Step S22: preprocessing the environmental data, specifically, obtaining environmental optimization data by performing data cleaning, time alignment and normalization on the environmental data;

[0017] Step S23: Video surveillance data preprocessing, specifically extracting image frames from the video surveillance data at a rate of one frame per second, and denoising and standardizing each frame of the image to obtain surveillance standard image data, and dividing all images in the surveillance standard image data into G fixed-size image blocks to obtain surveillance image block data.

[0018] Further, in step S3, the environmental risk prediction is used to predict environmental risks, specifically, based on environmental optimization data, a self-attention multi-scale convolutional long short-term memory model is used to predict environmental risks to obtain environmental risk prediction information;

[0019] The self-attention multi-scale convolutional long short-term memory model includes an input layer, a multi-scale convolutional subnet, a random inactivation layer, a long short-term memory layer, a self-attention layer and an output layer;

[0020] The multi-scale convolutional subnet is used to capture multi-scale features from the data;

[0021] The long short-term memory layer is used to capture long-term and short-term dependencies from data;

[0022] The self-attention layer is used to focus on key time steps;

[0023] The environmental risk prediction includes the following steps:

[0024] Step S31: constructing a self-attention parallel convolutional long short-term memory model, including the following steps:

[0025] Step S311: constructing an input layer for receiving input data, specifically receiving environmental optimization data as input data of the model through the input layer;

[0026] Step S312: construct a multi-scale convolutional subnet, specifically, setting a parallel first convolutional layer, a second convolutional layer, and a third convolutional layer in the multi-scale convolutional subnet, and setting an average pooling layer after each convolutional layer, and splicing the output results of each average pooling layer through a feature splicing layer to obtain multi-scale features of the environment, wherein the first convolutional layer contains 16 4×1 convolutional kernels, the second convolutional layer contains 32 4×5 convolutional kernels, and the third convolutional layer contains 64 4×5 convolutional kernels;

[0027] Step S313: construct a random dropout layer to avoid model overfitting. Specifically, the environment multi-scale features are processed through the random dropout layer to obtain the environment random dropout features.

[0028] Step S314: constructing a long short-term memory layer, specifically setting 295 long short-term memory units in the long short-term memory layer, processing the random inactivation characteristics of the environment through the long short-term memory layer, and obtaining the long short-term time series characteristics of the environment;

[0029] The long short-term memory unit includes a forget gate, an input gate, an output gate and a memory unit;

[0030] Step S315: construct a self-attention layer. Specifically, in the self-attention layer, the self-attention mechanism is used to process the long-term and short-term time series features of the environment to obtain the self-attention features of the environment. The calculation formula is:

[0031] ;

[0032] Where Q is the query matrix, h is the long-term and short-term time series features of the environment, the long-term and short-term time series features are a matrix of u×d, u is the total number of time steps of the long-term and short-term time series features of the environment, d is the dimension of the long-term and short-term time series features of the environment, and W Q is a d×d query weight matrix, K is the key matrix, W K is a d×d key weight matrix, V is a value matrix, W V is a d×d value weight matrix, Att is the self-attention score, softmax(·) is the softmax activation function, T is the transpose operation, and h attis the environmental self-attention feature;

[0033] Step S316: construct an output layer for outputting the model prediction results, specifically, processing the environment self-attention features through a fully connected layer and a linear activation function to obtain the model prediction results;

[0034] Step S32: constructing an environmental risk prediction model, specifically, training a self-attention parallel convolutional long short-term memory model based on environmental optimization data, and using a particle swarm optimization algorithm to tune model parameters to obtain an environmental risk prediction model;

[0035] Step S33: Generate environmental risk prediction information, specifically, perform environmental risk prediction on each grid area through an environmental risk prediction model to obtain environmental risk prediction information, wherein the environmental risk prediction information includes environmental risk values ​​and major environmental risk factors.

[0036] Further, in step S4, the accident risk prediction is used to predict the accident risk, specifically, based on the environmental risk prediction information, the historical safety accident optimization data and the monitoring image block data, a dual-path multimodal deep neural network model is used to perform accident risk prediction to obtain accident risk prediction information;

[0037] The dual-path multimodal deep neural network model includes a dual-path feature processing subnet, a multimodal feature fusion layer and a prediction output layer;

[0038] The dual-path feature processing subnet is used to process feature information of multimodal data in parallel;

[0039] The multimodal feature fusion layer is used to fuse multimodal feature information;

[0040] The accident risk prediction includes the following steps:

[0041] Step S41: constructing a dual-path feature processing subnet, specifically setting a parallel first feature processing path and a second feature processing path in the dual-path feature processing subnet, including the following steps:

[0042] Step S411: constructing a first feature processing path, specifically setting a random forest model in the first feature processing path, processing environmental risk prediction information and historical safety accident optimization data through the first feature processing path, and obtaining environmental accident features;

[0043] Step S412: constructing a second feature processing path, specifically setting a flattened embedding layer, an image visual transformer model, a long short-term memory layer, and an average pooling layer in the second feature processing path, processing the monitoring image block data through the second feature processing path, and obtaining monitoring image features, including the following steps:

[0044] Step S4121: construct a flattened embedding layer, specifically, flatten each monitoring image block in the monitoring image block data into a vector, and perform linear mapping on the vector to obtain the monitoring image block mapping feature, and the calculation formula is:

[0045] ;

[0046] In the formula, z i is the mapping feature of the i-th monitoring image block, i is the monitoring image block index, W p is the linear mapping weight, x i is the flattened vector of the ith monitoring image block, b p is the linear mapping bias term;

[0047] Step S4121: constructing a visual transformer model, specifically, setting 24 layers of encoders in the visual transformer model, and setting 16 self-attention heads in each layer, and processing the monitoring image block mapping features through the visual transformer model to obtain the monitoring image global features;

[0048] Step S4122: constructing a long short-term memory layer, setting 295 long short-term memory units in the long short-term memory layer, processing the global features of the monitoring image through the long short-term memory layer, and obtaining the temporal features of the monitoring image;

[0049] Step S4123: construct an average pooling layer for aggregating features in the time dimension. Specifically, the monitoring image temporal features are processed by the average pooling layer to obtain the monitoring average pooling features. The calculation formula is:

[0050] ;

[0051] In the formula, f G is the monitoring average pooling feature, t max is the maximum time step, t is the time step index, c t is the temporal feature of the monitoring image at the tth time step;

[0052] Step S42: construct a multimodal feature fusion layer, specifically, weighted fusion of environmental accident features and monitoring average pooling features to obtain accident risk features. The calculation formula is:

[0053] ;

[0054] In the formula, f total is the accident risk characteristic, is the first weighting coefficient, which is used to represent the weight of environmental accident characteristics, f env It is the characteristic of environmental accidents. is the second weighting coefficient, which is used to represent the monitoring average pooling feature weight;

[0055] Step S43: constructing a prediction output layer, specifically processing the accident risk features through a fully connected layer and a softmax activation function to obtain the final prediction result;

[0056] Step S44: constructing a dual-path multimodal deep neural network model, specifically by constructing a dual-path multimodal deep neural network model through the construction of a dual-path feature processing subnet, the construction of a multimodal feature fusion layer, and the construction of a prediction output layer;

[0057] Step S45: constructing an accident risk prediction model, specifically, training a dual-path multimodal deep neural network model based on environmental risk prediction information, historical safety accident optimization data, and monitoring image block data, and using a particle swarm optimization algorithm to tune model parameters to obtain an accident risk prediction model;

[0058] Step S46: Generate accident risk prediction information, specifically, perform accident risk prediction through an accident risk prediction model to obtain accident risk prediction information, wherein the accident risk prediction information includes accident risk levels and major accident risk factors.

[0059] Furthermore, in step S5, the construction safety management optimization is used to optimize the grid management of construction safety, specifically, based on the accident risk prediction information and the environmental risk prediction information, optimization of comprehensive construction safety management is performed to obtain construction safety management prediction auxiliary information, and by conducting grid construction site safety inspections, data aggregation and visual comprehensive analysis based on the construction safety management prediction auxiliary information, a construction safety management optimization report is generated to help construction safety managers perform safety management and decision-making within the grid area.

[0060] The artificial intelligence-based construction safety grid management optimization system provided by the present invention includes: a grid area division module, a data preprocessing module, an environmental risk prediction module, an accident risk prediction module and a construction safety management optimization module;

[0061] The grid area division module is used for grid area division, and obtains historical safety accident data, environmental data and video monitoring data through grid area division, and sends the historical safety accident data, the environmental data and the video monitoring data to the data preprocessing module;

[0062] The data preprocessing module is used for data preprocessing. Through data preprocessing, historical safety accident optimization data, environmental optimization data and monitoring image block data are obtained, and the environmental optimization data is sent to the environmental risk prediction module, and the historical safety accident optimization data and the monitoring image block data are sent to the accident risk prediction module;

[0063] The environmental risk prediction module is used for environmental risk prediction, obtains environmental risk prediction information through environmental risk prediction, and sends the environmental risk prediction information to the accident risk prediction module and the construction safety management optimization module;

[0064] The accident risk prediction module is used for accident risk prediction, obtains accident risk prediction information through accident risk prediction, and sends the accident risk prediction information to the construction safety management optimization module;

[0065] The construction safety management optimization module is used for construction safety management optimization, and through the construction safety management optimization, construction safety management prediction auxiliary information and a construction safety management optimization report are obtained.

[0066] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0067] (1) In the existing environmental risk prediction process, the changes in data such as temperature, humidity, and dust concentration in the environment often have trends on different time scales. The traditional environmental prediction model only considers single-scale features, ignores the information complementarity between different scales, and cannot effectively capture multi-scale environmental changes, resulting in poor prediction accuracy. This solution creatively uses a self-attention multi-scale convolutional long short-term memory model for environmental risk prediction, comprehensively captures information of different scales in environmental-related data, effectively handles long-term dependencies, and enhances attention to critical moments, thereby accurately capturing important risk signals, improving prediction accuracy and sensitivity, and helping to improve the overall effectiveness and reliability of the method.

[0068] (2) In the existing accident risk prediction process, the accident risk at the construction site needs to be analyzed by comprehensively considering environmental changes, historical safety accidents and real-time monitoring. However, the traditional accident risk prediction usually relies on a single data source and cannot fully consider information in multiple dimensions, resulting in a technical problem of reduced prediction accuracy. This solution creatively adopts a dual-path multimodal deep neural network model for accident risk prediction, effectively processes multimodal data related to accident risks, comprehensively captures various factors affecting accident risks, and enhances the accuracy of prediction results. In addition, by processing different types of data through dual paths, the model calculation efficiency is improved, which helps to timely discover potential safety hazards at the construction site.

[0069] (3) In the process of optimizing the existing grid-based construction safety management, it is difficult to accurately predict and intervene in the potential risks in different areas of the construction site in the existing construction site management process. The main safety hazards at the construction site come from environmental changes and construction site accidents, which leads to the technical problem that traditional safety management lacks regionalization, precise prediction and real-time adjustment. This solution creatively adopts two steps, environmental risk prediction and accident risk prediction, to assist in the optimization of grid-based construction safety management. Through the prediction of environmental risk and accident risk at two levels, it can realize dynamic safety assessment and targeted inspection of each area under the grid management framework, thereby optimizing resource allocation and inspection efficiency, reducing safety hazards, improving the accuracy, response speed and visualization of construction safety management, and ensuring that the safety management of each grid area is more scientific and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of the process of the construction safety grid management optimization method based on artificial intelligence provided by the present invention;

[0071] Figure 2 A schematic diagram of the construction safety grid management optimization system based on artificial intelligence provided by the present invention;

[0072] Figure 3 is a schematic flow chart of step S3;

[0073] Figure 4 It is a schematic diagram of the process of step S4.

[0074] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0075] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0076] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0077] Example 1, see Figure 1 The invention provides an artificial intelligence-based construction safety grid management optimization method, which comprises the following steps:

[0078] Step S1: grid area division;

[0079] Step S2: data preprocessing;

[0080] Step S3: Environmental risk prediction;

[0081] Step S4: accident risk prediction;

[0082] Step S5: Optimization of construction safety management.

[0083] Example 2, see Figure 1 , This embodiment is based on the above embodiment. In step S1, the grid area division is used to divide the grid area and collect data. Specifically, the construction site is divided into N grid areas according to the distribution of the work area, and the historical safety accident data of the construction site is collected. The environmental data and video monitoring data in the grid area are collected by sensors;

[0084] The historical safety accident data, including accident date, location, category and level;

[0085] The environmental data, including temperature, humidity, light, noise and dust concentration

[0086] The grid area includes construction work area, storage area, passage area, personnel concentrated activity area and fire safety area.

[0087] Example 3, see Figure 1 This embodiment is based on the above embodiment. In step S2, the data preprocessing is used to optimize the data quality, including the following steps:

[0088] Step S21: preprocessing the historical safety accident data, specifically, performing data cleaning and normalization on the historical safety accident data, and label encoding the type data in the historical safety accident data to obtain historical safety accident optimized data;

[0089] Step S22: preprocessing the environmental data, specifically, obtaining environmental optimization data by performing data cleaning, time alignment and normalization on the environmental data;

[0090] Step S23: Video surveillance data preprocessing, specifically extracting image frames from the video surveillance data at a rate of one frame per second, and denoising and standardizing each frame of the image to obtain surveillance standard image data, and dividing all images in the surveillance standard image data into G fixed-size image blocks to obtain surveillance image block data.

[0091] Example 4, see Figure 1 and Figure 3 , this embodiment is based on the above embodiment. In step S3, the environmental risk prediction is used to predict environmental risks, specifically, based on environmental optimization data, a self-attention multi-scale convolutional long short-term memory model is used to predict environmental risks to obtain environmental risk prediction information;

[0092] The self-attention multi-scale convolutional long short-term memory model includes an input layer, a multi-scale convolutional subnet, a random inactivation layer, a long short-term memory layer, a self-attention layer and an output layer;

[0093] The multi-scale convolutional subnet is used to capture multi-scale features from the data;

[0094] The long short-term memory layer is used to capture long-term and short-term dependencies from data;

[0095] The self-attention layer is used to focus on key time steps;

[0096] The environmental risk prediction includes the following steps:

[0097] Step S31: constructing a self-attention parallel convolutional long short-term memory model, including the following steps:

[0098] Step S311: constructing an input layer for receiving input data, specifically receiving environmental optimization data as input data of the model through the input layer;

[0099] Step S312: construct a multi-scale convolutional subnet, specifically, setting a parallel first convolutional layer, a second convolutional layer, and a third convolutional layer in the multi-scale convolutional subnet, and setting an average pooling layer after each convolutional layer, and splicing the output results of each average pooling layer through a feature splicing layer to obtain multi-scale features of the environment, wherein the first convolutional layer contains 16 4×1 convolutional kernels, the second convolutional layer contains 32 4×5 convolutional kernels, and the third convolutional layer contains 64 4×5 convolutional kernels;

[0100] Step S313: construct a random dropout layer to avoid model overfitting. Specifically, the environment multi-scale features are processed through the random dropout layer to obtain the environment random dropout features.

[0101] Step S314: constructing a long short-term memory layer, specifically setting 295 long short-term memory units in the long short-term memory layer, processing the random inactivation characteristics of the environment through the long short-term memory layer, and obtaining the long short-term time series characteristics of the environment;

[0102] The long short-term memory unit includes a forget gate, an input gate, an output gate and a memory unit;

[0103] Step S315: construct a self-attention layer. Specifically, in the self-attention layer, the self-attention mechanism is used to process the long-term and short-term time series features of the environment to obtain the self-attention features of the environment. The calculation formula is:

[0104] ;

[0105] Where Q is the query matrix, h is the long-term and short-term time series features of the environment, the long-term and short-term time series features are a matrix of u×d, u is the total number of time steps of the long-term and short-term time series features of the environment, d is the dimension of the long-term and short-term time series features of the environment, and W Q is a d×d query weight matrix, K is the key matrix, W K is a d×d key weight matrix, V is a value matrix, W V is a d×d value weight matrix, Att is the self-attention score, softmax(·) is the softmax activation function, T is the transpose operation, and h att is the environmental self-attention feature;

[0106] Step S316: construct an output layer for outputting the model prediction results, specifically, processing the environment self-attention features through a fully connected layer and a linear activation function to obtain the model prediction results;

[0107] Step S32: constructing an environmental risk prediction model, specifically, training a self-attention parallel convolutional long short-term memory model based on environmental optimization data, and using a particle swarm optimization algorithm to tune model parameters to obtain an environmental risk prediction model;

[0108] Step S33: Generate environmental risk prediction information, specifically, perform environmental risk prediction on each grid area through an environmental risk prediction model to obtain environmental risk prediction information, wherein the environmental risk prediction information includes environmental risk values ​​and main environmental risk factors;

[0109] By performing the above operations, in view of the fact that in the existing environmental risk prediction process, changes in data such as temperature, humidity, dust concentration in the environment often have trends on different time scales, and the traditional environmental prediction model only considers single-scale features, ignores the information complementarity between different scales, and cannot effectively capture multi-scale environmental changes, resulting in poor prediction accuracy. Technical problems, this scheme creatively uses the self-attention multi-scale convolutional long short-term memory model for environmental risk prediction, comprehensively captures information of different scales in environmental-related data, effectively handles long-term dependencies, and enhances attention to critical moments, thereby accurately capturing important risk signals, improving prediction accuracy and sensitivity, and helping to improve the overall effectiveness and reliability of the method.

[0110] Example 5, see Figure 1 and Figure 4, this embodiment is based on the above embodiment. In step S4, the accident risk prediction is used to predict the accident risk, specifically, based on the environmental risk prediction information, the historical safety accident optimization data and the monitoring image block data, a dual-path multimodal deep neural network model is used to perform accident risk prediction to obtain accident risk prediction information;

[0111] The dual-path multimodal deep neural network model includes a dual-path feature processing subnet, a multimodal feature fusion layer and a prediction output layer;

[0112] The dual-path feature processing subnet is used to process feature information of multimodal data in parallel;

[0113] The multimodal feature fusion layer is used to fuse multimodal feature information;

[0114] The accident risk prediction includes the following steps:

[0115] Step S41: constructing a dual-path feature processing subnet, specifically setting a parallel first feature processing path and a second feature processing path in the dual-path feature processing subnet, including the following steps:

[0116] Step S411: constructing a first feature processing path, specifically setting a random forest model in the first feature processing path, processing environmental risk prediction information and historical safety accident optimization data through the first feature processing path, and obtaining environmental accident features;

[0117] Step S412: constructing a second feature processing path, specifically setting a flattened embedding layer, an image visual transformer model, a long short-term memory layer, and an average pooling layer in the second feature processing path, processing the monitoring image block data through the second feature processing path, and obtaining monitoring image features, including the following steps:

[0118] Step S4121: construct a flattened embedding layer, specifically, flatten each monitoring image block in the monitoring image block data into a vector, and perform linear mapping on the vector to obtain the monitoring image block mapping feature, and the calculation formula is:

[0119] ;

[0120] In the formula, z i is the mapping feature of the i-th monitoring image block, i is the monitoring image block index, W p is the linear mapping weight, x i is the flattened vector of the ith monitoring image block, b p is the linear mapping bias term;

[0121] Step S4121: constructing a visual transformer model, specifically, setting 24 layers of encoders in the visual transformer model, and setting 16 self-attention heads in each layer, and processing the monitoring image block mapping features through the visual transformer model to obtain the monitoring image global features;

[0122] Step S4122: constructing a long short-term memory layer, setting 295 long short-term memory units in the long short-term memory layer, processing the global features of the monitoring image through the long short-term memory layer, and obtaining the temporal features of the monitoring image;

[0123] Step S4123: construct an average pooling layer for aggregating features in the time dimension. Specifically, the monitoring image temporal features are processed by the average pooling layer to obtain the monitoring average pooling features. The calculation formula is:

[0124] ;

[0125] In the formula, f G is the monitoring average pooling feature, t max is the maximum time step, t is the time step index, c t is the temporal feature of the monitoring image at the tth time step;

[0126] Step S42: construct a multimodal feature fusion layer, specifically, weighted fusion of environmental accident features and monitoring average pooling features to obtain accident risk features. The calculation formula is:

[0127] ;

[0128] In the formula, f total is the accident risk characteristic, is the first weighting coefficient, which is used to represent the weight of environmental accident characteristics, f env It is the characteristic of environmental accidents. is the second weighting coefficient, which is used to represent the monitoring average pooling feature weight;

[0129] Step S43: constructing a prediction output layer, specifically processing the accident risk features through a fully connected layer and a softmax activation function to obtain the final prediction result;

[0130] Step S44: constructing a dual-path multimodal deep neural network model, specifically by constructing a dual-path multimodal deep neural network model through the construction of a dual-path feature processing subnet, the construction of a multimodal feature fusion layer, and the construction of a prediction output layer;

[0131] Step S45: constructing an accident risk prediction model, specifically, training a dual-path multimodal deep neural network model based on environmental risk prediction information, historical safety accident optimization data, and monitoring image block data, and using a particle swarm optimization algorithm to tune model parameters to obtain an accident risk prediction model;

[0132] Step S46: generating accident risk prediction information, specifically, performing accident risk prediction through an accident risk prediction model to obtain accident risk prediction information, wherein the accident risk prediction information includes an accident risk level and major accident risk factors;

[0133] By performing the above operations, in the existing accident risk prediction process, the accident risks at the construction site need to be analyzed by comprehensively considering environmental changes, historical safety accidents and real-time monitoring. However, traditional accident risk prediction usually relies on a single data source and cannot fully consider information in multiple dimensions, resulting in a technical problem of reduced prediction accuracy. This solution creatively uses a dual-path multimodal deep neural network model to predict accident risks, effectively processes multimodal data related to accident risks, comprehensively captures various factors affecting accident risks, and enhances the accuracy of prediction results. In addition, by processing different types of data through dual paths, the model calculation efficiency is improved, which helps to timely discover potential safety hazards at construction sites.

[0134] Example 6, see Figure 1 This embodiment is based on the above embodiment. In step S5, the construction safety management optimization is used to optimize the grid management of construction safety. Specifically, based on the accident risk prediction information and the environmental risk prediction information, the construction safety comprehensive management optimization is performed to obtain construction safety management prediction auxiliary information, and based on the construction safety management prediction auxiliary information, grid construction site safety inspections, data aggregation and visual comprehensive analysis are performed to generate a construction safety management optimization report to help construction safety managers perform safety management and decision-making in the grid area;

[0135] By performing the above operations, in the process of optimizing the existing grid-based construction safety management, it is difficult to accurately predict and intervene in the potential risks in different areas of the construction site during the existing construction site management process. The main safety hazards at the construction site come from environmental changes and construction site accidents, which leads to the technical problem that traditional safety management lacks regionalization, precise prediction and real-time adjustment. This solution creatively adopts two steps, environmental risk prediction and accident risk prediction, to assist in the optimization of grid-based construction safety management. Through the prediction of environmental risk and accident risk at two levels, it can realize dynamic safety assessment and targeted inspection of each area under the grid management framework, thereby optimizing resource allocation and inspection efficiency, reducing safety hazards, improving the accuracy, response speed and visualization of construction safety management, and ensuring that the safety management of each grid area is more scientific and efficient.

[0136] Embodiment 7, see Figure 2 , this embodiment is based on the above embodiment, and the artificial intelligence-based construction safety grid management optimization system provided by the present invention includes: a grid area division module, a data preprocessing module, an environmental risk prediction module, an accident risk prediction module and a construction safety management optimization module;

[0137] The grid area division module is used for grid area division, and obtains historical safety accident data, environmental data and video monitoring data through grid area division, and sends the historical safety accident data, the environmental data and the video monitoring data to the data preprocessing module;

[0138] The data preprocessing module is used for data preprocessing. Through data preprocessing, historical safety accident optimization data, environmental optimization data and monitoring image block data are obtained, and the environmental optimization data is sent to the environmental risk prediction module, and the historical safety accident optimization data and the monitoring image block data are sent to the accident risk prediction module;

[0139] The environmental risk prediction module is used for environmental risk prediction, obtains environmental risk prediction information through environmental risk prediction, and sends the environmental risk prediction information to the accident risk prediction module and the construction safety management optimization module;

[0140] The accident risk prediction module is used for accident risk prediction, obtains accident risk prediction information through accident risk prediction, and sends the accident risk prediction information to the construction safety management optimization module;

[0141] The construction safety management optimization module is used for construction safety management optimization, and through the construction safety management optimization, construction safety management prediction auxiliary information and a construction safety management optimization report are obtained.

[0142] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device.

[0143] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0144] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. The construction safety grid management optimization method based on artificial intelligence is characterized by: The method comprises the following steps: Step S1: grid area division; Step S2: data preprocessing; Step S3: Environmental risk prediction, which is used to predict environmental risks. Specifically, based on environmental optimization data, a self-attention multi-scale convolutional long short-term memory model is used to predict environmental risks to obtain environmental risk prediction information; The self-attention multi-scale convolutional long short-term memory model includes an input layer, a multi-scale convolutional subnet, a random inactivation layer, a long short-term memory layer, a self-attention layer and an output layer; The multi-scale convolutional subnet is used to capture multi-scale features from the data; The long short-term memory layer is used to capture long-term and short-term dependencies from data; The self-attention layer is used to focus on key time steps; Step S4: accident risk prediction, which is used to predict accident risks, specifically, based on environmental risk prediction information, historical safety accident optimization data and monitoring image block data, a dual-path multimodal deep neural network model is used to predict accident risks and obtain accident risk prediction information; The dual-path multimodal deep neural network model includes a dual-path feature processing subnet, a multimodal feature fusion layer and a prediction output layer; The dual-path feature processing subnet is used to process feature information of multimodal data in parallel; The multimodal feature fusion layer is used to fuse multimodal feature information; Step S5: Optimization of construction safety management.

2. The construction safety grid management optimization method based on artificial intelligence according to claim 1 is characterized by: In step S3, the environmental risk prediction includes the following steps: Step S31: constructing a self-attention parallel convolutional long short-term memory model, including the following steps: Step S311: constructing an input layer for receiving input data, specifically receiving environmental optimization data as input data of the model through the input layer; Step S312: construct a multi-scale convolutional subnet, specifically, setting a parallel first convolutional layer, a second convolutional layer, and a third convolutional layer in the multi-scale convolutional subnet, and setting an average pooling layer after each convolutional layer, and splicing the output results of each average pooling layer through a feature splicing layer to obtain multi-scale features of the environment, wherein the first convolutional layer contains 16 4×1 convolutional kernels, the second convolutional layer contains 32 4×5 convolutional kernels, and the third convolutional layer contains 64 4×5 convolutional kernels; Step S313: construct a random dropout layer to avoid model overfitting. Specifically, the multi-scale features of the environment are processed through the random dropout layer to obtain the random dropout features of the environment. Step S314: constructing a long short-term memory layer, specifically setting 295 long short-term memory units in the long short-term memory layer, processing the random inactivation characteristics of the environment through the long short-term memory layer, and obtaining the long short-term time series characteristics of the environment; The long short-term memory unit includes a forget gate, an input gate, an output gate and a memory unit; Step S315: construct a self-attention layer. Specifically, in the self-attention layer, the self-attention mechanism is used to process the long-term and short-term time series features of the environment to obtain the self-attention features of the environment. The calculation formula is: ； Where Q is the query matrix, h is the long-term and short-term time series features of the environment, the long-term and short-term time series features are a matrix of u×d, u is the total number of time steps of the long-term and short-term time series features of the environment, d is the dimension of the long-term and short-term time series features of the environment, and W Q is a d×d query weight matrix, K is the key matrix, W K is a d×d key weight matrix, V is a value matrix, W V is a d×d value weight matrix, Att is the self-attention score, softmax(·) is the softmax activation function, T is the transpose operation, and h att is the environmental self-attention feature; Step S316: construct an output layer for outputting the model prediction results, specifically, processing the environment self-attention features through a fully connected layer and a linear activation function to obtain the model prediction results; Step S32: constructing an environmental risk prediction model, specifically, training a self-attention parallel convolutional long short-term memory model based on environmental optimization data, and using a particle swarm optimization algorithm to tune model parameters to obtain an environmental risk prediction model; Step S33: Generate environmental risk prediction information, specifically, perform environmental risk prediction on each grid area through an environmental risk prediction model to obtain environmental risk prediction information.

3. The construction safety grid management optimization method based on artificial intelligence according to claim 2 is characterized by: In step S4, the accident risk prediction includes the following steps: Step S41: constructing a dual-path feature processing subnet, specifically setting a parallel first feature processing path and a second feature processing path in the dual-path feature processing subnet, including the following steps: Step S411: constructing a first feature processing path, specifically setting a random forest model in the first feature processing path, processing environmental risk prediction information and historical safety accident optimization data through the first feature processing path, and obtaining environmental accident features; Step S412: constructing a second feature processing path, specifically setting a flattened embedding layer, an image visual transformer model, a long short-term memory layer, and an average pooling layer in the second feature processing path, processing the monitoring image block data through the second feature processing path, and obtaining monitoring image features, including the following steps: Step S4121: construct a flattened embedding layer, specifically, flatten each monitoring image block in the monitoring image block data into a vector, and perform linear mapping on the vector to obtain the monitoring image block mapping feature, and the calculation formula is: ； In the formula, z i is the mapping feature of the i-th monitoring image block, i is the monitoring image block index, W p is the linear mapping weight, x i is the flattened vector of the ith monitoring image block, b p is the linear mapping bias term; Step S4121: constructing a visual transformer model, specifically, setting 24 layers of encoders in the visual transformer model, and setting 16 self-attention heads in each layer, and processing the monitoring image block mapping features through the visual transformer model to obtain the monitoring image global features; Step S4122: constructing a long short-term memory layer, setting 295 long short-term memory units in the long short-term memory layer, processing the global features of the monitoring image through the long short-term memory layer, and obtaining the temporal features of the monitoring image; Step S4123: construct an average pooling layer for aggregating features in the time dimension. Specifically, the monitoring image temporal features are processed by the average pooling layer to obtain the monitoring average pooling features. The calculation formula is: ； In the formula, f G is the monitoring average pooling feature, t max is the maximum time step, t is the time step index, c t is the temporal feature of the monitoring image at the tth time step; Step S42: construct a multimodal feature fusion layer, specifically, weighted fusion of environmental accident features and monitoring average pooling features to obtain accident risk features. The calculation formula is: ； In the formula, f total is the accident risk characteristic, is the first weighting coefficient, which is used to represent the weight of environmental accident characteristics, f env It is the characteristic of environmental accidents. is the second weighting coefficient, which is used to represent the monitoring average pooling feature weight; Step S43: constructing a prediction output layer, specifically processing the accident risk features through a fully connected layer and a softmax activation function to obtain the final prediction result; Step S44: constructing a dual-path multimodal deep neural network model, specifically by constructing a dual-path multimodal deep neural network model through the construction of a dual-path feature processing subnet, the construction of a multimodal feature fusion layer, and the construction of a prediction output layer; Step S45: constructing an accident risk prediction model, specifically, training a dual-path multimodal deep neural network model based on environmental risk prediction information, historical safety accident optimization data, and monitoring image block data, and using a particle swarm optimization algorithm to tune model parameters to obtain an accident risk prediction model; Step S46: Generate accident risk prediction information, specifically, perform accident risk prediction through an accident risk prediction model to obtain accident risk prediction information.

4. The construction safety grid management optimization method based on artificial intelligence according to claim 3 is characterized by: In step S1, the grid area division is used to divide the grid area and collect data. Specifically, the construction site is divided into N grid areas according to the distribution of the work area, and the historical safety accident data of the construction site is collected. The environmental data and video surveillance data in the grid area are collected through sensors.

5. The construction safety grid management optimization method based on artificial intelligence according to claim 4 is characterized by: In step S2, the data preprocessing is used to optimize the data quality, including the following steps: Step S21: preprocessing the historical safety accident data, specifically, performing data cleaning and normalization on the historical safety accident data, and label encoding the type data in the historical safety accident data to obtain historical safety accident optimized data; Step S22: preprocessing the environmental data, specifically, obtaining environmental optimization data by performing data cleaning, time alignment and normalization on the environmental data; Step S23: Video surveillance data preprocessing, specifically extracting image frames from the video surveillance data at a rate of one frame per second, and denoising and standardizing each frame of the image to obtain surveillance standard image data, and dividing all images in the surveillance standard image data into G fixed-size image blocks to obtain surveillance image block data.

6. The construction safety grid management optimization method based on artificial intelligence according to claim 5 is characterized by: In step S5, the construction safety management optimization is used to optimize the grid management of construction safety. Specifically, based on the accident risk prediction information and the environmental risk prediction information, the comprehensive management optimization of construction safety is performed to obtain construction safety management prediction auxiliary information, and by performing grid construction site safety inspections, data aggregation and visual comprehensive analysis based on the construction safety management prediction auxiliary information, a construction safety management optimization report is generated to help construction safety managers perform safety management and decision-making within the grid area.

7. An artificial intelligence-based construction safety grid management optimization system, used to implement the artificial intelligence-based construction safety grid management optimization method as described in any one of claims 1 to 6, characterized in that: It includes grid area division module, data preprocessing module, environmental risk prediction module, accident risk prediction module and construction safety management optimization module.

8. The construction safety grid management optimization system based on artificial intelligence according to claim 7 is characterized by: The grid area division module is used for grid area division, and obtains historical safety accident data, environmental data and video monitoring data through grid area division, and sends the historical safety accident data, the environmental data and the video monitoring data to the data preprocessing module; The data preprocessing module is used for data preprocessing. Through data preprocessing, historical safety accident optimization data, environmental optimization data and monitoring image block data are obtained, and the environmental optimization data is sent to the environmental risk prediction module, and the historical safety accident optimization data and the monitoring image block data are sent to the accident risk prediction module; The environmental risk prediction module is used for environmental risk prediction, obtains environmental risk prediction information through environmental risk prediction, and sends the environmental risk prediction information to the accident risk prediction module and the construction safety management optimization module; The accident risk prediction module is used for accident risk prediction, obtains accident risk prediction information through accident risk prediction, and sends the accident risk prediction information to the construction safety management optimization module; The construction safety management optimization module is used for construction safety management optimization, and through the construction safety management optimization, construction safety management prediction auxiliary information and a construction safety management optimization report are obtained.

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