Construction behavior data processing method and device based on artificial intelligence and electronic equipment

Abstract

The application provides a construction behavior data processing method and device based on artificial intelligence and electronic equipment, relates to the technical field of data processing, and solves the technical problem of low accuracy of construction behavior analysis of construction personnel in a construction site. The method comprises the following steps: in response to a pan-tilt device being connected to a target terminal, the pan-tilt device is used to control the target terminal to follow the real-time position of a target construction personnel by using a feature point tracking algorithm and a movement detection algorithm and to collect construction behavior images corresponding to the target construction personnel; the construction behavior images and power consumption data of all target construction equipment used by the target construction personnel in the construction process are sent to a total control server, and the total control server is used to analyze the construction behavior of the target construction personnel according to the construction behavior images and the power consumption data by using an artificial intelligence (AI) system, so as to obtain a construction behavior analysis result of the construction site.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus and electronic device for processing construction behavior data based on artificial intelligence. Background Technology

[0002] Currently, traditional methods for analyzing construction site behavior mainly rely on a combination of manual observation and basic tools to achieve operational monitoring and safety management. A typical methodology includes inspection and judgment, employing fixed procedures such as observing the integrity of guardrails, listening for unusual mechanical noises, and smelling for gas leaks. However, relying on experience to judge safety hazards, such as assessing structural stability through the degree of rust on scaffolding fasteners, results in relatively low accuracy in analyzing the construction behavior of workers on-site. Summary of the Invention

[0003] The purpose of this invention is to provide a construction behavior data processing method, device, and electronic device based on artificial intelligence, so as to solve the technical problem of low accuracy in analyzing the construction behavior of construction workers on construction sites.

[0004] Firstly, this application provides a construction behavior data processing method based on artificial intelligence, applied to a construction behavior monitoring system at a construction site, the method comprising: In response to the Near Field Communication (NFC) device located at the construction site identifying the target terminal corresponding to the target construction worker, a power supply command is sent to the power control device at the construction site to activate the power supply for the target construction equipment; wherein, the target construction equipment is a preset construction equipment required for the construction process of the target construction worker; a gimbal device is correspondingly installed at the location of the NFC device; In response to the gimbal device being connected to the target terminal, the gimbal device is used to control the target terminal to follow the real-time location of the target construction worker and collect images of the construction behavior corresponding to the target construction worker through feature point tracking algorithm and motion detection algorithm; The construction behavior images and the power consumption data of all the target construction equipment used by the target construction personnel during the construction process are sent to the central control server. The central control server then uses an artificial intelligence (AI) system to analyze the construction behavior of the target construction personnel based on the construction behavior images and the power consumption data to obtain the on-site construction behavior analysis results.

[0005] In one possible implementation, a speaker and a sound sensor are also provided at the location of the NFC device; After the NFC device located at the construction site identifies the target terminal corresponding to the target construction worker and sends a power-on command for the target construction equipment to the power control device at the construction site, the method further includes: In response to the sound sensor detecting that the volume of the construction sound is greater than a preset volume, a negative sound wave with the opposite direction and the same wave value as the positive sound wave corresponding to the construction sound is determined based on the construction sound. The target sound corresponding to the negative sound wave is determined, and the speaker is controlled to play the target sound, so as to achieve active noise reduction by canceling out the construction sound.

[0006] In one possible implementation, the target construction worker's work shoes at the construction site are equipped with pressure sensors on the bottom; The step involves using an AI system to analyze the construction behavior of the target construction workers based on the construction behavior images and electricity consumption data, resulting in on-site construction behavior analysis results, including: Based on each frame of data in the construction behavior image, the speed of the target construction worker's construction action is identified using a Kalman filter algorithm and a posture estimation model. The amount of handling work performed by the target construction worker during the construction process is determined based on the pressure data collected by the pressure sensor and the weight of the target construction worker. Based on the construction action speed, the amount of handling operations, the construction time of the target construction personnel, the electricity consumption data, and the current construction progress corresponding to the construction process, the AI ​​system analyzes the construction behavior efficiency of the target construction personnel and determines the construction behavior efficiency as at least part of the on-site construction behavior analysis results.

[0007] In one possible implementation, after analyzing the efficiency of the construction behavior of the target construction personnel using an AI system based on the construction action speed, the amount of material handling, the construction time of the target construction personnel, the electricity consumption data, and the current construction progress corresponding to the construction process, the method further includes: Obtain the target construction behavior efficiency of each of the multiple target construction workers and the specified total working time of each of the multiple target construction workers; Based on the various construction projects at the construction site, the efficiency of multiple target construction behaviors, and the specified total working hours, the AI ​​system dynamically allocates the scheduling data among multiple target construction personnel. For each construction project, target construction personnel corresponding to the efficiency of the target construction behavior that is greater than the specified construction efficiency of the project are assigned to obtain the scheduling results of multiple target construction personnel. In one possible implementation, the step of using an AI system to analyze the construction behavior of the target construction worker based on the construction behavior image and the electricity consumption data to obtain the construction behavior analysis results at the construction site includes: Based on the electricity consumption data, the peak electricity consumption, peak time, low electricity consumption, low time, total electricity consumption, and electricity consumption change status of the target construction equipment during the construction process are analyzed. Based on the data of each frame in the construction behavior image, the amplitude and speed data of the target construction worker's construction movements are identified by the Kalman filter algorithm and the attitude estimation model; Based on the peak electricity consumption, peak time, low electricity consumption, low time, total electricity consumption, electricity consumption change status, and construction action amplitude and speed data, the AI ​​system analyzes the total construction workload corresponding to the construction behavior of the target construction personnel, and determines the total construction workload as at least part of the on-site construction behavior analysis results.

[0008] In one possible implementation, it also includes: In response to the total workload exceeding the preset workload, the target terminal is controlled to issue a fatigue work warning tone; or, In response to the total amount of work being less than or equal to the preset amount of work, the target terminal is controlled to emit a prompt sound to accelerate the construction progress.

[0009] In one possible implementation, the step of using an AI system to analyze the construction behavior of the target construction worker based on the construction behavior image and the electricity consumption data to obtain the construction behavior analysis results at the construction site includes: Based on the data of each frame in the construction behavior image, the target construction worker is tracked across frames using a human body tracking algorithm, so that the same target construction worker can be associated between different frames in the construction behavior image; OpenPose is used to identify the key movement parts of the associated target construction workers, and based on the positional changes of the key movement parts between consecutive frames, the displacement distance, displacement direction and displacement time interval of the key movement parts are analyzed to obtain the movement angle data, movement amplitude data and movement speed data of the key movement parts. Based on the action angle data, action amplitude data, and action speed data, analyze whether the construction behavior of the target construction worker conforms to the specified construction action specifications. In response to a construction action that does not conform to the specified specifications, the target terminal and the central control server are controlled to issue a construction hazard warning signal.

[0010] Secondly, this application provides an artificial intelligence-based construction behavior data processing device, applied to a construction behavior monitoring system at a construction site, comprising: The sending module is used to respond to an NFC device located at the construction site recognizing a target terminal corresponding to a target construction worker, and to send a power-on command to the power control device at the construction site for the target construction equipment; wherein, the target construction equipment is a preset construction equipment required by the target construction worker for the construction process; and a gimbal device is correspondingly installed at the location of the NFC device; The control module is used to respond to the gimbal device being connected to the target terminal, and to use the gimbal device to control the target terminal to follow the real-time position of the target construction worker and collect images of the construction behavior corresponding to the target construction worker through feature point tracking algorithm and motion detection algorithm; The analysis module is used to send the construction behavior images and the power consumption data of all the target construction equipment used by the target construction personnel during the construction process to the central control server. The central control server then uses an AI system to analyze the construction behavior of the target construction personnel based on the construction behavior images and power consumption data to obtain the construction behavior analysis results at the construction site.

[0011] Thirdly, this application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the method described in the first aspect above.

[0012] Fourthly, this application also provides a computer-readable storage medium storing computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method described in the first aspect above.

[0013] This application brings the following beneficial effects: This application provides a construction behavior data processing method, apparatus, and electronic device based on artificial intelligence. In response to an NFC device located at a construction site identifying a target terminal corresponding to a target construction worker, the device sends a power-on command to the corresponding power control equipment at the construction site to activate the target construction equipment. The target construction equipment is a preset construction device required for the construction process of the target construction worker. A gimbal device is positioned at the location of the NFC device. In response to the gimbal device connecting to the target terminal, the device controls the target terminal to follow the real-time location of the target construction worker using feature point tracking and motion detection algorithms, and collects data on the construction behavior of the target construction worker. The system generates construction behavior images; it then sends these images, along with power consumption data for all target construction equipment used by the target construction personnel during the construction process, to a central control server. The server then uses an AI system to analyze the construction behavior of the target personnel based on the images and power consumption data, resulting in an analysis of the construction behavior at the construction site. In this solution, when a target construction worker enters the construction site and is identified by an NFC device located there, the system automatically powers on the pre-set construction equipment required by the worker. This ensures that the equipment is used only when needed, and this automated management improves work efficiency and safety. When the target terminal carried by the construction worker is connected to the NFC device... A connection is established between the PTZ device and the NFC-enabled device. The PTZ device then uses feature point tracking and motion detection algorithms to track the target construction worker's location in real time, achieving precise location tracking and providing accurate spatial positioning data for subsequent behavior analysis. During the tracking process, the PTZ device captures images of the target construction worker's work behavior. These images contain detailed information about how the worker operates equipment and performs tasks, providing valuable data for analyzing their work behavior. Simultaneously, the system records the power consumption data of all construction equipment used by the target construction worker throughout the construction process. This data not only reflects equipment usage but also indirectly reflects the worker's work efficiency and habits. Construction activity images and electricity consumption data are sent to the central control server for in-depth analysis by an artificial intelligence (AI) system. This AI system can process large amounts of images and electricity consumption data, identifying patterns and anomalies to provide analytical data on construction behavior. Combined with high-precision location tracking data, detailed construction activity images, and electricity consumption data reflecting work patterns, the AI ​​system can more accurately analyze the behavior of construction workers, significantly improving the accuracy of on-site construction behavior analysis. Therefore, by collecting detailed data from multiple perspectives and employing AI analysis technology, the accuracy of analyzing the behavior of construction workers on construction sites is effectively improved, solving the technical problem of low accuracy in analyzing the construction behavior of workers on construction sites.

[0014] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the construction behavior data processing method based on artificial intelligence provided in this application embodiment; Figure 2 An example of a monitoring interface in the construction behavior data processing method based on artificial intelligence provided in the embodiments of this application; Figure 3 A schematic diagram of the structure of a construction behavior data processing device based on artificial intelligence provided in an embodiment of this application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] The terms "comprising" and "having," and any variations thereof, used in the embodiments of this application, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0019] Currently, the accuracy of analyzing the construction behavior of workers on construction sites is relatively low. Therefore, this application provides an artificial intelligence-based method, apparatus, and electronic device for processing construction behavior data. This method can solve the technical problem of low accuracy in analyzing the construction behavior of workers on construction sites.

[0020] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0021] Figure 1 This is a flowchart illustrating an artificial intelligence-based construction behavior data processing method provided in an embodiment of this application. The method is applied to a construction behavior monitoring system at a construction site. Figure 1 As shown, the method includes: Step S110: In response to the NFC device set up at the construction site recognizing the target terminal corresponding to the target construction worker, a power supply command for the target construction equipment is sent to the power supply control equipment at the construction site.

[0022] Among them, the target construction equipment is the preset construction equipment required by the target construction personnel for the construction process; a gimbal device is set at the location of the NFC device.

[0023] One possible implementation involves a construction worker entering the construction site carrying their target terminal (e.g., a smartphone, RFID tag, or other device identifiable by an NFC device). When the target terminal enters the effective identification range of the NFC device, the NFC device identifies it. The system authenticates the identified target terminal to determine the identity of the construction worker and their corresponding pre-set construction equipment. It confirms whether the construction worker has permission to use the specific construction equipment. If authentication is successful and permissions are confirmed, the system sends a power-on command to the corresponding power control equipment at the construction site, targeting the pre-set construction equipment required by the construction worker. Upon receiving the command, the power control equipment provides power to the target construction equipment, putting it into standby or operational mode. A gimbal device is positioned at the location of the NFC device. When the target terminal is identified and connected to the gimbal device, the gimbal device is activated.

[0024] Step S120: In response to the PTZ device connecting to the target terminal, the PTZ device controls the target terminal to follow the real-time location of the target construction personnel and collect images of the construction behavior corresponding to the target construction personnel through feature point tracking algorithm and motion detection algorithm.

[0025] The feature point tracking and motion detection algorithms on the PTZ device begin initialization. These algorithms are likely pre-loaded software modules on the PTZ device, used for real-time analysis of visual information in the video stream. The feature point tracking algorithm identifies specific feature points on the target construction worker (such as clothing color patterns, reflective markings, etc.) and continuously tracks the positional changes of these feature points in the video stream. Figure 2As shown, motion detection algorithms monitor movement within the frame to help pinpoint the actual location of construction workers more accurately and adapt to dynamic changes in different environments. During tracking, the PTZ device simultaneously records video or captures images, documenting the work scene and actions of the construction workers. For example... Figure 2 As shown, these image data can be used for various purposes such as subsequent quality control, safety inspection, and efficiency evaluation.

[0026] Step S130: Send the construction behavior images and the power consumption data of all target construction equipment used by the target construction personnel during the construction process to the central control server. The central control server then uses the AI ​​system to analyze the construction behavior of the target construction personnel based on the construction behavior images and power consumption data to obtain the construction behavior analysis results at the construction site.

[0027] In some embodiments, pressure sensors are installed on the soles of the work shoes worn by the target construction workers at the construction site; the above-mentioned analysis of the construction behavior of the target construction workers using an AI system based on construction behavior images and electricity consumption data to obtain the construction behavior analysis results at the construction site may specifically include the following steps: Based on each frame of data from construction behavior images, the Kalman filter algorithm and attitude estimation model are used to identify the construction speed of the target construction workers. Pressure data collected by pressure sensors and the weight of the target construction workers themselves are used to determine the amount of material handling they perform during construction. Based on the construction speed, the amount of material handling, the construction time, electricity consumption, and the current construction progress, an AI system is used to analyze the efficiency of the target construction workers' behavior, and this efficiency is identified as at least a portion of the site construction behavior analysis results. Through these data analysis methods, the site construction behavior analysis results become more accurate.

[0028] In some embodiments, the above-mentioned analysis of the construction behavior of target construction workers using an AI system based on construction behavior images and electricity consumption data to obtain on-site construction behavior analysis results may specifically include the following steps: Based on electricity consumption data analysis, the peak electricity consumption, peak time, low electricity consumption, low time, total electricity consumption, and electricity consumption change status of the target construction equipment during the construction process are analyzed; based on the data of each frame in the construction behavior image, the amplitude and speed data of the construction actions of the target construction personnel are identified through Kalman filter algorithm and attitude estimation model. Based on data such as peak electricity consumption, peak time, low electricity consumption, low time, total electricity consumption, electricity consumption change status, and the magnitude and speed of construction actions, the AI ​​system is used to analyze the total workload of the target construction personnel corresponding to their construction behaviors, and the total workload is determined as at least part of the analysis results of the on-site construction behaviors. In response to the total amount of work being completed exceeding the preset amount, the control target terminal will issue a fatigue work warning tone; or, in response to the total amount of work being completed being less than or equal to the preset amount, the control target terminal will issue a prompt tone to accelerate the construction progress.

[0029] By combining electricity consumption data analysis (including peak electricity consumption, peak hours, off-peak hours, off-peak hours, total electricity consumption, and changes in electricity consumption) with construction behavior image recognition (such as the amplitude and speed of construction actions), comprehensive monitoring of the construction process has been achieved. This enables managers to obtain more accurate and detailed information about the construction site, providing strong support for scientific decision-making.

[0030] In this embodiment, based on the comparison between the total workload and the preset workload, the system can automatically determine whether the construction workers are in a state of fatigue or need to speed up the construction progress. When the workload of the construction workers is detected to exceed the safety threshold, a fatigue warning sound will be issued in a timely manner to remind the relevant personnel to take measures to avoid safety accidents caused by overwork; conversely, it will prompt to speed up the construction progress to ensure that the project is completed on time.

[0031] In some embodiments, the above-mentioned analysis of the construction behavior of target construction workers using an AI system based on construction behavior images and electricity consumption data to obtain on-site construction behavior analysis results may specifically include the following steps: Based on the data of each frame in the construction behavior image, the human body tracking algorithm is used to track individual target construction workers across frames, so that the same target construction worker can be associated between different frames in the construction behavior image; OpenPose is used to identify the key movement parts of the associated target construction workers. Based on the positional changes of the key movement parts between consecutive frames, the displacement distance, displacement direction and displacement time interval of the key movement parts are analyzed to obtain the movement angle data, movement amplitude data and movement speed data of the key movement parts. Based on motion angle data, motion amplitude data, and motion speed data, analyze whether the construction behavior of the target construction personnel conforms to the specified construction behavior specifications; in response to the construction behavior not conforming to the specified construction behavior specifications, control the target terminal and the central control server to issue a construction hazard behavior alarm signal.

[0032] This method utilizes human body tracking algorithms to track construction workers across frames and employs technologies such as OpenPose to identify and analyze positional changes in key movement areas. It can accurately capture the details of construction workers' movements while performing tasks, including data such as displacement distance, direction, time interval, angle, amplitude, and speed.

[0033] Furthermore, based on the detailed motion data mentioned above, such as Figure 2As shown, the system can deeply analyze whether every action of the construction workers complies with the preset safety construction specifications. This not only helps to discover small errors or non-standard operations that may be overlooked, but also effectively identifies high-risk behaviors, providing a valid basis for accident prevention.

[0034] In this embodiment, when a target construction worker enters the construction site and is identified by an NFC device located there, the system automatically powers on the pre-set construction equipment required by the worker. This ensures that the equipment is used only when needed, and improves work efficiency and safety through this automated management. When the target terminal carried by the worker establishes a connection with the gimbal device located at the NFC device's location, the gimbal device begins to use feature point tracking algorithms and motion detection algorithms to follow the worker's position in real time, thereby achieving accurate tracking of the worker's location and providing accurate spatial positioning data for subsequent behavior analysis. During the tracking process, the gimbal device collects images of the worker's construction behavior. These images contain detailed information about how the worker operates the equipment and performs tasks, serving as the basic data source for analyzing their construction behavior. Simultaneously, the system also records all data of the target construction workers. The electricity consumption data of the construction equipment used throughout the construction process not only reflects the equipment's usage but also indirectly reflects the work efficiency and habits of the construction workers. Construction behavior images and electricity consumption data are sent to the central control server, where they undergo in-depth analysis by an AI system. This AI system can process large amounts of images and electricity consumption data, identify patterns and anomalies, and thus provide analytical data on construction behavior. Combined with high-precision location tracking data, detailed construction behavior images, and electricity consumption data reflecting work patterns, the AI ​​system can more accurately analyze the behavior of construction workers, significantly improving the accuracy of on-site construction behavior analysis. Therefore, by collecting detailed data from multiple perspectives and utilizing AI analysis technology, the accuracy of analyzing on-site construction worker behavior is effectively improved. This method not only helps improve construction quality and efficiency but also enhances on-site safety management.

[0035] In some embodiments, a speaker and a sound sensor are also provided at the location of the NFC device; after the NFC device located at the construction site identifies the target terminal corresponding to the target construction worker and sends a power-on command for the target construction equipment to the power control equipment at the construction site, the method may further include the following steps: In response to the sound sensor detecting that the volume of the construction sound is greater than the preset volume, a negative sound wave with the opposite direction and the same wave value to the positive sound wave corresponding to the construction sound is determined; the target sound corresponding to the negative sound wave is determined, and the speaker is controlled to play the target sound, so as to achieve active noise reduction by canceling out the construction sound.

[0036] On construction sites, the operation of construction equipment and the handling of building materials generate a significant amount of noise. This noise not only affects the work efficiency of construction workers but can also cause long-term damage to their hearing. By detecting construction sounds through sound sensors and generating inverse negative sound waves with equal wave values, and then playing these negative sound waves (i.e., the target sound) through loudspeakers, the actual construction sounds can be effectively canceled out, thereby reducing noise.

[0037] In some embodiments, after analyzing the efficiency of the construction behavior of the target construction personnel using an AI system based on the construction action speed, the amount of handling operations, the construction time of the target construction personnel, electricity consumption data, and the current construction progress corresponding to the construction process, the method may further include the following steps: The system obtains the target construction behavior efficiency and the designated total working time for each of the multiple target construction workers. Based on the various construction projects, target construction behavior efficiencies, and designated total working times at the construction site, the system uses an AI system to dynamically allocate the scheduling data among the multiple target construction workers. This allows for the assignment of target construction workers with target construction behavior efficiencies greater than the designated construction efficiency of the project to each construction project, thus obtaining the scheduling results for the multiple target construction workers.

[0038] In this embodiment, based on the specific needs of each construction project and combined with the individual target construction efficiency of each construction worker (i.e., the efficiency of an individual when performing a specific construction task), the AI ​​system can accurately assign the most suitable construction workers to each project. This not only ensures that each construction project can be completed by efficient personnel skilled in the task, but also maximizes the professional skills of each construction worker.

[0039] By rationally allocating construction workers with high efficiency in their target construction activities to relevant construction projects, the completion speed and quality of these projects can be significantly improved. This data- and algorithm-based intelligent scheduling method overcomes the inaccuracies and inefficiencies that may exist in traditional manual scheduling, and helps to achieve more efficient resource utilization.

[0040] Figure 3 A schematic diagram of an artificial intelligence-based construction behavior data processing device is provided. This device can be applied to on-site construction behavior monitoring systems. Figure 3 As shown, the artificial intelligence-based construction behavior data processing device 300 includes: The sending module 301 is used to respond to the NFC device set at the construction site recognizing the target terminal corresponding to the target construction worker, and to send a power-on command for the target construction equipment to the power supply control device at the construction site; wherein, the target construction equipment is a preset construction equipment required by the target construction worker in the construction process; and a gimbal device is set at the location of the NFC device. Control module 302 is used to respond to the gimbal device being connected to the target terminal, and to use the gimbal device to control the target terminal to follow the real-time position of the target construction worker and collect the construction behavior images corresponding to the target construction worker through feature point tracking algorithm and motion detection algorithm; The analysis module 303 is used to send the construction behavior image and the power consumption data of all the target construction equipment used by the target construction personnel during the construction process to the central control server. The central control server uses the AI ​​system to analyze the construction behavior of the target construction personnel based on the construction behavior image and the power consumption data to obtain the construction behavior analysis results at the construction site.

[0041] The artificial intelligence-based construction behavior data processing device provided in this application embodiment has the same technical features as the artificial intelligence-based construction behavior data processing method provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0042] An electronic device provided in this application embodiment, such as Figure 4 As shown, the electronic device 400 includes a processor 402 and a memory 401. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method provided in the above embodiments.

[0043] See Figure 4 The electronic device also includes a bus 403 and a communication interface 404. The processor 402, the communication interface 404 and the memory 401 are connected via the bus 403. The processor 402 is used to execute executable modules, such as computer programs, stored in the memory 401.

[0044] The memory 401 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 404 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0045] Bus 403 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0046] The memory 401 is used to store programs. After receiving an execution instruction, the processor 402 executes the program. The method executed by the apparatus defined by the process disclosed in any of the preceding embodiments of this application can be applied to the processor 402 or implemented by the processor 402.

[0047] Processor 402 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 402 or by instructions in software form. The processor 402 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 401, and processor 402 reads the information from memory 401 and, in conjunction with its hardware, completes the steps of the above method.

[0048] Corresponding to the above-described artificial intelligence-based construction behavior data processing method, this application embodiment also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are invoked and executed by a processor, the computer-executable instructions cause the processor to perform the steps of the above-described artificial intelligence-based construction behavior data processing method.

[0049] The artificial intelligence-based construction behavior data processing device provided in this application embodiment can be specific hardware on the device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in this application embodiment are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0050] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0051] For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0052] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0053] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0054] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the artificial intelligence-based construction behavior data processing method described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0055] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, when an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0056] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for processing construction behavior data based on artificial intelligence, characterized in that, A construction behavior monitoring system applied to construction sites, the method comprising: In response to the NFC device located at the construction site recognizing the target terminal corresponding to the target construction worker, a power supply command is sent to the power control equipment at the construction site to activate the power supply for the target construction equipment; wherein, the target construction equipment is a preset construction equipment required for the construction process of the target construction worker; and a gimbal device is correspondingly installed at the location of the NFC device; In response to the gimbal device being connected to the target terminal, the gimbal device is used to control the target terminal to follow the real-time location of the target construction worker and collect images of the construction behavior corresponding to the target construction worker through feature point tracking algorithm and motion detection algorithm; The construction behavior images and the power consumption data of all the target construction equipment used by the target construction personnel during the construction process are sent to the central control server. The central control server then uses an artificial intelligence (AI) system to analyze the construction behavior of the target construction personnel based on the construction behavior images and power consumption data to obtain the on-site construction behavior analysis results.

2. The method according to claim 1, characterized in that, The NFC device is also equipped with a speaker and a sound sensor at its location. After the NFC device located at the construction site identifies the target terminal corresponding to the target construction worker and sends a power-on command for the target construction equipment to the power control device at the construction site, the method further includes: In response to the sound sensor detecting that the volume of the construction sound is greater than a preset volume, a negative sound wave with the opposite direction and the same wave value as the positive sound wave corresponding to the construction sound is determined based on the construction sound. The target sound corresponding to the negative sound wave is determined, and the speaker is controlled to play the target sound, so as to achieve active noise reduction by canceling out the construction sound.

3. The method according to claim 1, characterized in that, The target construction worker's work shoes at the construction site were equipped with pressure sensors on the bottom; The step involves using an artificial intelligence (AI) system to analyze the construction behavior of the target construction workers based on the construction behavior images and electricity consumption data, resulting in on-site construction behavior analysis results, including: Based on each frame of data in the construction behavior image, the speed of the target construction worker's construction action is identified using a Kalman filter algorithm and a posture estimation model. The amount of handling work performed by the target construction worker during the construction process is determined based on the pressure data collected by the pressure sensor and the weight of the target construction worker. Based on the construction action speed, the amount of material handling, the construction time of the target construction personnel, the electricity consumption data, and the current construction progress corresponding to the construction process, the artificial intelligence (AI) system is used to analyze the construction behavior efficiency of the target construction personnel, and the construction behavior efficiency is determined as at least part of the on-site construction behavior analysis results.

4. The method according to claim 3, characterized in that, After analyzing the efficiency of the construction behavior of the target construction personnel using an artificial intelligence (AI) system based on the construction action speed, the amount of material handling, the construction time of the target construction personnel, the electricity consumption data, and the current construction progress corresponding to the construction process, the method further includes: Obtain the target construction behavior efficiency of each of the multiple target construction workers and the specified total working time of each of the multiple target construction workers; Based on the various construction projects at the construction site, the efficiency of multiple target construction behaviors, and the specified total working hours, the AI ​​system dynamically allocates the scheduling data among multiple target construction personnel. For each construction project, target construction personnel with a target construction behavior efficiency greater than the specified construction efficiency of the project are assigned to obtain the scheduling results of multiple target construction personnel.

5. The method according to claim 1, characterized in that, The step involves using an artificial intelligence (AI) system to analyze the construction behavior of the target construction workers based on the construction behavior images and electricity consumption data, resulting in on-site construction behavior analysis results, including: Based on the electricity consumption data, the peak electricity consumption, peak time, low electricity consumption, low time, total electricity consumption, and electricity consumption change status of the target construction equipment during the construction process are analyzed. Based on the data of each frame in the construction behavior image, the amplitude and speed data of the target construction worker's construction movements are identified by the Kalman filter algorithm and the attitude estimation model; Based on the peak electricity consumption, peak time, low electricity consumption, low time, total electricity consumption, electricity consumption change status, and construction action amplitude and speed data, the artificial intelligence (AI) system analyzes the total construction workload corresponding to the construction behavior of the target construction personnel, and determines the total construction workload as at least part of the on-site construction behavior analysis results.

6. The method according to claim 5, characterized in that, Also includes: In response to the total workload exceeding the preset workload, the target terminal is controlled to issue a fatigue work warning tone; or, In response to the total amount of work being less than or equal to the preset amount of work, the target terminal is controlled to emit a prompt sound to accelerate the construction progress.

7. The method according to claim 1, characterized in that, The step involves using an artificial intelligence (AI) system to analyze the construction behavior of the target construction workers based on the construction behavior images and electricity consumption data, resulting in on-site construction behavior analysis results, including: Based on the data of each frame in the construction behavior image, the target construction worker is tracked across frames using a human body tracking algorithm, so that the same target construction worker can be associated between different frames in the construction behavior image; OpenPose is used to identify the key movement parts of the associated target construction workers, and based on the positional changes of the key movement parts between consecutive frames, the displacement distance, displacement direction and displacement time interval of the key movement parts are analyzed to obtain the movement angle data, movement amplitude data and movement speed data of the key movement parts. Based on the action angle data, action amplitude data, and action speed data, analyze whether the construction behavior of the target construction worker conforms to the specified construction action specifications. In response to a construction action that does not conform to the specified specifications, the target terminal and the central control server are controlled to issue a construction hazard warning signal.

8. A construction behavior data processing device based on artificial intelligence, characterized in that, Construction behavior monitoring systems applied to construction sites include: The sending module is used to respond to an NFC device located at the construction site recognizing a target terminal corresponding to a target construction worker, and to send a power-on command to the power control device at the construction site for the target construction equipment; wherein, the target construction equipment is a preset construction equipment required by the target construction worker for the construction process; and a gimbal device is correspondingly installed at the location of the NFC device; The control module is used to respond to the gimbal device being connected to the target terminal, and to use the gimbal device to control the target terminal to follow the real-time position of the target construction worker and collect images of the construction behavior corresponding to the target construction worker through feature point tracking algorithm and motion detection algorithm; The analysis module is used to send the construction behavior images and the power consumption data of all the target construction equipment used by the target construction personnel during the construction process to the central control server. The central control server then uses an artificial intelligence (AI) system to analyze the construction behavior of the target construction personnel based on the construction behavior images and power consumption data to obtain the construction behavior analysis results at the construction site.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.

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