Metal roof assembly coordination system
Patent Information
- Application Number
- CN202610985615.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-03
- Publication Date
- 2026-10-27
Smart Images
Figure CN122988488A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of roof assembly technology, and in particular to a metal roof assembly coordination system. Background Technology
[0002] Metal roofs are widely used in stadiums, airport terminals, industrial plants, and large public buildings. With the promotion of prefabricated construction technology, metal roof construction is gradually shifting from traditional on-site assembly to a modular construction model of "module division, factory prefabrication, and on-site overall installation." This model divides the steel structure roof into several modules, each module is further subdivided into unit components, and the unit components are pre-assembled in the factory before being transported to the site for module hoisting and connection. This model has significant advantages such as high construction efficiency, less high-altitude work, and controllable quality.
[0003] However, the modular installation, high-altitude welding, and sealant application processes for metal roofs are highly sensitive to meteorological conditions such as wind speed, precipitation, temperature, and humidity. For example, modular installation typically requires wind speeds not exceeding 8 m / s; otherwise, components are prone to swaying, collisions, or falling. On-site welding requires relative humidity not exceeding 85%; otherwise, welds are prone to defects such as porosity and cracks. Sealant application requires ambient temperatures between 5°C and 35°C and a dry substrate; otherwise, adhesion durability will be affected. Furthermore, sudden strong winds, rain showers, and intense sunlight can exacerbate the thermal expansion and contraction deformation of installed components, further increasing assembly errors.
[0004] Currently, the construction schedule is usually formulated based on long-term climate statistics before construction begins. On-site execution mainly relies on the personal experience of construction management personnel to make temporary judgments on whether to stop work or adjust the work process. There is a lack of dynamic schedule rescheduling based on weather forecasts, which leads to frequent work stoppages and idle time due to weather reasons, or forced operation under unsuitable weather conditions, resulting in safety hazards and quality risks. Therefore, this application proposes a new technical solution. Summary of the Invention
[0005] To improve the dynamic adaptability of construction plans to weather conditions, this application provides a metal roof assembly coordination system.
[0006] This application provides a metal roof assembly coordination system, which adopts the following technical solution: A metal roof assembly coordination system, comprising: The meteorological data acquisition unit is used to collect hourly meteorological forecast data for the roof construction area within a future preset time period. The meteorological forecast data includes at least wind speed, precipitation probability, relative humidity, and ambient temperature. The control center is electrically connected to both the meteorological data acquisition unit and the pre-set terminal on the ground. The control center is configured as follows: Establish a mapping database between assembly processes and weather sensitivity, as well as a personnel requirement benchmark for each process; the mapping database marks at least one weather sensitivity parameter and its corresponding prohibited operation threshold range for each assembly process, and the personnel requirement benchmark includes the standard number of personnel required for each process and the skill level requirements for each job. Obtain hourly weather forecast data for the roof construction area within a preset future time period; Obtain the current construction progress and determine the remaining workload for each process; By comparing meteorological forecast data with the mapping database, the assembly processes affected by meteorological conditions in the future preset time period and the affected time periods are determined. Combined with the remaining workload, the original planned schedule uploaded in advance is reallocated to generate a dynamic coordination plan. The dynamic coordination plan includes at least the work periods for each process and the corresponding personnel demand benchmarks within a preset future time period. Based on the dynamic coordination plan, coordination instructions are generated and sent to the corresponding terminals.
[0007] Optionally, it also includes an image acquisition unit for acquiring image data of the roof construction area, and the control center is electrically connected to the image acquisition unit; the control center performs the function of obtaining the current construction progress, which includes: Acquire image data of the current roof construction area fed back by the image acquisition unit; By comparing the image data with the pre-uploaded BIM model, the remaining workload for each process can be obtained.
[0008] Optionally, the control center is also configured as follows: Based on image data, the location and posture of each person in the image are identified and compared with the preset process area boundary map to count the actual number of workers in each process area. The personnel requirements for the corresponding work processes are adjusted and updated based on the actual number of workers.
[0009] Optionally, the step of adjusting and updating the personnel type requirement benchmark for the corresponding work process based on the actual number of workers includes: When the actual number of workers in a certain process exceeds the standard number of workers for that process, and the deviation exceeds the preset threshold and the duration exceeds the preset duration, then the adjustment process one will be executed. When the actual number of workers in a certain process is lower than the standard number of workers in that process, and the deviation exceeds the preset threshold and the duration exceeds the preset duration, then the adjustment process two will be executed. The adjustment process one includes: If the current construction progress is faster than the original planned progress, it is determined that the baseline model underestimates the progress, and the standard number of workers for that process is automatically increased. If the current construction progress is slower than the original plan, it is determined that the working conditions are abnormal and a yellow warning is issued to the terminal.
[0010] Optionally, the second adjustment process includes: If the current construction progress is faster than the original plan, it is determined that the baseline model has overestimated the progress, and the standard number of workers for this process will be automatically reduced. If the current construction progress is slower than the original plan, it is determined to be due to insufficient manpower, and an orange warning is issued to the terminal.
[0011] Optionally, the image acquisition unit includes a drone, a camera module, a wireless communication module, and a controller. The camera module and the wireless communication module are mounted on the drone. The controller is electrically connected to the camera module and the wireless communication module respectively. The wireless communication module establishes a data connection with the control center to upload the acquired data to the control center and receive instructions issued by the control center. The controller is configured as follows: When the drone receives an aerial photography command from the control center, it will automatically cruise along the preset route. During the cruise, the camera module will be triggered to collect data and transmit the collected images of the roof construction area back to the drone.
[0012] Optionally, the drone is equipped with a microphone module, which is electrically connected to the controller, and the control center is further configured as follows: When the adjustment process determines that the operating conditions are abnormal, a field detection command is sent to the controller. Acquire live audio from the microphone module; Acquire on-site images from the camera module; Speech recognition and natural language processing are performed based on on-site audio to extract keywords; Action behavior is obtained by performing pose recognition based on on-site images; Based on keywords and actions, the system searches a pre-defined database of task difficulty types, obtains a matching task difficulty type, and sends it to the terminal. The controller is configured as follows: When the drone receives an on-site detection command from the control center, it flies to the target process area, hovers at a preset height, and triggers the camera and microphone modules to collect data, and transmits the collected on-site images and audio back.
[0013] Optionally, the control center is also configured as follows: When the difficulty type of the operation is material shortage, a material delivery instruction is sent to the controller; The controller is configured as follows: When a material delivery instruction is received from the control center, the drone flies to the preset material station to load the corresponding material package, and then flies to the target process area to deliver it.
[0014] In summary, this application includes the following beneficial technical effects: based on real-time acquired meteorological data and current progress, the original planned progress is automatically reallocated, generating an executable dynamic coordination plan and sending it to relevant terminals. This solves the problem that traditional static plans cannot cope with weather changes. Moreover, the dynamic coordination plan not only includes adjustments to the sequence of work processes, but also synchronously generates personnel demand benchmarks for each time period. This makes personnel allocation no longer dependent on experience-based judgment, but rather linked in real time with weather forecasts and changes in work processes, thereby improving the efficiency of human resource utilization. Attached Figure Description
[0015] Figure 1 This is a schematic diagram of the system architecture of an embodiment of this application.
[0016] Figure 2 This is a flowchart of the dynamic coordination method between meteorology and construction period in an embodiment of this application. Detailed Implementation
[0017] The following is in conjunction with the appendix Figure 1-2 This application will be described in further detail.
[0018] This application discloses a metal roof assembly coordination system.
[0019] Reference Figure 1 The metal roof assembly coordination system includes a meteorological data acquisition unit, a control center, and ground terminals.
[0020] The control center is electrically connected to the meteorological data acquisition unit and the ground terminal, respectively, and is used to receive data, execute decision-making algorithms, and issue coordination instructions.
[0021] The meteorological data acquisition unit is used to acquire hourly meteorological forecast data for the roof construction area over a predetermined time period (e.g., the next 24, 48, or 72 hours) in real time or at set intervals. The meteorological forecast data includes at least wind speed, precipitation probability, relative humidity, and ambient temperature. In this embodiment, the meteorological data acquisition unit can be a computer or embedded device with network communication capabilities, connecting to a public meteorological service platform (such as China Weather Network or a commercial meteorological service provider) via an API interface, or connecting to a weather station independently set up at the construction site. The meteorological data acquisition unit packages the collected meteorological data and sends it to the control center.
[0022] The control center consists of one or more servers, industrial computers or cloud servers, and integrates a database, algorithm engine and communication module.
[0023] Ground terminals include, but are not limited to: mobile terminals (phones, tablets) for construction management personnel, on-site electronic signboards, tower crane operator cab displays, and walkie-talkies worn by workers. Coordination instructions generated by the control center are pushed to the above terminals via wired or wireless networks (such as Wi-Fi, 4G / 5G, LoRa).
[0024] Reference Figure 2 Based on the above, the control center is configured as follows: S110. Establish a mapping database between assembly processes and weather sensitivity, as well as a baseline for the personnel requirements of each process. In the mapping database, each assembly process is marked with at least one meteorological sensitive parameter and its corresponding prohibited operation threshold range, for example: Group 1: Process name: Module hoisting and positioning; Meteorological sensitive parameter: wind speed; Prohibited operation threshold range: ≥8 m / s; Group 2: Process name: On-site welding; Meteorological sensitive parameter: Precipitation probability; Prohibited work threshold range: >10%; Group N... and so on.
[0025] The personnel requirements include the standard number of workers required for each process (e.g., "module hoisting and positioning" requires 2 crane operators, 2 hoisting workers, and 1 signalman, for a total of 5 people) as well as the skill level requirements for each job (e.g., senior welder, intermediate welder, etc.).
[0026] S210. Obtain hourly weather forecast data for the roof construction area within a future preset time period; It is understandable that the meteorological data acquisition unit acquires hourly weather forecast data for the next 72 hours at a frequency of, for example, once every hour, and stores the data in a real-time meteorological database.
[0027] S310. Obtain the current construction progress and determine the remaining workload for each process. In this embodiment, the construction progress is obtained through an image acquisition unit (see below for details). Alternatively, on-site management personnel can manually fill in the number of completed modules or welding nodes via a mobile terminal, and the control center can automatically calculate the remaining workload based on the pre-entered total workload.
[0028] The remaining workload can be represented by various quantitative indicators, such as the number of remaining modules, the remaining welding length (meters), and the number of remaining nodes. The control center combines the remaining workload with standard work efficiency (such as 0.5 hours for hoisting each module) to estimate the remaining operation time for each process.
[0029] S410. Compare the weather forecast data with the mapping database time-by-time to determine the assembly processes affected by weather conditions and the affected time periods within a preset future time period. For example, if the forecast wind speed is 9 m / s in the 3rd hour, the "module hoisting and positioning" process is marked as "prohibited" during that time period. Based on the remaining workload, the pre-uploaded original schedule (which can be a pre-uploaded process sequence table) is reallocated to generate a dynamic coordination plan. The dynamic coordination plan includes at least the work periods for each process within a preset future time period (e.g., the next 24 hours) and the corresponding personnel requirement baseline (job type and number of people) for each time period. The dynamic coordination plan can be stored in the form of a time sequence table. Understandably, the principles of reallocation include: removing affected processes from prohibited operation windows and assigning them to subsequent operable windows; inserting processes that are not affected by weather and can be implemented in advance (such as ground assembly and component repair) into the original affected windows; if processes on the critical path are affected by weather and cannot be resolved by moving windows, the algorithm will suggest that additional resources or process adjustments are needed.
[0030] S510. Generate coordination instructions according to the dynamic coordination plan and send them to the corresponding terminals.
[0031] It is understandable that coordination instructions can be divided into two categories: Schedule coordination instructions: For example, "Today from 14:00 to 16:00, module hoisting will be suspended and replaced with ground assembly; hoisting will resume from 9:00 to 11:00 tomorrow."
[0032] Personnel allocation instructions: For example, "The hoisting process at 9:00 tomorrow requires 2 crane operators and 2 hoisting workers. Please ensure that the relevant work teams arrive on time."
[0033] These instructions are sent to the corresponding ground terminals via wireless network. For example, schedule coordination instructions are pushed to the on-site electronic dashboard and the mobile phones of managers; personnel allocation instructions are pushed to the walkie-talkies of the labor team leaders.
[0034] With the above settings, based on real-time meteorological data and current progress, the original planned progress is automatically redistributed, generating an executable dynamic coordination plan and sending it to relevant terminals. This solves the problem that traditional static plans cannot cope with weather changes. Moreover, the dynamic coordination plan not only includes adjustments to the work sequence, but also synchronously generates the personnel demand benchmark (job type and number of people) for each time period. This makes personnel allocation no longer dependent on experience judgment, but is linked in real time with weather forecasts and work process changes, improving the efficiency of human resource utilization.
[0035] In another embodiment of this application, to automate the acquisition of construction progress, the system further includes an image acquisition unit. The image acquisition unit can be one or more fixed cameras (e.g., a PTZ camera mounted on top of a tower crane), or one or more drones. This embodiment uses a drone as an example for illustration.
[0036] The image acquisition unit includes the UAV itself, a camera module (such as a high-definition visible light camera or a thermal imaging camera), a wireless communication module (such as a 4G / 5G module or a point-to-point data radio), and a controller. The camera and wireless communication modules are mounted on the UAV, and the controller (i.e., the UAV's flight controller, such as a Pixhawk or an embedded flight control board) is electrically connected to both the camera and wireless communication modules. The wireless communication module establishes a two-way data link with the control center to upload acquired images and videos and to receive flight path or mission instructions from the control center.
[0037] Drones can also be equipped with RTK (real-time dynamic positioning) modules to improve positioning accuracy, as well as obstacle avoidance sensors (ultrasonic, visual obstacle avoidance, etc.) to ensure flight safety.
[0038] Based on the above, the controller is configured as follows: When the drone receives an aerial photography command from the control center, it will automatically cruise along the preset route. During the cruise, the camera module will be triggered to collect data and transmit the collected images of the roof construction area back to the drone.
[0039] Understandably, the control center issues aerial photography commands to the drone based on construction needs. These commands can be a pre-set flight path file (e.g., waypoint coordinates, flight altitude, gimbal pitch angle). Upon receiving the commands, the drone's controller automatically unlocks, takes off, and cruises along the pre-set flight path. During the cruise, the controller triggers the camera module at set time intervals or waypoints to capture images (photos or video streams) of the roof construction area. The captured images are then transmitted back to the control center in real time via the wireless communication module or in batches after landing.
[0040] Based on the above, after receiving the image data, the control center performs the following steps: 1) Use 3D reconstruction algorithms (such as oblique photography and motion-reconstructed structures) to generate a realistic 3D model of the construction area from multiple images; 2) Compare the real-world model with the pre-uploaded BIM design model, and automatically identify the number of installed modules, the number of completed welding nodes, and the area of installed purlins through Boolean operations or image semantic segmentation. 3) Calculate the remaining workload for each process based on the total workload preset in the BIM model (e.g., the number of remaining un-hoisted modules and the remaining un-welded length).
[0041] In another embodiment of this application, considering that the actual number of workers may deviate from the standard number in the pre-stored personnel baseline, in order to achieve self-calibration of the personnel baseline and prevent waste or shortage of manpower, the control center is also configured as follows: 1) Based on image data, identify the position and posture of each person in the image, compare it with the preset process area boundary map, and count the actual number of workers in each process area; Understandably, the work area boundaries (electronic fences) for each process are pre-defined on the BIM model or 2D plan, for example, the "Module A hoisting area" is defined by coordinates X1-X2, Y1-Y2. For images captured by drones or fixed cameras, the control center runs a deep learning-based personnel detection model (such as OpenPose or HRNet) to identify the position and posture of personnel in the images. The pixel coordinates are converted to site coordinates using camera calibration parameters, and then compared spatially with the electronic fence to count the actual number of people falling within each process area.
[0042] To differentiate job types, workers can be identified by the color of their safety helmets (red = welder, yellow = crane operator, blue = assembly worker) or by the ID tags on their smart safety helmets. Using UWB positioning tags would allow for more precise acquisition of each worker's real-time coordinates and identity, which could then be compared with electronic fences to determine the actual number of workers present at each work process.
[0043] 2) Adjust and update the personnel requirements benchmark for the corresponding work process based on the actual number of workers.
[0044] Understandably, the control center compares the actual number of workers in each process with the pre-stored standard number of workers for that process. To avoid misjudgments (e.g., sudden changes in numbers due to brief personnel passing by), the system introduces a number deviation threshold (e.g., deviation exceeding ±20%) and a duration threshold (e.g., number deviation exceeding the threshold continuously for more than 15 minutes) during the comparison. The adjustment process is triggered only when both conditions are met simultaneously, as specifically set below: When the actual number of workers in a certain process exceeds the standard number of workers for that process, and the deviation exceeds the preset threshold and the duration exceeds the preset duration, then the adjustment process one will be executed. The adjustment process includes: If the current construction progress is faster than the original plan (for example, 12 modules have actually been completed instead of the original plan of 10), it is determined that the baseline model underestimates the required number of workers for that process, meaning that the actual number of workers needed is too low, but the efficiency is higher than expected. The system automatically adjusts the standard number of workers for that process, and the adjustment amount can be determined based on the ratio of the actual number of workers to the standard number of workers (for example, new standard number of workers = actual number of workers × 0.9). The adjusted standard number of workers is stored and used for subsequent personnel requirement calculations.
[0045] If the current construction progress is slower than the original planned progress, it is judged as an abnormal working condition (which may include abnormal component dimensions, incompatible tools, etc.). At this time, the control center does not automatically adjust the standard number of personnel, but issues a yellow warning to the terminal, reminding the on-site management personnel to go to the area to check the cause. The yellow warning includes: work process name, location, comparison of actual number of personnel with standard number of personnel, progress deviation, and suggested inspection direction (such as "Please confirm whether there is any installation delay").
[0046] When the actual number of workers in a certain process is lower than the standard number of workers in that process, and the deviation exceeds the preset threshold and the duration exceeds the preset duration, then the adjustment process two will be executed. The second adjustment process includes: If the current construction progress is faster than the original planned progress, it is determined that the baseline model has overestimated, meaning that the standard number of workers for this process is set too high, and the actual efficiency is higher than expected. The system will automatically reduce the standard number of workers for this process, and the reduction amount can be determined similarly based on the ratio of the actual number of workers to the standard number of workers.
[0047] If the current construction progress is slower than the original planned progress, it is determined to be due to insufficient manpower. The control center issues an orange alert to the terminal and automatically attempts to allocate personnel from other non-critical processes or areas with idle personnel to supplement the work. The allocation logic is as follows: The control center scans the deviation between the actual number of workers and the standard number of workers in all processes to identify processes with redundant personnel (the actual number of workers is significantly higher than the standard number of workers and the progress is normal or faster). Personnel with the required skills can be redeployed from redundant processes (e.g., one assembler can be transferred from the ground assembly area to the module hoisting area where there is a shortage of personnel to provide assistance). Generate specific personnel deployment instructions ("Please have Zhang San and Li Si go to the hoisting area of Module E to provide support within 10 minutes") and push them to relevant personnel through the terminal; If there are no available spare personnel, the system will generate a supplementary labor application form, which includes the type of work, number of people, and expected arrival time, and send it to the project management department.
[0048] Through the above settings, the actual number of workers is automatically counted and compared with the standard number of workers, enabling dynamic adjustment of the personnel benchmark based on the actual number of workers, thus making personnel allocation more reasonable.
[0049] In another embodiment of this application, when an abnormal operation condition is triggered in the adjustment process one, in order to obtain the actual situation on site in a timely manner so as to respond and resolve the issue promptly, the drone is equipped with a microphone module. The microphone module is electrically connected to the controller, and the control center can automatically schedule the drone to conduct on-site detection. The control center issues an "on-site detection command" to the drone's controller. This command includes at least the coordinates (latitude and longitude or construction site coordinate system) of the target process area and the task to be performed (such as "voice acquisition + image acquisition").
[0050] When the drone's controller receives an on-site detection command from the control center, it commands the drone to fly to the corresponding target process area, hover at a preset height (usually 10-15 meters away from the work surface to ensure safety and collect effective voice and images), and triggers the camera module to collect on-site images and the microphone module to collect on-site audio.
[0051] To capture clear worker conversations in high-noise environments (such as rotor noise and construction machinery noise), the microphone module employs a directional microphone array, combined with beamforming algorithms to suppress background noise. Simultaneously, the drone can reduce its rotor speed to decrease its own noise, or adopt low-noise designs such as electric ducted fans.
[0052] Based on the above, the control center is configured to transmit the acquired on-site images and audio back to the control center in real time via a wireless communication module: Acquire live audio from the microphone module; Acquire on-site images from the camera module; Based on on-site audio, speech recognition and natural language processing are performed to extract keywords. For example, the audio stream is subjected to voice activity detection (VAD) to extract segments containing human voices, which are then fed into an automatic speech recognition (ASR) engine to convert them into text. Keywords are then extracted using a natural language processing (NLP) model. The keyword library is pre-built based on common operational difficulties, such as: "cannot be installed," "misaligned," "stuck," "incorrect drawing," "two centimeters off," "out of welding rods," "waiting for the crane," "stripped bolt," etc.
[0053] Based on on-site images, posture recognition is performed to obtain action behaviors. For example, a posture recognition model (such as OpenPose) is run on video streams or images to detect workers' action behaviors, such as: repeatedly lifting components to try to insert them, standing still for a long time, multiple people whispering to each other, and repeatedly changing hand gestures. Based on keywords and actions, the system searches a pre-defined database of task difficulty types, obtains a matching task difficulty type, and sends it to the terminal. Understandably, the control center uses the extracted keywords and identified actions as input to search a pre-defined database of task difficulty types. This database is a rule-based or machine learning classifier that defines multiple sets of mapping relationships, for example: Group 1: Keywords: "cannot be installed", "cannot be aligned"; Action / behavior: Repeatedly adjusting the position of components; Type of operational difficulty: Installation interference; Group 2: Keywords: "No welding rods", "Missing bolts"; Actions and behaviors: searching around; Type of operational difficulty: Material shortage; Group N... and so on.
[0054] Once a match is found in the database, the control center sends the type of operational difficulty (along with location and timestamp) to the terminal. For example, an "installation interference" alert might be pushed to the mobile phone of the on-site technical supervisor, advising them to arrive at the scene as soon as possible.
[0055] With the above settings, voice recognition and posture recognition are used to intelligently determine the type of difficulty in the operation, shorten the problem response time, and ensure construction quality and safety.
[0056] In another embodiment of this application, when the operational difficulty type matched by the control center is material shortage, in order to deliver materials in a timely manner and improve efficiency, the control center is further configured as follows: When the difficulty type of the operation is material shortage, a material delivery instruction is issued to the controller. This instruction includes the coordinates of the target process area and the type and quantity of the required materials (e.g., "30 sets of M20×80 high-strength bolts are missing").
[0057] The controller is configured as follows: When a material delivery instruction is received from the control center, the drone flies to the preset material station to load the corresponding material package, and then flies to the target process area to deliver it.
[0058] Understandably, the drone's controller first directs the drone to a pre-designated material station. The material station is an automated replenishment platform set up on the ground of the construction site, which stores small material packages (each package contains a pre-defined number of bolts, welding rods, gaskets, etc.) in compartments according to material type.
[0059] After the drone flies over the material station, it automatically lands on the docking platform using onboard vision guidance. Based on the material type and quantity issued by the control center, the material station pushes the corresponding material package to the docking position. The drone's mounting mechanism (such as an electromagnetic chuck or hook) automatically connects to and locks the material package. After mounting is complete, the drone automatically takes off.
[0060] After the drone arrives at the target work area, it hovers at a safe height (usually 3-5 meters above the work surface) and issues a voice prompt via its onboard speaker: "Material is about to be delivered, please take cover." Once the workers have cleared the area, the drone drops the material package to the ground via a rope descent or electromagnetic release. After delivery, the drone automatically returns to its charging platform to await further instructions or perform its next task.
[0061] In some simplified embodiments, material delivery can also be carried out manually, whereby on-site personnel hang the material packages on the drone's hook, and the drone only performs transportation and release. This application does not limit the specific loading and unloading mechanical structure, as long as it can achieve automatic or semi-automatic material conveying.
[0062] By using the above setup, drones are employed for progress tracking, personnel statistics, anomaly detection, and material delivery, thereby improving the informatization and automation level of construction management.
[0063] Those skilled in the art should understand that the specific values (such as height, time threshold, wind speed threshold, etc.) in the above embodiments are merely examples and can be adjusted according to actual engineering requirements.
[0064] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.
Claims
1. A metal roof assembly coordination system, characterized in that, include: The meteorological data acquisition unit is used to collect hourly meteorological forecast data for the roof construction area within a future preset time period. The meteorological forecast data includes at least wind speed, precipitation probability, relative humidity, and ambient temperature. The control center is electrically connected to both the meteorological data acquisition unit and the pre-set terminal on the ground. The control center is configured as follows: Establish a mapping database between assembly processes and weather sensitivity, as well as a personnel requirement benchmark for each process; the mapping database marks at least one weather sensitivity parameter and its corresponding prohibited operation threshold range for each assembly process, and the personnel requirement benchmark includes the standard number of personnel required for each process and the skill level requirements for each job. Obtain hourly weather forecast data for the roof construction area within a preset future time period; Obtain the current construction progress and determine the remaining workload for each process; By comparing meteorological forecast data with the mapping database, the assembly processes affected by meteorological conditions in the future preset time period and the affected time periods are determined. Combined with the remaining workload, the original planned schedule uploaded in advance is reallocated to generate a dynamic coordination plan. The dynamic coordination plan includes at least the work periods for each process and the corresponding personnel demand benchmarks within a preset future time period. Based on the dynamic coordination plan, coordination instructions are generated and sent to the corresponding terminals.
2. The metal roof assembly coordination system according to claim 1, characterized in that: It also includes an image acquisition unit for acquiring image data of the roof construction area, and the control center is electrically connected to the image acquisition unit; The control center performs the function of obtaining the current construction progress, which includes: Acquire image data of the current roof construction area fed back by the image acquisition unit; By comparing the image data with the pre-uploaded BIM model, the remaining workload for each process can be obtained.
3. The metal roof assembly coordination system according to claim 2, characterized in that: The control center is also configured to: Based on image data, the location and posture of each person in the image are identified and compared with the preset process area boundary map to count the actual number of workers in each process area. The personnel requirements for the corresponding work processes are adjusted and updated based on the actual number of workers.
4. The metal roof assembly coordination system according to claim 3, characterized in that: The adjustment and updating of the personnel type requirement benchmark for the corresponding work process based on the actual number of workers includes: When the actual number of workers in a certain process exceeds the standard number of workers for that process, and the deviation exceeds the preset threshold and the duration exceeds the preset duration, then the adjustment process one will be executed. When the actual number of workers in a certain process is lower than the standard number of workers in that process, and the deviation exceeds the preset threshold and the duration exceeds the preset duration, then the adjustment process two will be executed. The adjustment process one includes: If the current construction progress is faster than the original planned progress, it is determined that the baseline model underestimates the progress, and the standard number of workers for that process is automatically increased. If the current construction progress is slower than the original plan, it is determined that the working conditions are abnormal and a yellow warning is issued to the terminal.
5. The metal roof assembly coordination system according to claim 4, characterized in that: The second adjustment process includes: If the current construction progress is faster than the original plan, it is determined that the baseline model has overestimated the progress, and the standard number of workers for this process will be automatically reduced. If the current construction progress is slower than the original plan, it is determined to be due to insufficient manpower, and an orange warning is issued to the terminal.
6. The metal roof assembly coordination system according to claim 4, characterized in that: The image acquisition unit includes a drone, a camera module, a wireless communication module, and a controller. The camera module and the wireless communication module are mounted on the drone. The controller is electrically connected to the camera module and the wireless communication module respectively. The wireless communication module establishes a data connection with the control center to upload the acquired data to the control center and receive instructions issued by the control center. The controller is configured as follows: When the drone receives an aerial photography command from the control center, it will automatically cruise along a preset route. During the cruise, the camera module will be triggered to collect data and transmit the collected images of the roof construction area back to the drone.
7. The metal roof assembly coordination system according to claim 6, characterized in that: The drone is equipped with a microphone module, which is electrically connected to the controller. The control center is also configured to: When the adjustment process determines that the operating conditions are abnormal, a field detection command is sent to the controller. Acquire live audio from the microphone module; Acquire on-site images from the camera module; Speech recognition and natural language processing are performed based on on-site audio to extract keywords; Action behavior is obtained by performing pose recognition based on on-site images; Based on keywords and actions, the system searches a pre-defined database of task difficulty types, obtains a matching task difficulty type, and sends it to the terminal. The controller is configured as follows: When the drone receives an on-site detection command from the control center, it flies to the target process area, hovers at a preset height, and triggers the camera and microphone modules to collect data, and transmits the collected on-site images and audio back.
8. The metal roof assembly coordination system according to claim 7, characterized in that: The control center is also configured to: When the difficulty type of the operation is material shortage, a material delivery instruction is sent to the controller; The controller is configured as follows: When a material delivery instruction is received from the control center, the drone flies to the preset material station to load the corresponding material package, and then flies to the target process area to deliver it.