Concealed project acceptance method based on intelligent safety helmet

Abstract

The invention relates to the technical field of hidden project acceptance, and discloses a hidden project acceptance method based on an intelligent safety helmet, and the method comprises the following specific steps: integrating a sensor and collecting data, arranging a high-precision sensor in the safety helmet for collecting structure and environment data, and ensuring the accuracy of the position of a worker through a GPS positioning technology. Capturing an internal image of the wall body through an AI camera, wherein the internal image comprises an environment condition video; real-time data analysis is carried out, information such as steel bar positions and wall structures is analyzed in real time, and instant feedback and decision support are provided through rapid data analysis. On the basis, through innovative technical means of high-precision data acquisition, real-time data analysis, acceptance flow optimization, data security improvement, augmented reality, voice control interaction and the like, the management efficiency and security of the construction site are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of concealed engineering acceptance, and specifically to a concealed engineering acceptance method based on an intelligent safety helmet. Background Art

[0002] Hidden work inspection and verification refers to the quality inspection and verification of the covered portions of a building or structure during construction. These typically include concrete pouring, piping installation, and wiring. Once covered, subsequent inspection and repair become extremely difficult. Therefore, hidden work inspection and verification is a crucial step in ensuring construction quality and safety.

[0003] The traditional acceptance method for concealed works mainly relies on regular inspections and record keeping by on-site supervisors. Supervisors regularly visit the construction site to inspect according to the construction progress and plan, use tools such as rulers and measuring instruments to measure and record, and record the inspection results in paper forms or reports, which are submitted to project managers for review and archiving. If problems are found, supervisors will notify the construction unit to make rectifications and conduct re-inspections in subsequent inspections.

[0004] Traditional concealed project acceptance relies on manual inspection and record-keeping, which is prone to human error and leads to inaccurate data. Furthermore, the lack of real-time data collection and transmission means that data cannot be updated and adjusted in a timely manner, significantly compromising its timeliness and accuracy. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for concealed project acceptance based on a smart helmet. This method solves the problem that traditional concealed project acceptance relies on manual inspection and recording, which is prone to human error and leads to inaccurate data. Furthermore, due to the lack of real-time data collection and transmission methods, data cannot be updated and adjusted in a timely manner, which greatly reduces the timeliness and accuracy of the data.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions:

[0007] A concealed engineering acceptance method based on a smart helmet, the specific steps are as follows:

[0008] S1: Integrated sensors and data acquisition: A high-precision sensor is built into the helmet to collect structural and environmental data. GPS positioning technology is used to ensure the accuracy of the worker's position. An AI camera captures images of the interior of the wall and also includes videos of environmental conditions.

[0009] S2: Real-time data analysis, real-time analysis of information such as steel bar location and wall structure, providing immediate feedback and decision support through rapid data analysis;

[0010] S3: AI-driven acceptance process, where AI automatically identifies and accepts structural integrity and specification consistency. A smart reminder and warning system also identifies any non-conformities. When a safety helmet is activated, it alerts the worker or supervisor to verify and correct them.

[0011] S4: Visualization support: Displays real-time analysis results through the smart hat's display or communication equipment to simplify the acceptance process. It also stores and displays historical data of concealed projects for easy auditing and comparison.

[0012] S5: Data recording and storage, which transmits data to the cloud via wireless connection for long-term storage and retrieval, and implements permission control to manage data access and protect sensitive information;

[0013] S6: Remote monitoring and collaboration: Project managers can remotely view the situation and provide guidance on the acceptance process via the Internet, thus achieving real-time collaboration and communication among multiple users at multiple locations.

[0014] S7: Augmented reality (AR) support, using AR technology to display real-time images and data overlays on the worker's hard hat. AR guidance can provide step-by-step instructions, standard comparisons, and highlight problem areas.

[0015] S8: Voice control and interaction, integrated voice recognition technology, allows workers to operate the functions of the helmet through voice commands, and the voice feedback system can automatically play warnings or prompts when problems are detected.

[0016] Preferably, the algorithm formula for collecting sensor data in S1 is described as follows:

[0017] Assume that the data collected by the sensor is D s , the timestamp is t, then the sensor data is represented as:

[0018] D s =f(t)

[0019] Among them, the function f is used to associate time and sensor data, and obtain continuous time series data through integration or difference calculation;

[0020] In this formula: sensor data usually changes over time, so a timestamp t is needed to record each data point. The function f is linear, which describes how the sensor reading changes over time. The integral can be used to estimate the overall change over a period of time, while the difference is used to calculate the rate of change between adjacent time points.

[0021] The triangulation formula of GPS positioning is described as follows:

[0022] Assume the position coordinates of the GPS receiver are (x, y) and the coordinates of the satellite are The distance is d, and the calculation formula is as follows:

[0023]

[0024] In this formula: at least three different satellite signals are required to determine the position of the receiver. By solving the equations, the precise position of the receiver can be obtained, and the Euclidean distance formula is used to calculate the distance between the receiver and the satellite.

[0025] The Euclidean distance formula is the basic method for calculating the straight-line distance between two points in multidimensional space. The formula is as follows:

[0026]

[0027] Where A=(x1,x2,…,x n ) and B=(y1,y2,…,y n ) are the coordinates of two points in n-dimensional space;

[0028] The visual positioning technology formula is described as follows:

[0029] Let the feature point set of the input image be P i =[x i ,y i ], the feature point set of the target image is P j =[x j ,y j ], the matching degree calculation formula is as follows:

[0030]

[0031] In this formula: the key feature points extracted from the image are used for matching and recognition. The similarity between the two images is measured by calculating the sum of the squares of the distances between the feature points. A smaller M value means a higher matching degree.

[0032] Preferably, the real-time data analysis described in S2 is described as follows:

[0033] Canny edge detection algorithm:

[0034] Suppose the input image is I(x,y), the image after Gaussian filtering is G(x,y), the gradient amplitude is M(x,y), and the gradient direction is θ(x,y), then the steps of Canny edge detection are as follows:

[0035] Gaussian filtering: Reduce image noise:

[0036] G(x,y)=I(x,y)*H(x,y)

[0037] Where H(x,y) is the Gaussian kernel;

[0038] Compute the gradient magnitude and direction:

[0039]

[0040] Non-maximum suppression: retain local maxima;

[0041] Dual threshold detection: uses two thresholds, high and low, to determine edges;

[0042] Edge connection: connect edges by hysteresis threshold;

[0043] In this formula:

[0044] Gaussian filtering: used to smooth images and reduce noise effects;

[0045] Gradient calculation: Determine the rate and direction of change of intensity for each pixel in the image;

[0046] Non-maximum suppression: ensures that only local maxima are retained, thereby refining edges;

[0047] Dual-threshold detection: By setting two thresholds to distinguish strong edges from weak edges, the detection accuracy is improved;

[0048] Edge connection: connect discontinuous edge fragments into complete edge lines;

[0049] The texture analysis algorithm formula is described as follows:

[0050] Assume that the input image is I(x,y) and the response of the Gabor filter is R(x,y). The steps of texture analysis are as follows:

[0051] The expression of Gabor filter is:

[0052]

[0053] in, is the space coordinate vector, k μ,u and σ are parameters that control the direction and scale of the filter;

[0054] Among them, k u is the maximum frequency, u is the scale factor;

[0055] Among them, μ is the direction index, which realizes the direction angle φ in the first formula μ ;

[0056] By combining the scale factor and maximum frequency with the direction angle, a complex expression is generated. This expression directly affects how the filter captures features of a specific direction and scale in image processing, so that each possible μ value corresponds to a specific, quantized angle, allowing the filter to cover multiple directions and enhance its ability to respond to multi-directional features in the image;

[0057] In this formula:

[0058] is the normalization factor used to adjust the amplitude of the filter;

[0059] is a Gaussian function used to control the spatial expansion of the filter;

[0060] is a complex exponential function minus a constant term, used to generate the oscillation pattern;

[0061] In this formula: Gabor filter is used to extract local features of the image. In texture analysis and edge detection, by adjusting the parameters μ and u, filter responses in different directions and scales can be obtained, thereby capturing different features of the image.

[0062] Preferably, the algorithm description of the AI-driven acceptance process described in S3 is as follows:

[0063] Rule engine algorithm:

[0064] Assuming the structural integrity standard is S and the specification consistency standard is C, the evaluation function of the rule engine is:

[0065]

[0066] Anomaly Detection Algorithms:

[0067] Assume that the data set is D = |{d1,d2,…,d n}, the anomaly score is a i , then the calculation formula of the anomaly score of the isolation forest is:

[0068]

[0069] where h(d i ) is the height of the tree, c(n) is the average path length of the tree;

[0070] In this formula:

[0071] Rules engine: evaluates structural integrity and specification consistency based on predefined rules;

[0072] Anomaly Detection: Identify outliers by building multiple random trees and calculating the average path length of samples. A smaller anomaly score indicates a higher probability of anomaly.

[0073] Preferably, regarding the algorithm formula and analysis of the visualization support described in S4:

[0074] Real-time data collection:

[0075] Let the real-time data collected at time t be D(t), which is a vector or matrix, depending on the dimension of the data, for example:

[0076] D(t)=[d1(t),d2(t),…,d n (t)]

[0077] where d i (t) represents the value of the i-th data point at time t;

[0078] Real-time analytics:

[0079] The analysis function f processes the real-time data D(t) and generates the analysis result R(t). The function f is analyzed to determine the form of the function.

[0080] To make this concrete, let f be a simple linear transformation:

[0081] R(t)=f(D(t))=W·D(t)+b

[0082] Where W is the weight matrix and b is the bias vector;

[0083] Results:

[0084] The display conversion function g converts the analysis result R(t) into a form V(t) displayed on a display screen or communication device. Assuming that the display form is a scalar value or an image, then g is a simple scalar conversion or image processing function. For example, if R(t) is a scalar value, g can be a simple threshold function:

[0085]

[0086] Where θ is the threshold;

[0087] Historical data storage and retrieval, historical data sets:

[0088] The historical dataset H contains all historical records, each record contains a timestamp and analysis results:

[0089] H={h1,h2,…,h n}

[0090] Each h iIs a record containing a timestamp and analysis results, such as h i =(t i ,R(t i ))

[0091] Storing new data:

[0092] When the new analysis result R(t) is added to the historical data set H, a new record h is formed. n+1 =(t,R(t))

[0093] Retrieve historical data:

[0094] For auditing or comparison purposes, specific records or the entire dataset can be retrieved from the historical dataset H;

[0095] Audit and Compare: Audit Function;

[0096] Audit function A returns an audit report that includes data integrity checks, anomaly detection, etc.

[0097] For example, the comparison function:

[0098] The comparison function C returns a comparison result that indicates how different or similar R(t) is compared to the records in the historical dataset H, for example:

[0099] C(R(t),H)=Comparison Result(R(t),H)

[0100] In this formula, through specific mathematical formulas and process descriptions, we can clearly see how to obtain analysis results from real-time data through analysis functions, then display the results through display conversion functions, and store and display historical data of hidden projects for auditing and comparison.

[0101] Preferably, the algorithm formula for recording and storing the data in S5 is described as follows:

[0102] Data transmission and encryption:

[0103] Assume that the transmitted data is T(t), the key is K, and the encrypted data transmission is E(t), then the encryption formula is:

[0104]

[0105] The key K is a 256-bit random number generated by a secure hash algorithm to ensure the uniqueness and security of each encryption;

[0106] In this formula, in order to protect the confidentiality of data during data transmission, a strong encryption algorithm is used to encrypt the data to ensure the security of the data;

[0107] Data storage and permission control:

[0108] Assume that the data received by the cloud server is S(t), the user role set is U, and the permission matrix is P. The permission control is expressed as follows:

[0109] S i =P u [i]·S[i]

[0110] Among them, P u [i] represents the access permission for the i-th data;

[0111] In this approach, after receiving the data, the cloud server stores it in the database. To protect sensitive information, data access needs to be controlled by permissions. A role-based access control model is used to define different user roles and their corresponding permissions. For example, administrators can access and modify all data, while ordinary users can only access specific data.

[0112] Preferably, the specific steps of the remote monitoring and collaboration in S6 are as follows:

[0113] Establish network connection: Establish a stable end-to-end connection with the remote server through the wireless network interface to ensure reliable transmission of data packets;

[0114] Data collection and preliminary processing: Use sensors mounted on the helmet to collect environmental and structural data, including but not limited to temperature, humidity, location, and image data. Apply data preprocessing algorithms, such as sliding average filters, to denoise the collected signals and improve data accuracy.

[0115] Video stream compression: Implement the H.264 video coding standard to compress the raw video stream captured by the AI camera to reduce the amount of data transmission. This process involves DCT transformation and quantization steps, specifically:

[0116]

[0117] Among them, f(u,v) is the frequency domain coefficient after transformation, which represents the amplitude of the component with frequency (u,v);

[0118] α u and α v are normalization factors used to ensure energy conservation in the transformation. Their values depend on u and v. If u = 0 or v = 0, then and If ueq 0 and veq 0, then and

[0119] x and y are pixel coordinates in the spatial domain, ranging from 0 to m-1 and 0 to n-1;

[0120] i(x,y) is the pixel value of the original image at position (x,y);

[0121] π is the circumference constant, approximately equal to 3.14159;

[0122] m and n are the sizes of the image blocks, usually 4x4 or 8x8;

[0123] In this formula, the DCT coefficients of each frequency component (u, v) are calculated by multiplying each pixel value i(x, y) in the image block with the corresponding cosine function and summing them. These frequency domain coefficients are then subjected to a quantization step to further reduce the amount of data and prepare for entropy coding.

[0124] Encryption and secure transmission:

[0125] The AES encryption algorithm is used to protect the security of transmitted data and prevent data from being intercepted and tampered during transmission. The encryption process is simplified as follows:

[0126] encrypted_data=aes_encrypt(plain_data,key)

[0127] Use the TLS protocol to add an encryption layer to the TCP connection to ensure the integrity and confidentiality of data during Internet transmission;

[0128] Real-time data analysis and feedback:

[0129] Deploy a real-time data analysis module on a remote server and use the canny edge detection algorithm to extract features from received video frames for further analysis and decision making;

[0130] Based on the analysis results, feedback instructions or adjustment instructions are sent to the on-site helmets through encrypted communication channels to guide on-site operations;

[0131] Through the above steps, the method can ensure that project managers can remotely view the actual situation through the network and provide guidance for the acceptance process, thereby realizing real-time collaboration and communication among multiple locations and multiple users.

[0132] Preferably, the key steps and technologies for supporting augmented reality (AR) in S7 are described as follows:

[0133] Image recognition and processing: Extract key feature points from the input image. These feature points are used for subsequent matching and overlay operations;

[0134] The formula is described as: Let the feature point set of the input image be P i ={p1,p2,…,pn}, the feature point set of the target image is Q j ={q1,q2,…,q m}, then the matching degree calculation formula is:

[0135]

[0136] Where i is the feature point index of the input image, j is the feature point index of the target image, and a smaller value means a higher matching degree;

[0137] Real-time rendering and overlay: 3D model loading and conversion, loading 3D models into the AR environment as needed and performing necessary coordinate conversion;

[0138] Assume that the vertex coordinates of the 3D model are {v1,v2,…,v k}, and the transformed coordinates are {u1,u2,…,u k}, the conversion formula is:

[0139] u=transform(v)

[0140] Among them, transform is a function that converts 2D coordinates into 3D space coordinates;

[0141] Perspective transformation: Use perspective transformation to project the 3D model onto a 2D plane. The perspective transformation matrix is:

[0142]

[0143] Where f is the focal length, c x and c y are the center point coordinates, and by multiplying the 3D coordinates by this matrix, they can be converted to 2D coordinates;

[0144] AR guidance and interaction: Based on the analysis results, problem areas are highlighted, and specific areas are highlighted using color filters or marker boxes.

[0145] Preferably, the algorithm formula and description of the voice control and interaction in S8 are as follows:

[0146] Speech recognition module:

[0147] Input: real-time audio stream audio_stream, output: recognized text recognized_text;

[0148] The algorithm steps of the speech recognition module are:

[0149] Assuming the speech recognition engine is R, the preprocessing function is P, and the transcription function is T, the speech recognition process can be expressed as:

[0150] recognized_text=T(P(audio_stream))

[0151] Among them, the preprocessing function P performs noise reduction and enhancement on the audio stream, and the transcription function T converts the processed audio into text;

[0152] Command parsing and execution module:

[0153] Input: recognized text recognized_text, Output: operation command command;

[0154] The algorithm steps of the command parsing and execution module: Assuming the command library is C and the keyword matching function is K, the command parsing process can be expressed as:

[0155] command=K(recognized_text,C)

[0156] The keyword matching function K searches for keywords in the command library C in the identified text and returns the corresponding operation instructions;

[0157] Environmental monitoring and feedback module:

[0158] Input: operation command command, output: environmental parameters environmental_parameters; warning message warning_message;

[0159] The algorithm steps of the environmental monitoring and feedback module are as follows:

[0160] Let the environmental data acquisition function be E and the anomaly detection function be A;

[0161] The process of environmental monitoring and feedback can be expressed as:

[0162] (E(command),A(E(command)))

[0163] The environmental data acquisition function E obtains relevant environmental data according to the operation instructions, and the anomaly detection function A analyzes these data and generates warning information;

[0164] Warning and prompt playback module:

[0165] Input: warning message warning_message, output: voice feedback audio_feedback;

[0166] The algorithm steps of the warning and prompt playback module are as follows:

[0167] Assuming the text-to-speech conversion function is S, the warning playback process can be expressed as:

[0168] audio_feedback=S(warning_message)

[0169] Among them, the text-to-speech conversion function S converts the warning information into speech and plays it through the speaker;

[0170] In this formula and this step, the working principle and implementation method of integrating voice recognition technology and voice feedback system in the smart helmet can be clearly described.

[0171] In summary, the present invention mainly has the following beneficial effects:

[0172] 1. By building high-precision sensors into the hard hat, structural and environmental data can be collected in real time to ensure the accuracy and timeliness of the data. This method can effectively capture various detailed information on the construction site, thereby providing instant feedback and decision support; using GPS positioning technology, the worker's position can be accurately determined, and combined with AI cameras to capture images of the interior of the wall, including environmental condition videos, making data collection more comprehensive and accurate; through rapid data analysis, information such as the location of steel bars and wall structures can be analyzed in real time to provide instant feedback and decision support. This method improves the efficiency and accuracy of data processing, allowing managers to discover and deal with problems in a timely manner; using the Canny edge detection algorithm and Gabor filter for texture analysis can effectively extract edge and texture features in the image, thereby improving the accuracy and efficiency of image processing;

[0173] Through innovative technologies such as high-precision data collection, real-time data analysis, optimized acceptance processes, improved data security, augmented reality and voice control interaction, the management efficiency and safety of construction sites have been greatly improved.

[0174] Second, AI automatically identifies and accepts structural integrity and specification consistency. At the same time, the intelligent reminder and warning system can immediately remind workers or supervisors to verify and repair any non-conformities when they are detected. This method greatly improves the efficiency and accuracy of the acceptance process; the real-time analysis results are displayed through the display screen or communication equipment of the smart hat to simplify the acceptance process, and the historical data of hidden projects is stored and displayed for easy auditing and comparison. This method makes the display of data more intuitive and easy to understand; the data is transmitted to the cloud via wireless connection for long-term storage and retrieval, and permission control is implemented to manage data access and protect sensitive information. This method ensures the confidentiality and integrity of the data;

[0175] 3. Real-time collaboration and communication among multiple users. This approach improves management flexibility and responsiveness. Using AR technology, real-time images and data overlays are displayed on workers' hard hats. AR guidance provides step-by-step instructions, standard comparisons, and highlights problem areas. This approach makes on-site operations more intuitive and convenient. Integrating voice recognition technology allows workers to operate hard hat functions through voice commands, and the voice feedback system can automatically play warnings or prompts when problems are detected. This approach improves operational convenience and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0176] Figure 1 The present invention is a flowchart of the steps of the concealed project acceptance management method based on the intelligent safety helmet. DETAILED DESCRIPTION

[0177] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0178] refer to Figure 1 , a concealed engineering acceptance method based on smart helmet, the specific steps are as follows:

[0179] S1: Integrated sensors and data acquisition: A high-precision sensor is built into the helmet to collect structural and environmental data. GPS positioning technology is used to ensure the accuracy of the worker's position. An AI camera captures images of the interior of the wall and also includes videos of environmental conditions.

[0180] S2: Real-time data analysis, real-time analysis of information such as steel bar location and wall structure, providing immediate feedback and decision support through rapid data analysis;

[0181] S3: AI-driven acceptance process, where AI automatically identifies and accepts structural integrity and specification consistency. A smart reminder and warning system also identifies any non-conformities. When a safety helmet is activated, it alerts the worker or supervisor to verify and correct them.

[0182] S4: Visualization support: Displays real-time analysis results through the smart hat's display or communication equipment to simplify the acceptance process. It also stores and displays historical data of concealed projects for easy auditing and comparison.

[0183] S5: Data recording and storage, which transmits data to the cloud via wireless connection for long-term storage and retrieval, and implements permission control to manage data access and protect sensitive information;

[0184] S6: Remote monitoring and collaboration: Project managers can remotely view the situation and provide guidance on the acceptance process via the Internet, thus achieving real-time collaboration and communication among multiple users at multiple locations.

[0185] S7: Augmented reality (AR) support, using AR technology to display real-time images and data overlays on the worker's hard hat. AR guidance can provide step-by-step instructions, standard comparisons, and highlight problem areas.

[0186] S8: Voice control and interaction, integrated voice recognition technology, allows workers to operate the functions of the helmet through voice commands, and the voice feedback system can automatically play warnings or prompts when problems are detected.

[0187] As a further aspect of the present invention, the algorithm formula for collecting sensor data in S1 is described as follows:

[0188] Assume that the data collected by the sensor is D s , the timestamp is t, then the sensor data is represented as:

[0189] D s =f(t)

[0190] Among them, the function f is used to associate time and sensor data, and obtain continuous time series data through integration or difference calculation;

[0191] In this formula: sensor data usually changes over time, so a timestamp t is needed to record each data point. The function f is linear, which describes how the sensor reading changes over time. The integral can be used to estimate the overall change over a period of time, while the difference is used to calculate the rate of change between adjacent time points.

[0192] The triangulation formula of GPS positioning is described as follows:

[0193] Assume the position coordinates of the GPS receiver are (x, y) and the coordinates of the satellite are The distance is d, and the calculation formula is as follows:

[0194]

[0195] In this formula: at least three different satellite signals are required to determine the position of the receiver. By solving the equations, the precise position of the receiver can be obtained, and the Euclidean distance formula is used to calculate the distance between the receiver and the satellite.

[0196] The Euclidean distance formula is the basic method for calculating the straight-line distance between two points in multidimensional space. The formula is as follows:

[0197]

[0198] Where A=(x1,x2,…,x n ) and B=(y1,y2,…,y n ) are the coordinates of two points in n-dimensional space;

[0199] The visual positioning technology formula is described as follows:

[0200] Let the feature point set of the input image be P i =[x i ,y i ], the feature point set of the target image is P j =[x j ,y j ], the matching degree calculation formula is as follows:

[0201]

[0202] In this formula: the key feature points extracted from the image are used for matching and recognition. The similarity between the two images is measured by calculating the sum of the squares of the distances between the feature points. A smaller M value means a higher matching degree.

[0203] As a further aspect of the present invention, the real-time data analysis in S2 is described as follows:

[0204] Canny edge detection algorithm:

[0205] Suppose the input image is I(x,y), the image after Gaussian filtering is G(x,y), the gradient amplitude is M(x,y), and the gradient direction is θ(x,y), then the steps of Canny edge detection are as follows:

[0206] Gaussian filtering: Reduce image noise:

[0207] G(x,y)=I(x,y)*H(x,y)

[0208] Where H(x,y) is the Gaussian kernel;

[0209] Compute the gradient magnitude and direction:

[0210]

[0211] Non-maximum suppression: retain local maxima;

[0212] Dual threshold detection: uses two thresholds, high and low, to determine edges;

[0213] Edge connection: connect edges by hysteresis threshold;

[0214] In this formula:

[0215] Gaussian filtering: used to smooth images and reduce noise effects;

[0216] Gradient calculation: Determine the rate and direction of change of intensity for each pixel in the image;

[0217] Non-maximum suppression: ensures that only local maxima are retained, thereby refining edges;

[0218] Dual-threshold detection: By setting two thresholds to distinguish strong edges from weak edges, the detection accuracy is improved;

[0219] Edge connection: connect discontinuous edge fragments into complete edge lines;

[0220] The texture analysis algorithm formula is described as follows:

[0221] Assume that the input image is I(x,y) and the response of the Gabor filter is R(x,y). The steps of texture analysis are as follows:

[0222] The expression of Gabor filter is:

[0223]

[0224] in, is the space coordinate vector, k μ,u and σ are parameters that control the direction and scale of the filter;

[0225] Among them, k u is the maximum frequency, u is the scale factor;

[0226] Among them, μ is the direction index, which realizes the direction angle φ in the first formula μ ;

[0227] By combining the scale factor and maximum frequency with the direction angle, a complex expression is generated. This expression directly affects how the filter captures features of a specific direction and scale in image processing, so that each possible μ value corresponds to a specific, quantized angle, allowing the filter to cover multiple directions and enhance its ability to respond to multi-directional features in the image;

[0228] In this formula:

[0229] is the normalization factor used to adjust the amplitude of the filter;

[0230] is a Gaussian function used to control the spatial expansion of the filter;

[0231] is a complex exponential function minus a constant term, used to generate the oscillation pattern;

[0232] In this formula: Gabor filter is used to extract local features of the image. In texture analysis and edge detection, by adjusting the parameters μ and u, filter responses in different directions and scales can be obtained, thereby capturing different features of the image.

[0233] As a further aspect of the present invention, the algorithm for the AI-driven acceptance process described in S3 is described as follows:

[0234] Rule engine algorithm:

[0235] Assuming the structural integrity standard is S and the specification consistency standard is C, the evaluation function of the rule engine is:

[0236]

[0237] Anomaly Detection Algorithms:

[0238] Assume that the data set is D = |{d1,d2,…,d n}, the anomaly score is a i , then the calculation formula of the anomaly score of the isolation forest is:

[0239]

[0240] where h(d i ) is the height of the tree, c(n) is the average path length of the tree;

[0241] In this formula:

[0242] Rules engine: evaluates structural integrity and specification consistency based on predefined rules;

[0243] Anomaly Detection: Identify outliers by building multiple random trees and calculating the average path length of samples. A smaller anomaly score indicates a higher probability of anomaly.

[0244] As a further aspect of the present invention, regarding the algorithm formula and analysis of the visualization support described in S4:

[0245] Real-time data collection:

[0246] Let the real-time data collected at time t be D(t), which is a vector or matrix, depending on the dimension of the data, for example:

[0247] D(t)=[d1(t),d2(t),…,d n (t)]

[0248] where d i (t) represents the value of the i-th data point at time t;

[0249] Real-time analytics:

[0250] The analysis function f processes the real-time data D(t) and generates the analysis result R(t). The function f is analyzed to determine the form of the function.

[0251] To make this concrete, let f be a simple linear transformation:

[0252] R(t)=f(D(t))=W·D(t)+b

[0253] Where W is the weight matrix and b is the bias vector;

[0254] Results:

[0255] The display conversion function g converts the analysis result R(t) into a form V(t) displayed on a display screen or communication device. Assuming that the display form is a scalar value or an image, then g is a simple scalar conversion or image processing function. For example, if R(t) is a scalar value, g can be a simple threshold function:

[0256]

[0257] Where θ is the threshold;

[0258] Historical data storage and retrieval, historical data sets:

[0259] The historical dataset H contains all historical records, each record contains a timestamp and analysis results:

[0260] H={h1,h2,…,h n}

[0261] Each h i Is a record containing a timestamp and analysis results, such as h i =(t i ,R(t i ))

[0262] Storing new data:

[0263] When the new analysis result R(t) is added to the historical data set H, a new record h is formed. n+1 =(t,R(t))

[0264] Retrieve historical data:

[0265] For auditing or comparison purposes, specific records or the entire dataset can be retrieved from the historical dataset H;

[0266] Audit and Compare: Audit Function;

[0267] Audit function A returns an audit report that includes data integrity checks, anomaly detection, etc.

[0268] For example, the comparison function:

[0269] The comparison function C returns a comparison result that indicates how different or similar R(t) is compared to the records in the historical dataset H, for example:

[0270] C(R(t),H)=Comparison Result(R(t),H)

[0271] In this formula, through specific mathematical formulas and process descriptions, we can clearly see how to obtain analysis results from real-time data through analysis functions, then display the results through display conversion functions, and store and display historical data of hidden projects for auditing and comparison.

[0272] As a further aspect of the present invention, the algorithm formula for recording and storing the data in S5 is described as follows:

[0273] Data transmission and encryption:

[0274] Assume that the transmitted data is T(t), the key is K, and the encrypted data transmission is E(t), then the encryption formula is:

[0275]

[0276] The key K is a 256-bit random number generated by a secure hash algorithm to ensure the uniqueness and security of each encryption;

[0277] In this formula, in order to protect the confidentiality of data during data transmission, a strong encryption algorithm is used to encrypt the data to ensure the security of the data;

[0278] Data storage and permission control:

[0279] Assume that the data received by the cloud server is S(t), the user role set is U, and the permission matrix is P. The permission control is expressed as follows:

[0280] S i =P u [i]·S[i]

[0281] Among them, P u [i] represents the access permission for the i-th data;

[0282] In this approach, after receiving the data, the cloud server stores it in the database. To protect sensitive information, data access needs to be controlled by permissions. A role-based access control model is used to define different user roles and their corresponding permissions. For example, administrators can access and modify all data, while ordinary users can only access specific data.

[0283] As a further aspect of the present invention, the specific steps of the remote monitoring and collaboration in S6 are as follows:

[0284] Establish network connection: Establish a stable end-to-end connection with the remote server through the wireless network interface to ensure reliable transmission of data packets;

[0285] Data collection and preliminary processing: Use sensors mounted on the helmet to collect environmental and structural data, including but not limited to temperature, humidity, location, and image data. Apply data preprocessing algorithms, such as sliding average filters, to denoise the collected signals and improve data accuracy.

[0286] Video stream compression: Implement the H.264 video coding standard to compress the raw video stream captured by the AI camera to reduce the amount of data transmission. This process involves DCT transformation and quantization steps, specifically:

[0287]

[0288] Among them, f(u,v) is the frequency domain coefficient after transformation, which represents the amplitude of the component with frequency (u,v);

[0289] α u and α v are normalization factors used to ensure energy conservation in the transformation. Their values depend on u and v. If u = 0 or v = 0, then and If ueq 0 and veq 0, then and

[0290] x and y are pixel coordinates in the spatial domain, ranging from 0 to m-1 and 0 to n-1;

[0291] i(x,y) is the pixel value of the original image at position (x,y);

[0292] π is the circumference constant, approximately equal to 3.14159;

[0293] m and n are the sizes of the image blocks, usually 4x4 or 8x8;

[0294] In this formula, the DCT coefficients of each frequency component (u, v) are calculated by multiplying each pixel value i(x, y) in the image block with the corresponding cosine function and summing them. These frequency domain coefficients are then subjected to a quantization step to further reduce the amount of data and prepare for entropy coding.

[0295] Encryption and secure transmission:

[0296] The AES encryption algorithm is used to protect the security of transmitted data and prevent data from being intercepted and tampered during transmission. The encryption process is simplified as follows:

[0297] encrypted_data=aes_encrypt(plain_data,key)

[0298] Use the TLS protocol to add an encryption layer to the TCP connection to ensure the integrity and confidentiality of data during Internet transmission;

[0299] Real-time data analysis and feedback:

[0300] Deploy a real-time data analysis module on a remote server and use the canny edge detection algorithm to extract features from received video frames for further analysis and decision making;

[0301] Based on the analysis results, feedback instructions or adjustment instructions are sent to the on-site helmets through encrypted communication channels to guide on-site operations;

[0302] Through the above steps, the method can ensure that project managers can remotely view the actual situation through the network and provide guidance for the acceptance process, thereby realizing real-time collaboration and communication among multiple locations and multiple users.

[0303] As a further step of the present invention, the key steps and techniques for the augmented reality (AR) support described in S7 are described as follows:

[0304] Image recognition and processing: Extract key feature points from the input image. These feature points are used for subsequent matching and overlay operations;

[0305] The formula is described as: Let the feature point set of the input image be P i ={p1,p2,…,p n}, the feature point set of the target image is Q j ={q1,q2,…,q m}, then the matching degree calculation formula is:

[0306]

[0307] Where i is the feature point index of the input image, j is the feature point index of the target image, and a smaller value means a higher matching degree;

[0308] Real-time rendering and overlay: 3D model loading and conversion, loading 3D models into the AR environment as needed and performing necessary coordinate conversion;

[0309] Assume that the vertex coordinates of the 3D model are {v1,v2,…,v k}, and the transformed coordinates are {u1,u2,…,uk}, the conversion formula is:

[0310] u=transform(v)

[0311] Among them, transform is a function that converts 2D coordinates into 3D space coordinates;

[0312] Perspective transformation: Use perspective transformation to project the 3D model onto a 2D plane. The perspective transformation matrix is:

[0313]

[0314] Where f is the focal length, c x and c y are the center point coordinates, and by multiplying the 3D coordinates by this matrix, they can be converted to 2D coordinates;

[0315] AR guidance and interaction: Based on the analysis results, problem areas are highlighted, and specific areas are highlighted using color filters or marker boxes.

[0316] As a further aspect of the present invention, the algorithm formula and description of the voice control and interaction in S8 are as follows:

[0317] Speech recognition module:

[0318] Input: real-time audio stream audio_stream, output: recognized text recognized_text;

[0319] The algorithm steps of the speech recognition module are:

[0320] Assuming the speech recognition engine is R, the preprocessing function is P, and the transcription function is T, the speech recognition process can be expressed as:

[0321] recognized_text=T(P(audio_stream))

[0322] Among them, the preprocessing function P performs noise reduction and enhancement on the audio stream, and the transcription function T converts the processed audio into text;

[0323] Command parsing and execution module:

[0324] Input: recognized text recognized_text, Output: operation command command;

[0325] The algorithm steps of the command parsing and execution module: Assuming the command library is C and the keyword matching function is K, the command parsing process can be expressed as:

[0326] command=K(recognized_text,C)

[0327] The keyword matching function K searches for keywords in the command library C in the identified text and returns the corresponding operation instructions;

[0328] Environmental monitoring and feedback module:

[0329] Input: operation command command, output: environmental parameters environmental_parameters; warning message warning_message;

[0330] The algorithm steps of the environmental monitoring and feedback module are as follows:

[0331] Let the environmental data acquisition function be E and the anomaly detection function be A;

[0332] The process of environmental monitoring and feedback can be expressed as:

[0333] (E(command),A(E(command)))

[0334] The environmental data acquisition function E obtains relevant environmental data according to the operation instructions, and the anomaly detection function A analyzes these data and generates warning information;

[0335] Warning and prompt playback module:

[0336] Input: warning message warning_message, output: voice feedback audio_feedback;

[0337] The algorithm steps of the warning and prompt playback module are as follows:

[0338] Assuming the text-to-speech conversion function is S, the warning playback process can be expressed as:

[0339] audio_feedback=S(warning_message)

[0340] Among them, the text-to-speech conversion function S converts the warning information into speech and plays it through the speaker;

[0341] In this formula and this step, the working principle and implementation method of integrating voice recognition technology and voice feedback system in the smart helmet can be clearly described.

[0342] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A concealed engineering acceptance method based on smart helmets, characterized in that: The specific steps are as follows: S1: Integrated sensors and data acquisition: A high-precision sensor is built into the helmet to collect structural and environmental data. GPS positioning technology is used to ensure the accuracy of the worker's position. An AI camera captures images of the interior of the wall and also includes videos of environmental conditions. S2: Real-time data analysis, real-time analysis of information such as steel bar location and wall structure, providing immediate feedback and decision support through rapid data analysis; S3: AI-driven acceptance process, where AI automatically identifies and accepts structural integrity and specification consistency. A smart reminder and warning system also identifies any non-conformities. When a safety helmet is activated, it alerts the worker or supervisor to verify and correct them. S4: Visualization support: real-time analysis results are displayed on the smart hat's display or communication equipment to simplify the acceptance process. It also stores and displays historical data of concealed projects for easy auditing and comparison. S5: Data recording and storage, which transmits data to the cloud via wireless connection for long-term storage and retrieval, and implements permission control to manage data access and protect sensitive information; S6: Remote monitoring and collaboration: Project managers can remotely view the situation and provide guidance on the acceptance process via the Internet, thus achieving real-time collaboration and communication among multiple users at multiple locations. S7: Augmented reality (AR) support, using AR technology to display real-time images and data overlays on the worker's hard hat. AR guidance can provide step-by-step instructions, standard comparisons, and highlight problem areas. S8: Voice control and interaction, integrated voice recognition technology, allows workers to operate the functions of the helmet through voice commands, and the voice feedback system can automatically play warnings or prompts when problems are detected.

2. A concealed engineering acceptance method based on a smart helmet according to claim 1, characterized in that: The algorithm formula for sensor data collection described in S1 is described as follows: Assume that the data collected by the sensor is D s , the timestamp is t, then the sensor data is represented as: D s =f(t) Among them, the function f is used to associate time and sensor data, and obtain continuous time series data through integration or difference calculation; In this formula: sensor data usually changes over time, so a timestamp t is needed to record each data point. The function f is linear, which describes how the sensor reading changes over time. The integral can be used to estimate the overall change over a period of time, while the difference is used to calculate the rate of change between adjacent time points. The triangulation formula of GPS positioning is described as follows: Assume the position coordinates of the GPS receiver are (x, y) and the coordinates of the satellite are The distance is d, and the calculation formula is as follows: In this formula: at least three different satellite signals are required to determine the position of the receiver. By solving the equations, the precise position of the receiver can be obtained, and the Euclidean distance formula is used to calculate the distance between the receiver and the satellite. The Euclidean distance formula is the basic method for calculating the straight-line distance between two points in multidimensional space. The formula is as follows: Where A=(x1,x2,…,x n ) and B=(y1,y2,…,y n ) are the coordinates of two points in n-dimensional space; The visual positioning technology formula is described as follows: Let the feature point set of the input image be P i =[x i ,y i ], the feature point set of the target image is P j =[x j ,y j ], the matching degree calculation formula is as follows: In this formula: the key feature points extracted from the image are used for matching and recognition. The similarity between the two images is measured by calculating the sum of the squares of the distances between the feature points. A smaller M value means a higher matching degree.

3. The method for acceptance of concealed works based on smart helmets according to claim 1, characterized in that: The real-time data analysis described in S2 is described as follows: Canny edge detection algorithm: Suppose the input image is I(x,y), the image after Gaussian filtering is G(x,y), the gradient amplitude is M(x,y), and the gradient direction is θ(x,y), then the steps of Canny edge detection are as follows: Gaussian filtering: Reduce image noise: G(x,y)=I(x,y)*H(x,y) Where H(x,y) is the Gaussian kernel; Compute the gradient magnitude and direction: Non-maximum suppression: retain local maxima; Dual threshold detection: uses two thresholds, high and low, to determine edges; Edge connection: connect edges by hysteresis threshold; In this formula: Gaussian filtering: used to smooth images and reduce noise effects; Gradient calculation: Determine the rate and direction of change of intensity for each pixel in the image; Non-maximum suppression: ensures that only local maxima are retained, thereby refining edges; Dual-threshold detection: By setting two thresholds to distinguish strong edges from weak edges, the detection accuracy is improved; Edge connection: connect discontinuous edge fragments into complete edge lines; The texture analysis algorithm formula is described as follows: Assume that the input image is I(x,y) and the response of the Gabor filter is R(x,y). The steps of texture analysis are as follows: The expression of Gabor filter is: in, is the space coordinate vector, k μ,u and σ are parameters that control the direction and scale of the filter; Among them, k u is the maximum frequency, u is the scale factor; Among them, μ is the direction index, which realizes the direction angle φ in the first formula μ ; By combining the scale factor and maximum frequency with the direction angle, a complex expression is generated. This expression directly affects how the filter captures features of a specific direction and scale in image processing, so that each possible μ value corresponds to a specific, quantized angle, allowing the filter to cover multiple directions and enhance its ability to respond to multi-directional features in the image; In this formula: is the normalization factor used to adjust the amplitude of the filter; is a Gaussian function used to control the spatial expansion of the filter; is a complex exponential function minus a constant term, used to generate the oscillation pattern; In this formula: Gabor filter is used to extract local features of the image. In texture analysis and edge detection, by adjusting the parameters μ and u, filter responses in different directions and scales can be obtained, thereby capturing different features of the image.

4. The method for acceptance of concealed works based on smart helmets according to claim 1, characterized in that: The algorithm description of the AI-driven acceptance process described in S3 is as follows: Rule engine algorithm: Assuming the structural integrity standard is S and the specification consistency standard is C, the evaluation function of the rule engine is: Anomaly Detection Algorithms: Assume that the data set is D = |{d1,d2,…,d n }, the anomaly score is a i , then the calculation formula of the anomaly score of the isolation forest is: where h(d i ) is the height of the tree, c(n) is the average path length of the tree; In this formula: Rules engine: evaluates structural integrity and specification consistency based on predefined rules; Anomaly Detection: Identify outliers by building multiple random trees and calculating the average path length of samples. A smaller anomaly score indicates a higher probability of anomaly.

5. The method for acceptance of concealed works based on smart helmets according to claim 1, characterized in that: Regarding the algorithmic formulation and analysis supported by the visualization described in S4: Real-time data collection: Let the real-time data collected at time t be D(t), which is a vector or matrix, depending on the dimension of the data, for example: D(t)=[d1(t),d2(t),…,d n (t)] where d i (t) represents the value of the i-th data point at time t; Real-time analytics: The analysis function f processes the real-time data D(t) and generates the analysis result R(t). The function f is analyzed to determine the form of the function. To make this concrete, let f be a simple linear transformation: R(t)=f(D(t))=W·D(t)+b Where W is the weight matrix and b is the bias vector; Results: The display conversion function g converts the analysis result R(t) into a form V(t) displayed on a display screen or communication device. Assuming that the display form is a scalar value or an image, then g is a simple scalar conversion or image processing function. For example, if R(t) is a scalar value, g can be a simple threshold function: Where θ is the threshold; Historical data storage and retrieval, historical data sets: The historical dataset H contains all historical records, each record contains a timestamp and analysis results: H={h1,h2,…,h n } Each h i Is a record containing a timestamp and analysis results, such as h i =(t i ,R(t i )) Storing new data: When the new analysis result R(t) is added to the historical data set H, a new record h is formed. n+1 =(t,R(t)) Retrieve historical data: For auditing or comparison purposes, specific records or the entire dataset can be retrieved from the historical dataset H; Audit and Compare: Audit Function; Audit function A returns an audit report that includes data integrity checks, anomaly detection, etc. For example, the comparison function: The comparison function C returns a comparison result that indicates how different or similar R(t) is compared to the records in the historical dataset H, for example: C(R(t),H)=Comparison Result(R(t),H) In this formula, through specific mathematical formulas and process descriptions, we can clearly see how to obtain analysis results from real-time data through analysis functions, then display the results through display conversion functions, and store and display historical data of hidden projects for auditing and comparison.

6. The method for acceptance of concealed works based on smart helmets according to claim 1, characterized in that: The algorithm formula for recording and storing the data described in S5 is described as follows: Data transmission and encryption: Assume that the transmitted data is T(t), the key is K, and the encrypted data transmission is E(t), then the encryption formula is: The key K is a 256-bit random number generated by a secure hash algorithm to ensure the uniqueness and security of each encryption; In this formula, in order to protect the confidentiality of data during data transmission, a strong encryption algorithm is used to encrypt the data to ensure the security of the data; Data storage and permission control: Assume that the data received by the cloud server is S(t), the user role set is U, and the permission matrix is P. The permission control is expressed as follows: S i =P u [i]·S[i] Among them, P u [i] represents the access permission for the i-th data; In this approach, after receiving the data, the cloud server stores it in the database. To protect sensitive information, data access needs to be controlled by permissions. A role-based access control model is used to define different user roles and their corresponding permissions. For example, administrators can access and modify all data, while ordinary users can only access specific data.

7. The method for acceptance of concealed works based on smart helmets according to claim 1, characterized in that: The specific steps of remote monitoring and collaboration in S6 are as follows: Establish network connection: Establish a stable end-to-end connection with the remote server through the wireless network interface to ensure reliable transmission of data packets; Data collection and preliminary processing: Use sensors mounted on the helmet to collect environmental and structural data, including but not limited to temperature, humidity, location, and image data. Apply data preprocessing algorithms, such as sliding average filters, to denoise the collected signals and improve data accuracy. Video stream compression: Implement the H.264 video coding standard to compress the raw video stream captured by the AI camera to reduce the amount of data transmission. This process involves DCT transformation and quantization steps, specifically: Among them, f(u,v) is the frequency domain coefficient after transformation, which represents the amplitude of the component with frequency (u,v); α u and α v are normalization factors used to ensure energy conservation in the transformation. Their values depend on u and v. If u = 0 or v = 0, then and If ueq 0 and veq 0, then and x and y are pixel coordinates in the spatial domain, ranging from 0 to m-1 and 0 to n-1; i(x,y) is the pixel value of the original image at position (x,y); π is the circumference constant, approximately equal to 3.14159; m and n are the sizes of the image blocks, usually 4x4 or 8x8; In this formula, the DCT coefficients of each frequency component (u, v) are calculated by multiplying each pixel value i(x, y) in the image block with the corresponding cosine function and summing them. These frequency domain coefficients are then subjected to a quantization step to further reduce the amount of data and prepare for entropy coding. Encryption and secure transmission: The AES encryption algorithm is used to protect the security of transmitted data and prevent data from being intercepted and tampered during transmission. The encryption process is simplified as follows: encrypted_data=aes_encrypt(plain_data,key) Use the TLS protocol to add an encryption layer to the TCP connection to ensure the integrity and confidentiality of data during Internet transmission; Real-time data analysis and feedback: Deploy a real-time data analysis module on a remote server and use the canny edge detection algorithm to extract features from received video frames for further analysis and decision making; Based on the analysis results, feedback instructions or adjustment instructions are sent to the on-site helmets through encrypted communication channels to guide on-site operations; Through the above steps, the method can ensure that project managers can remotely view the actual situation through the network and provide guidance for the acceptance process, thereby realizing real-time collaboration and communication among multiple locations and multiple users.

8. The method for concealed engineering acceptance based on smart helmet according to claim 1, characterized in that: The key steps and technologies for augmented reality (AR) support in S7 are described below: Image recognition and processing: Extract key feature points from the input image. These feature points are used for subsequent matching and overlay operations; The formula is described as: Let the feature point set of the input image be P i ={p1,p2,…,p n }, the feature point set of the target image is Q j ={q1,q2,…,q m }, then the matching degree calculation formula is: Where i is the feature point index of the input image, j is the feature point index of the target image, and a smaller value means a higher matching degree; Real-time rendering and overlay: 3D model loading and conversion, loading 3D models into the AR environment as needed and performing necessary coordinate conversion; Assume that the vertex coordinates of the 3D model are {v1,v2,…,v k }, and the transformed coordinates are {u1,u2,…,u k }, the conversion formula is: u=transform(v) Among them, transform is a function that converts 2D coordinates into 3D space coordinates; Perspective transformation: Use perspective transformation to project the 3D model onto a 2D plane. The perspective transformation matrix is: Where f is the focal length, c x and c y are the center point coordinates, and by multiplying the 3D coordinates by this matrix, they can be converted to 2D coordinates; AR guidance and interaction: Based on the analysis results, problem areas are highlighted, and specific areas are highlighted using color filters or marker boxes.

9. The method for concealed engineering acceptance based on smart helmet according to claim 1, characterized in that: The algorithm formula and description of the voice control and interaction described in S8 are as follows: Speech recognition module: Input: real-time audio stream audio_stream, output: recognized text recognized_text; The algorithm steps of the speech recognition module are: Assuming the speech recognition engine is R, the preprocessing function is P, and the transcription function is T, the speech recognition process can be expressed as: recognized_text=T(P(audio_stream)) Among them, the preprocessing function P performs noise reduction and enhancement on the audio stream, and the transcription function T converts the processed audio into text; Command parsing and execution module: Input: recognized text recognized_text, Output: operation command command; The algorithm steps of the command parsing and execution module: Assuming the command library is C and the keyword matching function is K, the command parsing process can be expressed as: command=K(recognized_text,C) The keyword matching function K searches for keywords in the command library C in the identified text and returns the corresponding operation instructions; Environmental monitoring and feedback module: Input: operation command command, output: environmental parameters environmental_parameters; warning message warning_message; The algorithm steps of the environmental monitoring and feedback module are as follows: Let the environmental data acquisition function be E and the anomaly detection function be A; The process of environmental monitoring and feedback can be expressed as: (E(command),A(E(command))) The environmental data acquisition function E obtains relevant environmental data according to the operation instructions, and the anomaly detection function A analyzes these data and generates warning information; Warning and prompt playback module: Input: warning message warning_message, output: voice feedback audio_feedback; The algorithm steps of the warning and prompt playback module are as follows: Assuming the text-to-speech conversion function is S, the warning playback process can be expressed as: audio_feedback=S(warning_message) Among them, the text-to-speech conversion function S converts the warning information into speech and plays it through the speaker; In this formula and this step, the working principle and implementation method of integrating voice recognition technology and voice feedback system in the smart helmet can be clearly described.

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