Smart construction site management system and method

Abstract

The present invention relates to the field of smart construction site management technology, and more specifically, to a smart construction site management system and method. The system comprises an image transmission connection unit, a worker information extraction unit, a worker entry recording unit, a team priority improvement unit, a helmet recognition unit, and a worker priority improvement unit; the image transmission connection unit is used to establish an image transmission connection with a face recognition machine of a real-name system channel of a construction site and a peripheral camera that monitors the real-name system channel, and is connected to a construction site personnel management terminal at the same time; by combining and allocating workers according to team information in worker information, each worker has a clear team affiliation; when workers from multiple teams appear at the same time, team priorities are sorted according to the distance between the pre-identified workers and the face recognition machine, with the team closest to the team having the highest priority; and, in combination with a number of helmet color analysis, the priority of workers wearing the most helmet colors and at the appropriate distance is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of smart construction site management, and in particular to a smart construction site management system and method. Background Art

[0002] In terms of construction site management, existing technologies aim to improve construction efficiency, protect worker safety, and ensure project quality. Through manual management combined with some information technology, such as attendance clock-in systems and safety inspection forms, a certain degree of monitoring of basic management of construction site personnel can be achieved.

[0003] At present, when workers enter and exit a construction site, they need to pass through a real-name channel for facial recognition. However, after a day of work at the construction site, due to the high dust on the construction site, the workers' faces may be obscured by impurities, resulting in the facial recognition device being unable to quickly authenticate the workers when performing facial recognition exit detection. The workers need to keep trying. When the recognition efficiency is low, the real-name channel will become crowded, the worker management will be chaotic, and workers will not queue up to rush to pass the facial recognition machine, resulting in incomplete records of workers leaving the site and low efficiency in worker management. Therefore, a smart construction site management system and method are proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a smart construction site management system and method to solve the problems raised in the above background technology.

[0005] To achieve a solution to the above technical problems, one of the objectives of the present invention is to provide a smart construction site management system, including an image transmission connection unit, a worker information extraction unit, a worker entry recording unit, a team priority improvement unit, a safety helmet recognition unit, and a worker priority improvement unit;

[0006] The image transmission connection unit is used to establish an image transmission connection with the face recognition machine of the real-name channel of the construction site and the surrounding cameras that monitor the real-name channel, and at the same time connect to the construction site personnel management terminal;

[0007] The worker information extraction unit is used to extract worker information at the construction site personnel management terminal, group and allocate the worker information according to the work teams, and then establish a worker database and a real-time record library for the construction site workers;

[0008] The worker entry recording unit is used to retain the image data of workers entering the construction site through the face recognition machine, and input the worker's team and helmet color into the construction site worker real-time record library for recording based on the worker image data;

[0009] The team priority improvement unit is used to pre-recognize the faces of workers leaving the site based on image data obtained by surrounding cameras, extract teams from the real-time record library of workers on the construction site based on the pre-recognized faces, and input the extracted teams into the face recognition machine for priority improvement;

[0010] The helmet recognition unit is used to extract the helmet color from the image data acquired by the surrounding cameras, and compare the extracted helmet color with the helmet color of the team extracted by the team priority improvement unit. When the helmet color recorded by the team contains other helmet colors, the priority of the workers with other helmet colors is restored;

[0011] The worker priority enhancement unit is used to analyze the quantity of different helmet colors, extract the most numerous helmet color, and perform dynamic position recognition in the image data obtained by the surrounding cameras, and dynamically adjust the recognition priority of the helmet color in the team according to the recognition results.

[0012] As a further improvement of the present technical solution, the image transmission connection unit is connected to the construction site personnel management terminal, and then establishes an image transmission connection between the construction site personnel management terminal and the face recognition machine of the construction site real-name channel and the surrounding cameras that monitor the real-name channel through the Internet of Things, so that the image data of the face recognition machine and the image data of the surrounding cameras can be collected in real time.

[0013] As a further improvement of this technical solution, the worker information extraction unit includes a team allocation module and a database establishment module;

[0014] The team allocation module is used to extract worker information from the construction site personnel management terminal and assign workers to teams based on the team information in the worker information, so that each worker has a corresponding team;

[0015] The database establishment module is used to establish a worker database based on the worker information of the combined allocation, and at the same time establish a real-time record library of construction site workers. The real-time record library of construction site workers is applied to the face recognition machine, and a corresponding number of labels are established in the real-time record library of construction site workers according to the number of teams.

[0016] As a further improvement of this technical solution, the worker entry recording unit includes an image retention module and a worker recording module;

[0017] The image retention module is used to retain the image data of workers entering the construction site through the face recognition machine, and when the worker leaves the construction site through the face recognition machine, the retained data of the worker is deleted from the real-time record library of the construction site workers;

[0018] The worker recording module is used to group the retained worker image data, save the worker in the construction site worker real-time record library with corresponding labels according to the identified team, and then perform helmet color analysis on the worker image data to obtain the color of the helmet worn by the worker.

[0019] As a further improvement of this technical solution, the team priority improvement unit includes a pre-identification module and a priority identification module;

[0020] The pre-identification module is used to extract image data obtained by surrounding cameras, perform face pre-identification on workers leaving the site based on the image data, obtain recognizable face information, and use it as the pre-identified worker;

[0021] The priority recognition module is used to extract the teams based on the pre-identified workers in the real-time record library of construction workers, feed the extracted teams back to the real-time record library of construction workers, and then send them to the face recognition machine through the real-time record library of construction workers to improve the priority of recognition;

[0022] When multiple teams are extracted, they are sorted according to the distance between the pre-identified workers and the face recognition machine. When the pre-identified workers of a team are closest to the face recognition machine, the recognition priority of this team is the highest.

[0023] As a further improvement of the present technical solution, the helmet recognition unit includes a helmet extraction module and a helmet comparison module;

[0024] The helmet extraction module is used to analyze and extract the color of the helmets worn by workers based on the image data obtained by the surrounding cameras, thereby obtaining the color of the helmet worn by each worker;

[0025] The hard hat comparison module is used to compare the obtained hard hat color with the hard hat color of the team with increased recognition priority. When the hard hat color recorded by the team in the real-time record library of construction site workers contains other hard hat colors that are not obtained by the hard hat extraction module, the workers with other hard hat colors in the team with increased recognition priority will be prioritized, and only workers with the same hard hat color as the analyzed and extracted will be retained in the team with increased recognition priority. Secondly, when the hard hat colors recorded by the team in the real-time record library of construction site workers are the same as the number of hard hat colors obtained by the hard hat extraction module, if there is only one color, continue monitoring; if there are multiple colors, send them to the worker priority improvement unit for the next step.

[0026] As a further improvement of this technical solution, the worker priority improvement unit includes a quantity analysis module and a safety helmet priority adjustment module;

[0027] The quantity analysis module is used to perform quantity analysis on different helmet colors, obtain the quantity of each helmet color, and then extract the helmet color with the largest quantity;

[0028] The safety helmet priority adjustment module is used to dynamically identify the positions of workers wearing the most safety helmet colors in the image data obtained by the surrounding cameras. When the color of the safety helmet worn by the worker closest to the face recognition machine is inconsistent with the color of the most safety helmets, the priority of the workers wearing the most safety helmet colors will be lowered than the workers wearing other safety helmet colors in the team with increased recognition priority. Secondly, when the color of the safety helmet worn by the worker closest to the face recognition machine is consistent with the color of the most safety helmets, the priority of the workers wearing the most safety helmet colors will be increased compared with the workers wearing other safety helmet colors in the team with increased recognition priority.

[0029] A second object of the present invention is to provide a smart construction site management method, including any one of the smart construction site management systems described above, comprising the following steps:

[0030] S1. Establish an image transmission connection with the facial recognition machine in the real-name access channel of the construction site and the surrounding cameras that monitor the real-name access channel. At the same time, connect to the construction site personnel management terminal, group and allocate worker information according to work groups, and then establish a worker database and a real-time record library for construction site workers.

[0031] S2. The image data of workers entering the construction site through the facial recognition machine is retained, and the worker's team and helmet color are input into the construction site worker real-time record library based on the worker image data for recording;

[0032] S3. Pre-identify the faces of workers leaving the site based on image data captured by surrounding cameras. Extract teams from the real-time record database of workers on the construction site based on the pre-identified faces. Input the extracted teams into the face recognition machine for priority enhancement. Compare the extracted helmet colors with those of the extracted teams. If the helmet colors recorded by the team contain other helmet colors, prioritize the workers with other helmet colors.

[0033] S4. Quantitatively analyze the different helmet colors, extract the helmet color with the largest number, and dynamically identify its position in the image data acquired by the surrounding cameras. Dynamically adjust the recognition priority of the helmet color in the team based on the recognition result.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The smart construction site management system and method combines and allocates workers based on the team information in the worker information, so that each worker has a clear team affiliation. When workers from multiple teams appear at the same time, the teams are prioritized according to the distance between the pre-identified workers and the face recognition machine. The team closest to the worker has the highest priority. At the same time, combined with the number of safety helmet colors, the priority of workers wearing the most safety helmets of the same color and at the right distance is increased. This dynamic adjustment method can better adapt to the actual situation of the construction site, improve the pertinence and effectiveness of management, and at the same time increase the speed of workers leaving and entering the site, avoiding large-scale congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is the overall structural principle diagram of the present invention.

[0037] The meaning of each number in the figure is:

[0038] 10. Image transmission connection unit; 20. Worker information extraction unit; 30. Worker entry recording unit; 40. Team priority improvement unit; 50. Safety helmet recognition unit; 60. Worker priority improvement unit. DETAILED DESCRIPTION

[0039] 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.

[0040] like Figure 1 As shown, one of the purposes of the present invention is to provide a smart construction site management system, including an image transmission connection unit 10, a worker information extraction unit 20, a worker entry recording unit 30, a team priority improvement unit 40, a helmet recognition unit 50 and a worker priority improvement unit 60;

[0041] The image transmission connection unit 10 is used to establish an image transmission connection with the face recognition machine of the real-name channel of the construction site and the surrounding cameras that monitor the real-name channel, and at the same time connect to the construction site personnel management terminal;

[0042] The image transmission connection unit 10 is connected to the construction site personnel management terminal, and then establishes an image transmission connection between the construction site personnel management terminal and the face recognition machine of the construction site real-name channel and the surrounding cameras monitoring the real-name channel through the Internet of Things, so that the image data of the face recognition machine and the image data of the surrounding cameras can be collected in real time. The specific steps are as follows:

[0043] Connecting to the construction site personnel management terminal: Determine the interface type and communication protocol of the construction site personnel management terminal to facilitate subsequent connection with other devices;

[0044] Connect the construction site personnel management terminal to the equipment through the Internet of Things: Select the appropriate Internet of Things communication technology, such as Wi-Fi, Bluetooth, ZigBee, or cellular network, taking into account the actual environment and needs of the construction site, to ensure the stability and reliability of communication. Then, configure the network parameters of the Internet of Things device, including IP address, subnet mask, gateway, etc., to establish a data transmission channel between the construction site personnel management terminal and the facial recognition machine and surrounding cameras.

[0045] Real-time image data collection: The facial recognition machine and surrounding cameras continuously collect image data and convert it into digital signals. Through the established image transmission connection, the image data is transmitted in real time to the construction site personnel management end.

[0046] The worker information extraction unit 20 is used to extract worker information at the construction site personnel management end, group and allocate the worker information according to the team, and then establish a worker database and a real-time record library of the construction site workers;

[0047] The worker information extraction unit 20 includes a team allocation module and a database establishment module;

[0048] The team allocation module is used to extract worker information from the construction site personnel management terminal and assign workers to teams based on the team information in the worker information, so that each worker has a corresponding team;

[0049] The database establishment module is used to establish a worker database based on the information of the workers assigned by the group, and to establish a real-time record library for construction workers. The real-time record library for construction workers is used by the face recognition machine. The corresponding number of labels is created in the real-time record library for construction workers according to the number of teams. The specific steps are as follows:

[0050] Extracting worker information: Connecting to the database or data source of the construction site personnel management end to obtain the worker information stored therein, and performing data cleaning and preprocessing on the obtained worker information to remove invalid data and erroneous information to ensure data accuracy and completeness;

[0051] Group assignment based on team information: Extract team information from worker information and group workers based on team information to ensure that each worker is assigned to a specific team;

[0052] Establish a worker database: Select an appropriate database management system, such as MySQL, Oracle, or MongoDB, and design the database table structure based on the needs of construction site management, including worker information tables and team information tables, and store the assigned worker information in the worker database;

[0053] Create tags in the real-time record library: Based on the number of teams, create corresponding tags for each team in the real-time record library of construction site workers, associate worker information with the corresponding team tags, and ensure that the worker information of a specific team can be quickly queried in the real-time record library.

[0054] The worker entry recording unit 30 is used to retain the image data of workers who enter the construction site through the face recognition machine, and input the worker's team and helmet color into the construction site worker real-time record library for recording based on the worker image data;

[0055] The worker entry recording unit 30 includes an image retention module and a worker recording module;

[0056] The image retention module is used to retain the image data of workers entering the construction site through the facial recognition machine. At the same time, when the worker leaves the construction site through the facial recognition machine, the retained data of the worker will be deleted from the real-time record library of the construction site workers. The specific steps are as follows:

[0057] Data reception and recognition: When the facial recognition machine detects a worker performing an identification operation, it collects the worker's facial image data in real time and converts it into digital format. This data is then sent to the server side of the construction site worker real-time record library via a pre-established transmission channel.

[0058] The facial recognition machine processes the captured image using a facial recognition algorithm to determine whether the worker matches the registered personnel information. If a match is successful, the worker is confirmed to be a legal person allowed to enter the construction site and is prepared for subsequent data retention operations.

[0059] Storage preparation and associated information integration: At the construction site worker real-time record library, the storage location and method of the image data are determined according to the storage rules set by the system. Other associated information corresponding to the worker, such as name, team affiliation, and entry time, is extracted and associated with the image data for subsequent query and management. For example, a data structure can be created that contains image data fields and other text information fields and filled with the corresponding content.

[0060] Departure detection and confirmation: When a worker approaches the facial recognition machine again for identification and prepares to leave the construction site, the facial recognition machine also captures their facial image and processes it through the recognition algorithm to confirm the worker's identity and departure behavior. By comparing the entry time and the current time, it can be comprehensively determined that the worker is indeed leaving the construction site, thereby triggering the subsequent data deletion process;

[0061] Data location and deletion operations: Based on the association information and index mechanism established during previous storage, the image data record retained when the worker previously entered the construction site is quickly located in the real-time record library of the construction site workers, and the deletion operation is performed.

[0062] The worker record module is used to group the retained worker image data, save the corresponding label of the worker in the real-time record library of the construction site workers according to the identified team, and then perform helmet color analysis on the worker image data to obtain the color of the helmet worn by the worker. The specific steps are as follows:

[0063] Image feature extraction: From the worker image data stored in the real-time worker record library on the construction site, select the images required for team identification. Using computer vision technology, extract the key features in the images and normalize the extracted features to an appropriate numerical range to facilitate subsequent comparison and analysis.

[0064] Compare with known team member features: The currently extracted worker image features are compared one by one with the features in the feature library of each team. The team to which the worker belongs is determined based on the set similarity threshold. Once the team to which the worker belongs is determined, the corresponding label of the team is obtained.

[0065] Image color space conversion: Select the portion of the image to be analyzed for helmet color from the retained worker image data and convert the image's color space from the common RGB (red, blue, yellow) color space to a color space more convenient for color analysis, such as the HSV (hue, saturation, value) color space;

[0066] Color range definition and recognition: According to the common color range of hard hats, the corresponding threshold interval is set in the HSV color space. The pixels of the hard hat area in the image after the color space conversion are traversed to determine whether the H, S, and V values of each pixel fall within the set threshold interval of a certain color. If the number of pixels falling within a certain color interval reaches a certain proportion (for example, more than 50% of the hard hat recognition units of the total number of pixels in the entire hard hat area), the hard hat is determined to be this color, and then the identified hard hat color information is saved in the record corresponding to the worker in the real-time record library of construction site workers.

[0067] The team priority raising unit 40 is used to pre-recognize the faces of workers leaving the site based on the image data obtained by the surrounding cameras, extract the teams from the real-time record library of workers on the construction site based on the pre-recognized faces, and input the extracted teams into the face recognition machine for priority raising;

[0068] The team priority enhancement unit 40 includes a pre-identification module and a priority identification module;

[0069] The pre-identification module is used to extract image data captured by surrounding cameras, perform facial pre-identification on workers leaving the site based on the image data, obtain recognizable facial information, and use it as the pre-identified worker. The specific steps are as follows:

[0070] Data acquisition: extracting image data from the storage location or real-time transmission channel established by surrounding cameras;

[0071] Face detection: Using a cascade classifier based on Haar features and a deep learning-based face detection algorithm (such as SSD and MTCNN), the preprocessed image data is fed into the selected face detection algorithm. The algorithm scans various parts of the image and determines which areas may be faces based on pre-learned facial feature patterns. It then outputs the coordinates of the face area and a possible face confidence score.

[0072] Facial feature extraction: For the detected face area, appropriate facial feature extraction techniques are used. Commonly used methods include those based on convolutional neural networks (CNNs). For example, network models that have been trained on large-scale face datasets (such as VGG-Face and ArcFace) can be used. These models can convert facial images into fixed-dimensional feature vectors that can effectively represent the unique characteristics of the face, such as the relative position of facial features, facial contour shape, texture, and other information.

[0073] Face matching and pre-recognition: Each facial feature vector currently extracted from the surrounding camera images is compared with the vectors in the worker database one by one. The formula is as follows:

[0074]

[0075] in, is the current face feature vector, is the face feature vector in the comparison library, n is the feature vector dimension, a i and b i is a vector and The elements in the i-th dimension, is a vector and The Euclidean distance between

[0076] The distance threshold is set to 0.8. When the calculated distance is less than the threshold, the two are considered to match, that is, the corresponding worker identity is identified, the relevant facial information of the worker is obtained, and the worker is marked as a pre-identified worker.

[0077] The priority recognition module is used to extract teams based on the pre-identified workers in the real-time record library of construction workers, feed the extracted teams back to the real-time record library of construction workers, and then send them to the face recognition machine through the real-time record library of construction workers to improve the recognition priority;

[0078] When multiple teams are extracted, they are sorted according to the distance between the pre-identified workers and the face recognition machine. When the pre-identified workers of a team are closest to the face recognition machine, the recognition priority of that team is the highest. The specific steps are as follows:

[0079] Locating pre-identified worker records: The system obtains a unique identifier from the pre-identified worker's facial information. This unique identifier is then used to search the real-time worker records database on the construction site to locate the record entry corresponding to the pre-identified worker. The team information to which the pre-identified worker belongs is then extracted from the located record entry.

[0080] Send information to the facial recognition machine to increase recognition priority: Encapsulate the extracted and updated team information, attach instruction information for increasing the recognition priority of the team, and send it to the facial recognition machine through the established communication connection in a predetermined data transmission format (e.g., JSON format), so that the facial recognition machine knows that a higher priority should be given to the recognition of the team;

[0081] Obtaining the distance information of pre-identified workers: When extracting multiple teams, it is necessary to obtain the distance data between the pre-identified workers of each team and the face recognition machine. The formula is as follows:

[0082]

[0083] Where Z represents the actual distance, that is, the actual spatial distance between the worker and the facial recognition machine. f is the focal length of the camera, which is an inherent parameter of the camera. It is usually marked when the camera leaves the factory or can be obtained through calibration experiments. D is the size of the object in the image, and d is the actual size of the object in the real world. For example, if the average height of a worker is known to be 1.7 meters, the distance can be estimated by converting the pixel size in the image into this formula.

[0084] Distance sorting operation: Sort the distance data of the pre-identified workers of each team. After the sorting is completed, the team where the pre-identified worker corresponding to the team closest to the team is located is the team with the highest recognition priority. The team information is sent to the face recognition machine again through the above-mentioned method to clearly inform the face recognition machine that this team should be given the highest recognition priority.

[0085] The helmet recognition unit 50 is used to extract the helmet color from the image data obtained by the surrounding cameras, and compare the extracted helmet color with the helmet color of the team extracted by the team priority improvement unit 40. When the helmet color recorded by the team contains other helmet colors, the priority of the workers with other helmet colors is restored;

[0086] The helmet recognition unit 50 includes a helmet extraction module and a helmet comparison module;

[0087] The helmet extraction module is used to analyze and extract the color of the helmets worn by workers based on the image data obtained by the surrounding cameras, thereby obtaining the color of the helmet worn by each worker;

[0088] The image area containing the worker and the helmet is selected from the image data obtained by the surrounding cameras, and the color characteristics of the pixels in the helmet area are analyzed.

[0089] The hard hat comparison module is used to compare the obtained hard hat colors with the hard hat colors of the team with increased recognition priority. When the hard hat colors recorded by the team in the real-time record library of construction site workers contain other hard hat colors that are not obtained by the hard hat extraction module, the workers with other hard hat colors in the team with increased recognition priority will be prioritized, and only workers with the same hard hat colors as those extracted by analysis will be retained in the team with increased recognition priority. Secondly, when the hard hat colors recorded by the team in the real-time record library of construction site workers are the same as the number of hard hat colors obtained by the hard hat extraction module, if there is only one color, continue monitoring; if there are multiple colors, send them to the worker priority improvement unit 60 for the next step.

[0090] The worker priority enhancement unit 60 is used to perform quantitative analysis on different helmet colors, extract the most numerous helmet color, and perform dynamic position recognition in the image data obtained by the surrounding cameras, and dynamically adjust the recognition priority of the helmet color in the team according to the recognition results.

[0091] The worker priority enhancement unit 60 includes a quantity analysis module and a helmet priority adjustment module;

[0092] The quantity analysis module is used to perform quantity analysis on different helmet colors, obtain the quantity of each helmet color, and then extract the helmet color with the largest quantity;

[0093] The safety helmet priority adjustment module is used to dynamically identify the positions of workers wearing the most numerous safety helmet colors in the image data obtained by the surrounding cameras. When the color of the safety helmet worn by the worker closest to the face recognition machine is inconsistent with the color of the most numerous safety helmets, the priority of the workers wearing the most numerous safety helmet colors will be lowered than the workers wearing other safety helmet colors in the team with increased recognition priority. Secondly, when the color of the safety helmet worn by the worker closest to the face recognition machine is consistent with the color of the most numerous safety helmets, the priority of the workers wearing the most numerous safety helmet colors will be increased compared with the workers wearing other safety helmet colors in the team with increased recognition priority.

[0094] In the image data obtained from the surrounding cameras, the image area where the workers wearing the most helmet colors are located is located again through target detection and other algorithms. Then, based on the camera calibration parameters and some geometric relationship calculations, the position information in the image is converted into the position coordinates in real space relative to the face recognition machine. The formula is as follows:

[0095]

[0096] Among them, (X, Y, Z) are world coordinates, that is, the position coordinates of the worker in the actual space, (u, v) are the pixel coordinates corresponding to the object on the image plane, K is the intrinsic parameter matrix of the camera, which contains parameters related to the imaging characteristics of the camera itself, such as focal length, and is generally obtained through calibration, R is the rotation matrix, and T is the translation vector, which together constitute the extrinsic parameters of the camera, describing the position and posture of the camera in the world coordinate system, and also need to be obtained through calibration, and Z is the depth information, which is often determined with the help of some prior knowledge or other auxiliary measurement methods to complete the conversion from image coordinates to world coordinates and realize the determination of the worker's position in the actual space.

[0097] A second object of the present invention is to provide a smart construction site management method, including any one of the smart construction site management systems described above, comprising the following steps:

[0098] S1. Establish an image transmission connection with the facial recognition machine in the real-name access channel of the construction site and the surrounding cameras that monitor the real-name access channel. At the same time, connect to the construction site personnel management terminal, group and allocate worker information according to work groups, and then establish a worker database and a real-time record library for construction site workers.

[0099] S2. The image data of workers entering the construction site through the facial recognition machine is retained, and the worker's team and helmet color are input into the construction site worker real-time record library based on the worker image data for recording;

[0100] S3. Pre-identify the faces of workers leaving the site based on image data captured by surrounding cameras. Extract teams from the real-time record database of workers on the construction site based on the pre-identified faces. Input the extracted teams into the face recognition machine for priority enhancement. Compare the extracted helmet colors with those of the extracted teams. If the helmet colors recorded by the team contain other helmet colors, prioritize the workers with other helmet colors.

[0101] S4. Quantitatively analyze the different helmet colors, extract the helmet color with the largest number, and dynamically identify its position in the image data acquired by the surrounding cameras. Dynamically adjust the recognition priority of the helmet color in the team based on the recognition result.

[0102] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. Smart construction site management system, characterized by: It includes an image transmission connection unit, a worker information extraction unit, a worker entry recording unit, a team priority improvement unit, a safety helmet recognition unit, and a worker priority improvement unit; The image transmission connection unit is used to establish an image transmission connection with the face recognition machine of the real-name channel of the construction site and the surrounding cameras that monitor the real-name channel, and at the same time connect to the construction site personnel management terminal; The worker information extraction unit is used to extract worker information at the construction site personnel management terminal, group and allocate the worker information according to the work teams, and then establish a worker database and a real-time record library for the construction site workers; The worker entry recording unit is used to retain the image data of workers entering the construction site through the face recognition machine, and input the worker's team and helmet color into the construction site worker real-time record library for recording based on the worker image data; The team priority improvement unit is used to perform face pre-recognition on workers leaving the site based on image data acquired by surrounding cameras, extract teams from the real-time record library of workers on the construction site based on the pre-recognized faces, and input the extracted teams into the face recognition machine for priority improvement; when multiple teams are extracted, they are sorted according to the distance between the pre-recognized workers and the face recognition machine. The team with the pre-recognized workers closest to the face recognition machine has the highest recognition priority; The hard hat recognition unit is used to extract the hard hat color from the image data obtained by the surrounding cameras, and compare the extracted hard hat color with the hard hat color of the team extracted by the team priority improvement unit; when the hard hat color recorded by the team in the real-time record library of the construction site workers contains other hard hat colors that are not obtained by the hard hat recognition unit, the workers with other hard hat colors in the team with improved recognition priority are prioritized, and only workers with the same hard hat color as the analyzed and extracted are retained in the team with improved recognition priority; secondly, when the hard hat colors recorded by the team in the real-time record library of the construction site workers are the same as the number of hard hat colors obtained by the hard hat recognition unit, if there is only one color, then continue to monitor; if there are multiple colors, send them to the worker priority improvement unit for the next step; The worker priority improvement unit is used to perform quantitative analysis on different safety helmet colors, extract the safety helmet color with the largest number, and perform dynamic position recognition in the image data obtained by the surrounding cameras; when the color of the safety helmet worn by the worker closest to the face recognition machine is inconsistent with the color of the safety helmet with the largest number, the priority of the worker wearing the safety helmet with the largest number will be reduced to a lower level than the workers wearing other safety helmet colors in the team with improved recognition priority; when the color of the safety helmet worn by the worker closest to the face recognition machine is consistent with the color of the safety helmet with the largest number, the priority of the worker wearing the safety helmet with the largest number in the team with improved recognition priority will be increased to a higher level than the workers wearing other safety helmet colors.

2. The intelligent construction site management system according to claim 1, characterized in that: The image transmission connection unit is connected to the construction site personnel management terminal, and then establishes an image transmission connection between the construction site personnel management terminal and the face recognition machine of the construction site real-name channel and the surrounding cameras that monitor the real-name channel through the Internet of Things, so that the image data of the face recognition machine and the image data of the surrounding cameras can be collected in real time.

3. The intelligent construction site management system according to claim 1, characterized in that: The worker information extraction unit includes a team allocation module and a database establishment module; The team allocation module is used to extract worker information from the construction site personnel management terminal and assign workers to teams based on the team information in the worker information, so that each worker has a corresponding team; The database establishment module is used to establish a worker database based on the worker information of the combined allocation, and at the same time establish a real-time record library of construction site workers. The real-time record library of construction site workers is applied to the face recognition machine, and a corresponding number of labels are established in the real-time record library of construction site workers according to the number of teams.

4. The intelligent construction site management system according to claim 1, characterized in that: The worker entry recording unit includes an image retention module and a worker recording module; The image retention module is used to retain the image data of workers entering the construction site through the face recognition machine, and when the worker leaves the construction site through the face recognition machine, the retained data of the worker is deleted from the real-time record library of the construction site workers; The worker record module is used to classify the retained worker image data into work groups, save the worker in the construction site worker real-time record library with corresponding labels according to the identified work groups, and then perform safety helmet color analysis on the worker image data to obtain the color of the safety helmet worn by the worker.

5. The intelligent construction site management system according to claim 1, characterized in that: The team priority enhancement unit includes a pre-identification module and a priority identification module; The pre-identification module is used to extract image data obtained by surrounding cameras, perform face pre-identification on workers leaving the site based on the image data, obtain recognizable face information, and use it as the pre-identified worker; The priority recognition module is used to extract work teams based on the pre-identified workers in the real-time record library of construction site workers, feed the extracted work teams back to the real-time record library of construction site workers, and then send them to the face recognition machine through the real-time record library of construction site workers for priority improvement.

6. The intelligent construction site management system according to claim 1, characterized in that: The helmet recognition unit includes a helmet extraction module and a helmet comparison module; The helmet extraction module is used to analyze and extract the color of the helmets worn by workers based on the image data obtained by the surrounding cameras, thereby obtaining the color of the helmet worn by each worker; The helmet comparison module is used to compare the acquired helmet color with the helmet color of the team with increased recognition priority.

7. The intelligent construction site management system according to claim 1, characterized in that: The worker priority enhancement unit includes a quantity analysis module and a safety helmet priority adjustment module; The quantity analysis module is used to perform quantity analysis on different helmet colors, obtain the quantity of each helmet color, and then extract the helmet color with the largest quantity; The helmet priority adjustment module is used to dynamically identify the position of workers wearing the most helmet colors in the image data obtained by the surrounding cameras, and dynamically adjust the recognition priority of the helmet colors in the team according to the recognition results.

8. A method for implementing a smart construction site management system, comprising the smart construction site management system according to any one of claims 1 to 7, characterized in that: The steps include: S1. Establish an image transmission connection with the facial recognition machine in the real-name access channel of the construction site and the surrounding cameras that monitor the real-name access channel. At the same time, connect to the construction site personnel management terminal, group and allocate worker information according to work groups, and then establish a worker database and a real-time record library for construction site workers. S2. The image data of workers entering the construction site through the facial recognition machine is retained, and the worker's team and helmet color are input into the construction site worker real-time record library based on the worker image data for recording; S3. Pre-identify the faces of workers leaving the site based on image data captured by surrounding cameras. Extract teams from the real-time record database of workers on the construction site based on the pre-identified faces. Input the extracted teams into the face recognition machine for priority enhancement. Compare the extracted helmet colors with those of the extracted teams. If the helmet colors recorded by the team contain other helmet colors, prioritize the workers with other helmet colors. S4. Quantitatively analyze the different helmet colors, extract the helmet color with the largest number, and dynamically identify its position in the image data acquired by the surrounding cameras. Dynamically adjust the recognition priority of the helmet color in the team based on the recognition result.

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