Intelligent construction site control method and system based on machine vision technology

Abstract

The present application relates to a method, system, computer equipment, storage medium and computer program product for intelligent construction site management based on machine vision technology. The method includes: obtaining identity recognition information; obtaining pulse feature sampling information, and processing the pulse feature sampling information through a preset heart rate algorithm to obtain heart rate data; outputting a preset concentration test image, and obtaining feedback image information while outputting the concentration test image; identifying key feature information in the feedback image information according to a preset feature recognition algorithm to obtain change trajectory information of the key feature information; judging whether the change trajectory information matches the preset concentration test image, and outputting fatigue test results according to the matching results. The use of this method can reduce the construction risks caused by fatigue of construction workers.

Description

Technical Field

[0001] The present application relates to the field of smart construction site technology, and in particular to a smart construction site management and control method, system, computer equipment, storage medium and computer program product based on machine vision technology. Background Art

[0002] With the development of artificial intelligence technology, more and more application solutions have emerged that integrate artificial intelligence into traditional industrial scenarios. The construction engineering industry, as a traditional industry, is also gradually moving towards intelligence.

[0003] The existing Chinese invention patent with publication number CN115514932A discloses a smart construction site management system based on the Internet of Things, including a management platform and several monitoring units and a mobile phone terminal. The management platform is connected to the several monitoring units and the mobile phone terminal through the Internet of Things. The management platform includes a model unit, an analysis unit, a storage unit, a display unit and a classification unit. The model unit is composed of a lightweight BIM model and a lightweight point cloud model. The analysis unit is a server cluster composed of multiple Internet of Things servers, and the model unit is installed on the analysis unit. The storage unit is implemented by any type of volatile or non-volatile storage device or a combination thereof. The classification unit designed by the present invention can number and group the monitoring units, so that information is transmitted according to the group during monitoring, reducing the load pressure on the server, and affecting the operation of the background business process to be fast, so as to quickly obtain the results of monitoring and analysis.

[0004] However, the system only manages materials, equipment and other supplies at the construction site, and is still unable to properly manage the construction workers who are indispensable at the construction site. Improper management of construction workers has led to fatigue and lack of concentration among construction workers, which has always been a major cause of construction accidents. Summary of the invention

[0005] Based on this, it is necessary to provide a smart construction site management method, system, computer equipment, computer-readable storage medium and computer program product based on machine vision technology that can reduce construction risks caused by construction worker fatigue in response to the above technical problems.

[0006] In a first aspect, the present application provides a smart construction site management method based on machine vision technology, the method comprising:

[0007] Obtain identification information;

[0008] Acquire pulse characteristic sampling information, and process the pulse characteristic sampling information by a preset heart rate algorithm to acquire heart rate data;

[0009] Output a preset concentration test image, and obtain feedback image information while outputting the concentration test image;

[0010] Identify key feature information in the feedback image information according to a preset feature recognition algorithm to obtain change trajectory information of the key feature information;

[0011] Determine whether the change trajectory information matches the preset concentration test image, and output the fatigue test result based on the matching result.

[0012] In one embodiment, the specific steps of presetting the heart rate algorithm include:

[0013] Segmenting the pulse characteristic sampling information to obtain single-cycle sampling information;

[0014] Fitting the single-cycle sampling information to obtain a double Gaussian function model matching the single-cycle sampling information;

[0015] The heart rate data is obtained by analyzing the peak features of the double Gaussian function model.

[0016] In one embodiment, the preset concentration test image is an indication image for guiding the tester's line of sight to obtain the tester's eye tracking feedback, and the preset concentration test image includes a facial positioning guide image and a line of sight guide icon. The specific steps of outputting the preset concentration test image include:

[0017] Outputting a facial positioning guide image so that the tester's face is located at a preset designated position;

[0018] Output a sight-guiding icon at a random position outside the boundary of the positioning guidance image.

[0019] In one embodiment, a specific method of outputting a sight line guidance icon at a random position outside the boundary of the positioning guidance image includes:

[0020] Outputting a first sight line guidance icon at a first random position outside the boundary of the positioning guidance image;

[0021] Outputting countdown prompt information at a first random position, and stopping outputting the first sight line guiding icon when the countdown corresponding to the countdown prompt information ends;

[0022] Outputting a second sight line guiding icon at a second random position outside the boundary of the positioning guiding image, wherein the distance between the second random position and the first random position is greater than a preset distance;

[0023] Randomly acquiring a sight line guiding path from a preset sight line guiding path library, outputting the sight line guiding path, and making an endpoint of the sight line guiding path coincide with the second random position;

[0024] A third sight line guiding animation is outputted, where the third sight line guiding animation is the second sight line guiding icon moving from the second random position along the sight line guiding path to another end point of the sight line guiding path.

[0025] In one embodiment, the specific steps of determining whether the change trajectory information matches the preset concentration test image and outputting the fatigue test result according to the matching result include:

[0026] If the change trajectory information does not match the preset concentration test image, and the heart rate data is greater than the preset heart rate threshold;

[0027] Then output a prompt message prohibiting entry into the construction site.

[0028] In one embodiment, the identity recognition information includes fingerprint information, and the pulse feature sampling information is a fingertip PPG signal obtained from the fingertips of the person being tested by photoplethysmography.

[0029] In a second aspect, the present application also provides a smart construction site management and control system based on machine vision technology, the system comprising:

[0030] Fingerprint collection module, used to obtain identity information;

[0031] A pulse sampling module is used to obtain pulse characteristic sampling information;

[0032] A display module, used for outputting a preset concentration test image;

[0033] An image acquisition module, used to obtain feedback image information;

[0034] The analysis and processing module is used to execute a preset heart rate algorithm to calculate the heart rate data through the pulse feature sampling information, to execute a preset feature recognition algorithm to identify the key feature information in the feedback image information, to obtain the change trajectory information of the key feature information, and to determine whether the change trajectory information matches the preset concentration test image, and then output the fatigue test result according to the matching result.

[0035] In a third aspect, the present application further provides a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0036] Obtain identification information;

[0037] Acquire pulse characteristic sampling information, and process the pulse characteristic sampling information by a preset heart rate algorithm to acquire heart rate data;

[0038] Output a preset concentration test image, and obtain feedback image information while outputting the concentration test image;

[0039] Identify key feature information in the feedback image information according to a preset feature recognition algorithm to obtain change trajectory information of the key feature information;

[0040] Determine whether the change trajectory information matches the preset concentration test image, and output the fatigue test result based on the matching result.

[0041] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0042] Obtain identification information;

[0043] Acquire pulse characteristic sampling information, and process the pulse characteristic sampling information by a preset heart rate algorithm to acquire heart rate data;

[0044] Output a preset concentration test image, and obtain feedback image information while outputting the concentration test image;

[0045] Identify key feature information in the feedback image information according to a preset feature recognition algorithm to obtain change trajectory information of the key feature information;

[0046] Determine whether the change trajectory information matches the preset concentration test image, and output the fatigue test result based on the matching result.

[0047] In a fifth aspect, the present application further provides a computer program product, the computer program product comprising a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0048] Obtain identification information;

[0049] Acquire pulse characteristic sampling information, and process the pulse characteristic sampling information by a preset heart rate algorithm to acquire heart rate data;

[0050] Output a preset concentration test image, and obtain feedback image information while outputting the concentration test image;

[0051] Identify key feature information in the feedback image information according to a preset feature recognition algorithm to obtain change trajectory information of the key feature information;

[0052] Determine whether the change trajectory information matches the preset concentration test image, and output the fatigue test result based on the matching result.

[0053] The above-mentioned intelligent construction site management and control method, system, computer equipment, storage medium and computer program product based on machine vision technology identify the identity information of the construction personnel before they enter the construction site to determine whether they have the authority to enter the construction site, and then collect the heart rate of the construction personnel and use concentration tests to determine whether the construction personnel are in a state of inattention and fatigue. If so, they are not allowed to enter the construction site, thereby reducing the probability of construction accidents caused by employee fatigue. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is an application environment diagram of a smart construction site management and control method based on machine vision technology in one embodiment;

[0055] Figure 2 A flowchart of a smart construction site management and control method based on machine vision technology in one embodiment;

[0056] Figure 3 is a flow chart of step S300 in one embodiment;

[0057] Figure 4 is a flow chart of step S320 in one embodiment;

[0058] Figure 5 is a schematic diagram of a double Gaussian function model in one embodiment;

[0059] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] The intelligent construction site management method based on machine vision technology provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be but is not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0062] In one embodiment, Figure 2 As shown in the figure, a smart construction site management method based on machine vision technology is provided. Figure 1 The terminal 102 in the example is used as an example to illustrate, and the following steps are included:

[0063] Step S100: Obtain identity information.

[0064] In an embodiment of the present application, the identity recognition information is fingerprint information collected by a fingerprint collection module. When establishing a server database, fingerprint information of construction personnel who have the authority to enter the construction site can be collected and stored in the database. Before entering the construction site, the fingerprints of the personnel entering the construction site are collected and verified by the fingerprint collection module to obtain the identity recognition information. The collected identity recognition information is then matched with the fingerprint information stored in the database. If the match is successful, it means that the person has the authority to enter the construction site and can be tested for concentration. If the match fails, it is prompted that the person does not have the authority to enter the construction site and no subsequent concentration test will be performed.

[0065] Step S200: Acquire pulse characteristic sampling information, and process the pulse characteristic sampling information using a preset heart rate algorithm to acquire heart rate data.

[0066] In the embodiment of the present application, the pulse feature sampling information is the fingertip PPG signal obtained from the fingertips of the person being tested by photoplethysmography. Since the identity authentication information in the present embodiment is fingerprint information, the pulse feature sampling information is also sampled from the fingertips, so steps S100 and S200 can be completed simultaneously when the personnel entering the construction site place the fingertips on the collection module, that is, the fingerprint and the pulse at the fingertips are collected at the same time. In a more preferred embodiment, the fingerprint collection module and the PPG signal collection module can be integrated to make the process of obtaining the pulse sampling feature information more seamless. While simplifying the workers' operating steps, it also reduces the workers' defense against collecting pulse feature sampling information, thereby improving the accuracy of the concentration test results.

[0067] The specific steps of presetting the heart rate algorithm include:

[0068] Step S210: segment the pulse feature sampling information to obtain single-cycle sampling information.

[0069] Step S220: Fitting the single-cycle sampling information to obtain a double Gaussian function model matching the single-cycle sampling information.

[0070] In step S210-step S220, the single-cycle sampling information is to segment the complete PPG signal according to the waveform characteristics of a single pulse. In this embodiment, when performing pulse adjustment sampling, a sampling time can be preset, for example, the sampling time is 5 seconds, and the PPG signal collected within 5 seconds can be segmented to obtain a number of single-cycle sampling information, and then waveform fitting is performed with the help of the data in the single-cycle sampling information, and the fitting of the single-cycle sampling information is completed using the double Gaussian function model. Specifically, the expression of the double Gaussian function model in this embodiment is as follows:

[0071]

[0072] Step S230: Acquire heart rate data based on the peak features of the double Gaussian function model.

[0073] In this embodiment, the image of the double Gaussian function model is as follows: Figure 6 As shown, after fitting each segmented single-cycle sampling information, several fitted waveforms of single-cycle sampling information can be obtained. After matching and identifying several fitted waveforms, the number of pulses contained therein that matches the actual pulse waveform can be determined. The matched number of pulses is the number of pulses that appears in the preset 5-second sampling duration. The pulse number is then geometrically expanded to the number of pulses corresponding to a duration of 1 minute, and the estimated value of the pulse number per minute can be obtained. The estimated value of the pulse number per minute is the required heart rate data.

[0074] Step S300: outputting a preset concentration test image, and obtaining feedback image information while outputting the concentration test image.

[0075] Among them, the concentration test image is image information displayed by the display module and used to test the attention of personnel entering the construction site. The feedback image is video stream information obtained by the image acquisition device. The content recorded in the video stream information is the real-time reflection status of the personnel entering the construction site to the concentration test image displayed by the display module, especially the state of the eyes of the personnel entering the construction site. By outputting the concentration test image and collecting the real-time facial state of the personnel entering the construction site, the state of their eyes is analyzed to judge their concentration. The specific steps of outputting the preset concentration test image include:

[0076] Step S310: outputting a face positioning guide image to locate the tester's face at a preset designated position.

[0077] Among them, the facial positioning guidance image is the facial boundary contour information displayed on the display module. Specifically, the facial boundary contour information can be preset as an elliptical contour, and the contour is output to the display module to guide the personnel entering the construction site to move their heads so that the image captured by the image acquisition device is an image of their face appearing within the elliptical contour area. By limiting the position of the head of the personnel entering the construction site, it can facilitate subsequent eye recognition.

[0078] Step S320: outputting a sightline guidance icon at a random position outside the boundary of the positioning guidance image.

[0079] The specific steps of outputting the sight line guidance icon at a random position outside the boundary of the positioning guidance image include:

[0080] Step S321: outputting a first sightline guide icon at a first random position outside the boundary of the positioning guide image.

[0081] Among them, the first implementation guide icon is a graphic mark of an arbitrary shape, which is used to guide the line of sight. The first random position information is the position information obtained by randomly selecting the area outside the elliptical outline of the aforementioned facial boundary contour information. In order to make the line of sight changes easier to detect, the position close to the edge of the display area of ​​the display module is preferably used as the first random position.

[0082] Step S322: outputting countdown prompt information at a first random position, and stopping outputting the first sight line guide icon when the countdown corresponding to the countdown prompt information ends.

[0083] Among them, after the first sight line guide icon is output, the countdown prompt information output at the first random position covers the first sight line guide icon, and the countdown duration corresponding to the countdown prompt information is a preset duration. In this embodiment, the preset countdown duration is 3 seconds. After the countdown duration ends, the display of the first sight line guide icon is stopped, that is, it disappears from the display area of ​​the display module.

[0084] Step S323: outputting a second sight line guiding icon at a second random position outside the boundary of the positioning guiding image, wherein the distance between the second random position and the first random position is greater than a preset distance.

[0085] Among them, the second sight line guiding icon is output immediately after the first sight line guiding icon stops displaying, and its purpose is to remind the people participating in the test to concentrate through countdown, and then switch the position and change the shape of the second sight line guiding icon. In this way, the reaction speed of the people participating in the test is tested. In particular, by setting a preset distance, the distance between the second random position and the first random position is enlarged, so that the tester's sight change is more obvious, and it is also easier to detect the change when collecting the tester's reaction information.

[0086] Step S324: randomly obtaining a sight line guiding path from a preset sight line guiding path library, outputting the sight line guiding path, and making an endpoint of the sight line guiding path coincide with the second random position.

[0087] Step S325: outputting a third sight line guiding animation, where the third sight line guiding animation is the second sight line guiding icon moving from the second random position along the sight line guiding path to another end point of the sight line guiding path.

[0088] Through the above steps, a continuous animation content of the second sight guiding icon moving along the sight guiding path can be displayed on the display module. Through the continuous animation content, the test subject's sight can be guided, and especially the eye features can be changed to match the sight guiding path. By identifying the eye change path and matching the result with the sight guiding path, the test of the test subject's concentration can be completed.

[0089] Step S330: when outputting the face positioning guidance image information, start acquiring the feedback image information, and record the starting timestamp information of starting to acquire the feedback image information.

[0090] The feedback image information is the video information of the tester's facial changes acquired by the image acquisition module during the execution of steps S321 to S325, and the start timestamp information is the time information when the first frame of the feedback image information is acquired.

[0091] Step S400: identifying key feature information in the feedback image information according to a preset feature recognition algorithm to obtain change trajectory information of the key feature information.

[0092] Among them, the key feature information includes the timestamp information when the first sight guide icon and the second sight guide icon are output, and the start time and the end time of the third sight guide animation are output. The key information also includes the timestamp information corresponding to the change in the eye contour features of the tester in the feedback image information, and the information of the eye contour feature change path. The time difference information is calculated by comparing the timestamp information of the appearance of the first sight guide icon with the timestamp information of the first change of the eye contour features corresponding to the first random position after the time, and recorded as the first time reference value. The time difference information is calculated by comparing the timestamp information of the appearance of the second sight guide icon with the timestamp information of the first change of the eye contour features corresponding to the second random position after the time, and recorded as the second time reference value. The first time reference value and the second time reference value are averaged to obtain the reaction time reference value.

[0093] Step S500: determining whether the change trajectory information matches a preset concentration test image, and outputting a fatigue test result according to the matching result.

[0094] In this step, the eye contour feature change path within the start time and end time of the output third sight line guiding animation is matched with the sight line guiding path, and the matching degree is used as the attention reference value. The attention reference value and the reaction time reference value are compared with the preset threshold value to obtain the fatigue test result.

[0095] Step S510: If the change trajectory information does not match the preset concentration test image, and the heart rate data is greater than the preset heart rate threshold.

[0096] In the embodiment of the present application, a match between the eye contour feature change path and the sight line guidance path lower than 60% is considered as a mismatch, and a match higher than 60%. The heart rate threshold is 100 beats / minute.

[0097] Step S520: Output a prompt message prohibiting entry into the construction site.

[0098] If the attention reference value, that is, the matching degree between the eye contour feature change path and the sight guidance path is lower than 60%, it means that the concentration of the test participant does not meet the standard, and the person's heart rate also exceeds the normal range of 100 beats / minute. It can be determined that the person is in a relatively fatigued state. If an unexpected situation occurs at the construction site, there is a high possibility that the person will not be able to respond in time. Restricting his entry into the construction site can effectively reduce the probability of injury to the person.

[0099] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0100] Based on the same inventive concept, the embodiment of the present application also provides a smart construction site control system based on machine vision technology for implementing the smart construction site control method based on machine vision technology involved above. The implementation solution for solving the problem provided by the system is similar to the implementation solution recorded in the above method, so the specific limitations in one or more embodiments of the smart construction site control system based on machine vision technology provided below can be referred to the limitations of the smart construction site control method based on machine vision technology above, and will not be repeated here.

[0101] In one embodiment, a smart construction site management and control system based on machine vision technology is provided, comprising:

[0102] Fingerprint collection module, used to obtain identity information;

[0103] A pulse sampling module is used to obtain the pulse characteristic sampling information of the fingertips. Preferably, the fingerprint acquisition module and the pulse acquisition module can be integrated to complete the work of fingerprint acquisition and pulse sampling at the same time;

[0104] A display module, used for outputting a preset concentration test image;

[0105] An image acquisition module, used to obtain feedback image information;

[0106] The analysis and processing module is used to execute a preset heart rate algorithm to calculate the heart rate data through the pulse feature sampling information, to execute a preset feature recognition algorithm to identify the key feature information in the feedback image information, to obtain the change trajectory information of the key feature information, and to determine whether the change trajectory information matches the preset concentration test image, and then output the fatigue test result according to the matching result.

[0107] By setting up the intelligent construction site management and control system based on machine vision technology at the entrance of the construction site or in high-risk areas, people entering the construction site can be detected, and people in a fatigued state can be restricted from entering the construction site or specific areas, thereby reducing the probability of casualties at the construction site.

[0108] Each module in the above-mentioned intelligent construction site management and control system based on machine vision technology can be implemented in whole or in part through software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0109] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WI FI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a smart construction site management method based on machine vision technology is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0110] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0111] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0112] Step S100: Obtain identity information.

[0113] Step S200: Acquire pulse characteristic sampling information, and process the pulse characteristic sampling information using a preset heart rate algorithm to acquire heart rate data.

[0114] The specific steps of presetting the heart rate algorithm include:

[0115] Step S210: segment the pulse feature sampling information to obtain single-cycle sampling information.

[0116] Step S220: Fitting the single-cycle sampling information to obtain a double Gaussian function model matching the single-cycle sampling information.

[0117] Step S230: Acquire heart rate data based on the peak features of the double Gaussian function model.

[0118] Step S300: outputting a preset concentration test image, and obtaining feedback image information while outputting the concentration test image.

[0119] The specific steps of outputting a preset concentration test image include:

[0120] Step S310: outputting a face positioning guide image to locate the tester's face at a preset designated position.

[0121] Step S320: outputting a sightline guidance icon at a random position outside the boundary of the positioning guidance image.

[0122] The specific steps of outputting the sight line guidance icon at a random position outside the boundary of the positioning guidance image include:

[0123] Step S321: outputting a first sightline guide icon at a first random position outside the boundary of the positioning guide image.

[0124] Step S322: outputting countdown prompt information at a first random position, and stopping outputting the first sight line guide icon when the countdown corresponding to the countdown prompt information ends.

[0125] Step S323: outputting a second sight line guiding icon at a second random position outside the boundary of the positioning guiding image, wherein the distance between the second random position and the first random position is greater than a preset distance.

[0126] Step S324: randomly obtaining a sight line guiding path from a preset sight line guiding path library, outputting the sight line guiding path, and making an endpoint of the sight line guiding path coincide with the second random position.

[0127] Step S325: outputting a third sight line guiding animation, where the third sight line guiding animation is the second sight line guiding icon moving from the second random position along the sight line guiding path to another end point of the sight line guiding path.

[0128] Step S330: when outputting the face positioning guidance image information, start acquiring the feedback image information, and record the starting timestamp information of starting to acquire the feedback image information.

[0129] Step S400: identifying key feature information in the feedback image information according to a preset feature recognition algorithm to obtain change trajectory information of the key feature information.

[0130] Step S500: determining whether the change trajectory information matches a preset concentration test image, and outputting a fatigue test result according to the matching result.

[0131] The specific steps include:

[0132] Step S510: If the change trajectory information does not match the preset concentration test image, and the heart rate data is greater than the preset heart rate threshold.

[0133] Step S520: Output a prompt message prohibiting entry into the construction site.

[0134] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0135] Step S100: Obtain identity information.

[0136] Step S200: Acquire pulse characteristic sampling information, and process the pulse characteristic sampling information using a preset heart rate algorithm to acquire heart rate data.

[0137] The specific steps of presetting the heart rate algorithm include:

[0138] Step S210: segment the pulse feature sampling information to obtain single-cycle sampling information.

[0139] Step S220: Fitting the single-cycle sampling information to obtain a double Gaussian function model matching the single-cycle sampling information.

[0140] Step S230: Acquire heart rate data based on the peak features of the double Gaussian function model.

[0141] Step S300: outputting a preset concentration test image, and obtaining feedback image information while outputting the concentration test image.

[0142] The specific steps of outputting a preset concentration test image include:

[0143] Step S310: outputting a face positioning guide image to locate the tester's face at a preset designated position.

[0144] Step S320: outputting a sightline guidance icon at a random position outside the boundary of the positioning guidance image.

[0145] The specific steps of outputting the sight line guidance icon at a random position outside the boundary of the positioning guidance image include:

[0146] Step S321: outputting a first sightline guide icon at a first random position outside the boundary of the positioning guide image.

[0147] Step S322: outputting countdown prompt information at a first random position, and stopping outputting the first sight line guide icon when the countdown corresponding to the countdown prompt information ends.

[0148] Step S323: outputting a second sight line guiding icon at a second random position outside the boundary of the positioning guiding image, wherein the distance between the second random position and the first random position is greater than a preset distance.

[0149] Step S324: randomly obtaining a sight line guiding path from a preset sight line guiding path library, outputting the sight line guiding path, and making an endpoint of the sight line guiding path coincide with the second random position.

[0150] Step S325: outputting a third sight line guiding animation, where the third sight line guiding animation is the second sight line guiding icon moving from the second random position along the sight line guiding path to another end point of the sight line guiding path.

[0151] Step S330: when outputting the face positioning guidance image information, start acquiring the feedback image information, and record the starting timestamp information of starting to acquire the feedback image information.

[0152] Step S400: identifying key feature information in the feedback image information according to a preset feature recognition algorithm to obtain change trajectory information of the key feature information.

[0153] Step S500: determining whether the change trajectory information matches a preset concentration test image, and outputting a fatigue test result according to the matching result.

[0154] The specific steps include:

[0155] Step S510: If the change trajectory information does not match the preset concentration test image, and the heart rate data is greater than the preset heart rate threshold.

[0156] Step S520: Output a prompt message prohibiting entry into the construction site.

[0157] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0158] Step S100: Obtain identity information.

[0159] Step S200: Acquire pulse characteristic sampling information, and process the pulse characteristic sampling information using a preset heart rate algorithm to acquire heart rate data.

[0160] The specific steps of presetting the heart rate algorithm include:

[0161] Step S210: segment the pulse feature sampling information to obtain single-cycle sampling information.

[0162] Step S220: Fitting the single-cycle sampling information to obtain a double Gaussian function model matching the single-cycle sampling information.

[0163] Step S230: Acquire heart rate data based on the peak features of the double Gaussian function model.

[0164] Step S300: outputting a preset concentration test image, and obtaining feedback image information while outputting the concentration test image.

[0165] The specific steps of outputting a preset concentration test image include:

[0166] Step S310: outputting a face positioning guide image to locate the tester's face at a preset designated position.

[0167] Step S320: outputting a sightline guidance icon at a random position outside the boundary of the positioning guidance image.

[0168] The specific steps of outputting the sight line guidance icon at a random position outside the boundary of the positioning guidance image include:

[0169] Step S321: outputting a first sightline guide icon at a first random position outside the boundary of the positioning guide image.

[0170] Step S322: outputting countdown prompt information at a first random position, and stopping outputting the first sight line guide icon when the countdown corresponding to the countdown prompt information ends.

[0171] Step S323: outputting a second sight line guiding icon at a second random position outside the boundary of the positioning guiding image, wherein the distance between the second random position and the first random position is greater than a preset distance.

[0172] Step S324: randomly obtaining a sight line guiding path from a preset sight line guiding path library, outputting the sight line guiding path, and making an endpoint of the sight line guiding path coincide with the second random position.

[0173] Step S325: outputting a third sight line guiding animation, where the third sight line guiding animation is the second sight line guiding icon moving from the second random position along the sight line guiding path to another end point of the sight line guiding path.

[0174] Step S330: when outputting the face positioning guidance image information, start acquiring the feedback image information, and record the starting timestamp information of starting to acquire the feedback image information.

[0175] Step S400: identifying key feature information in the feedback image information according to a preset feature recognition algorithm to obtain change trajectory information of the key feature information.

[0176] Step S500: determining whether the change trajectory information matches a preset concentration test image, and outputting a fatigue test result according to the matching result.

[0177] The specific steps include:

[0178] Step S510: If the change trajectory information does not match the preset concentration test image, and the heart rate data is greater than the preset heart rate threshold.

[0179] Step S520: Output a prompt message prohibiting entry into the construction site.

[0180] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0181] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0182] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0183] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A smart construction site management and control method based on machine vision technology, characterized in that: The method comprises: Obtain identification information; Acquire pulse characteristic sampling information, and process the pulse characteristic sampling information by a preset heart rate algorithm to acquire heart rate data; Output a preset concentration test image, and obtain feedback image information while outputting the concentration test image; Identify key feature information in the feedback image information according to a preset feature recognition algorithm to obtain change trajectory information of the key feature information; Determine whether the change trajectory information matches the preset concentration test image, and output the fatigue test result according to the matching result; The preset concentration test image is an indication image for guiding the tester's sight line to obtain the tester's eye tracking feedback. The preset concentration test image includes a facial positioning guide image and a sight line guide icon. The specific steps of outputting the preset concentration test image include: Outputting a facial positioning guide image so that the tester's face is located at a preset designated position; Outputting a sight guidance icon at a random position outside the boundary of the positioning guidance image; The specific method of outputting the sight line guidance icon at a random position outside the boundary of the positioning guidance image includes: Outputting a first sight line guidance icon at a first random position outside the boundary of the positioning guidance image; Outputting countdown prompt information at a first random position, and stopping outputting the first sight line guiding icon when the countdown corresponding to the countdown prompt information ends; Outputting a second sight line guiding icon at a second random position outside the boundary of the positioning guiding image, wherein the distance between the second random position and the first random position is greater than a preset distance; Randomly acquiring a sight line guiding path from a preset sight line guiding path library, outputting the sight line guiding path, and making an endpoint of the sight line guiding path coincide with the second random position; A third sight line guiding animation is outputted, where the third sight line guiding animation is the second sight line guiding icon moving from the second random position along the sight line guiding path to another end point of the sight line guiding path.

2. The intelligent construction site management and control method based on machine vision technology according to claim 1 is characterized in that: The specific steps of determining whether the change trajectory information matches the preset concentration test image and outputting the fatigue test result according to the matching result include: If the change trajectory information does not match the preset concentration test image, and the heart rate data is greater than the preset heart rate threshold; Then output a prompt message prohibiting entry into the construction site.

3. The intelligent construction site management and control method based on machine vision technology according to any one of claims 1 or 2, characterized in that: The identity recognition information includes fingerprint information, and the pulse feature sampling information is a fingertip PPG signal obtained from the fingertips of the person being tested through photoplethysmography.

4. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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