Intelligent construction site equipment control method and system based on Internet of Things

Abstract

The invention discloses an intelligent construction site equipment control method and system based on the Internet of Things, and relates to the technical field of digital construction sites, and the method comprises the steps: collecting environment data and situation data in real time through a sensing node network, generating a dynamic environment mapping, and determining the situation state of each target region based on the dynamic environment mapping. When an abnormal situation state is detected, the system automatically generates a processing task, the reliability of data transmission and the timeliness of emergency response are ensured through a differential redundancy and dynamic priority redundancy strategy based on task requirements, and in addition, the task processing efficiency is improved by controlling movement of a sensing node and changing the state of the node. According to the method, the intelligent level of construction site management is effectively improved, the dynamic response capability to complex situations is enhanced, and the method is suitable for efficient management of modern complex construction sites.

Description

Technical Field

[0001] The present application relates to the field of digital construction site technology, and in particular to a method and system for controlling smart construction site equipment based on the Internet of Things. Background Art

[0002] As the complexity and scale of modern construction projects increase, construction site management faces more and more challenges. Although some existing construction site management systems based on the Internet of Things can achieve environmental monitoring and equipment control, they still have some shortcomings when dealing with complex and changing construction site environments. For example, existing systems generally lack the ability to analyze the complex correlation between environmental data and contextual data, and it is difficult to effectively coordinate between multiple nodes, resulting in insufficient emergency response capabilities. In addition, the data redundancy strategy is not flexible enough, and the data transmission strategy cannot be dynamically adjusted according to the real-time situation, which may cause resource waste or response delays.

[0003] For example, the Chinese patent with publication number CN117911198A discloses a smart construction site management system, including a personnel management module, an equipment control module, an environmental monitoring module, an on-site supervision module and a management platform; the personnel management module is used to collect workers' personal information and attendance information; the environmental monitoring module is used to collect environmental data and send the environmental data to the management platform; the equipment control module is used to collect the current status of the equipment and send the equipment status information to the management platform; the on-site supervision module is used to identify the personal information and safety information of the construction personnel and send the identification information to the management platform; the management platform is used to display the workers' personal information and attendance information, environmental data, equipment status information, and identification information. If a problem occurs, the abnormality can be seen on the management platform in the first time, thereby improving the safety of the construction.

[0004] The above methods have the problems raised by this background technology. The existing system lacks the ability to analyze the complex correlation between environmental data and situational data, and it is difficult to effectively coordinate between multiple nodes, resulting in insufficient emergency response capabilities. Secondly, the data redundancy strategy is not flexible enough, and it is impossible to dynamically adjust the data transmission strategy according to the real-time situation, which may cause resource waste or response delay. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present application discloses a smart construction site equipment control method and system based on the Internet of Things.

[0006] In the first aspect, the present application provides a smart construction site equipment control method based on the Internet of Things, including:

[0007] Using a sensor node network to collect environmental data and scenario data of a target construction site in real time and generate a dynamic environmental map; wherein the target construction site includes a plurality of target areas, each of which is equipped with at least one sensor node;

[0008] Based on the dynamic environment mapping, determining the situational status of each of the target areas;

[0009] In response to the presence of a target context state in the context states of the target area, generating a processing task using a sensing node equipped with the target area having the target context state;

[0010] Based on the processing task, the target equipment in the target construction site is controlled to process the target situation state.

[0011] As an optional implementation, the dynamic environment mapping includes: static mapping information, dynamic mapping information, and environment mapping information; generating the dynamic environment mapping includes:

[0012] Obtaining static layout information of the target construction site and generating static mapping information;

[0013] Based on the scenario data, determining dynamic change factors and generating dynamic mapping information;

[0014] Based on the environmental data, generate environmental mapping information;

[0015] The mapping frequencies of the dynamic mapping information and the environment mapping information are respectively a preset first mapping frequency and a second mapping frequency.

[0016] As an optional implementation, the generated control instruction includes:

[0017] In response to the existence of a target context state in the context state of the target area, using the sensing node equipped with the target area where the target context state exists, generating current marking information of the target area when the target context state exists;

[0018] The tag information includes: target scenario status, time information, geographic information, equipment operation information in the area, and environmental data;

[0019] The marking information is uploaded to the sensing node network.

[0020] As an optional implementation, in response to the sensor node network detecting that a first number of the sensor nodes have uploaded the same target scenario state, all the sensor nodes are used to make a global decision to generate the control instruction.

[0021] As an optional implementation manner, in response to the sensing node network detecting that a certain sensing node has uploaded the target scenario state, the sensing node that has uploaded the target scenario state and the sensing nodes directly adjacent to the sensing node that has uploaded the target scenario state in the sensing node network are used to make a local decision and generate the control instruction;

[0022] After the sensing node uploads the target scenario state, the method further includes:

[0023] Increasing the mapping frequency of the dynamic mapping information from the first mapping frequency to a third mapping frequency;

[0024] The mapping frequency of the environment mapping information is increased from the second mapping frequency to a fourth mapping frequency.

[0025] As an optional implementation manner, the making a local decision and generating the control instruction includes:

[0026] For the sensing nodes directly adjacent to the sensing node that has uploaded the target scenario state, performing a correlation evaluation of the target scenario state, and generating correlation information of each of the directly adjacent sensing nodes;

[0027] Based on the correlation information, make a local decision and generate the control instruction;

[0028] The correlation information includes: environmental data correlation and scenario data correlation.

[0029] As an optional implementation, it is characterized in that, based on the correlation information, a data redundancy strategy between the sensing node that uploaded the target scenario state and a directly adjacent sensing node is determined; wherein the data redundancy strategy includes:

[0030] When the environmental data and the scenario data of the sensing node and its directly adjacent sensing nodes both show a first correlation, a difference redundancy strategy is adopted during data transmission; wherein the difference redundancy strategy includes: introducing data differences during transmission;

[0031] When the correlation of scenario data is high and the correlation of environmental data is low, a dynamic priority redundancy strategy is adopted in the data transmission process; wherein the dynamic priority redundancy strategy includes: prioritizing the transmission of scenario data and dynamically adjusting the frequency and order of data transmission.

[0032] As an optional implementation, it also includes:

[0033] Based on the correlation information, controlling the positions of the sensing node that uploaded the target situation state and its adjacent sensing nodes;

[0034] The sensing node has a mobile capability and is used to adjust its own physical position based on the correlation information;

[0035] Based on the correlation information, changing the monitoring angle of the sensing node;

[0036] Wherein, the sensing node is provided with an adjustable monitoring device.

[0037] In the second aspect, the present application also provides a smart construction site equipment control system based on the Internet of Things, including:

[0038] A sensing node unit, used to collect environmental data and scenario data of a target construction site in real time using a sensing node network, and generate a dynamic environmental map; wherein the target construction site includes a plurality of target areas, each of which is equipped with at least one sensing node;

[0039] A scenario analysis unit, configured to determine a scenario state of each of the target areas based on the dynamic environment mapping;

[0040] a processing unit, configured to generate a processing task using a sensing node provided in the target area where the target situation state exists, in response to the presence of a target situation state in the situation state of the target area;

[0041] A control unit is used to control the target equipment in the target construction site to process the target scenario state based on the processing task.

[0042] In a third aspect, an optional implementation of the present application further provides a computer device, a processor, and a memory, wherein the memory stores machine-readable instructions executable by the processor, and the processor is used to execute the machine-readable instructions stored in the memory, and when the machine-readable instructions are executed by the processor, the machine-readable instructions perform the steps of the above-mentioned first aspect, or any possible implementation of the first aspect.

[0043] In a fourth aspect, an optional implementation of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the steps of the above-mentioned first aspect, or any possible implementation of the first aspect are executed.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows: by collecting environmental data and scenario data in real time through the sensing node network and generating dynamic environmental mapping, the system can accurately reflect the actual situation of the construction site. Compared with traditional methods, this dynamic mapping can timely identify changes in the environment and equipment, improving the real-time and accuracy of construction site management. Based on dynamic environmental mapping and scenario recognition, when an abnormal situation state is detected, processing tasks can be automatically generated and corresponding response measures can be executed. The introduction of differential redundancy and dynamic priority redundancy strategies makes data transmission more reliable and efficient. By dynamically adjusting the redundancy level and data transmission frequency according to different scenarios, the system effectively reduces unnecessary network burdens while ensuring priority transmission of critical data. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flowchart of a smart construction site equipment control method based on the Internet of Things provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of a smart construction site equipment control system based on the Internet of Things provided in an embodiment of the present application;

[0047] Figure 3 A schematic diagram of the computer device structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0049] See also Figure 1 FIG. 1 is a flowchart of a method for controlling smart construction site equipment based on the Internet of Things provided by an embodiment of the present disclosure, wherein the method includes steps S101 to S104, wherein:

[0050] S101: using a sensor node network to collect environmental data and scenario data of a target construction site in real time, and generate a dynamic environmental map; wherein the target construction site includes a plurality of target areas, each of which is equipped with at least one sensor node;

[0051] S102: Determine the situation status of each target area based on the dynamic environment mapping;

[0052] S103: In response to the existence of a target context state in the context states of the target area, generating a processing task using a sensing node equipped with the target area having the target context state;

[0053] S104: Based on the processing task, control the target equipment in the target construction site to process the target scenario state.

[0054] Regarding S101 above:

[0055] In a specific implementation, multiple sensing nodes can be deployed in multiple target areas of the target construction site. Each target area is equipped with at least one sensing node, which are connected through wireless networks (such as LoRa, Zigbee, Wi-Fi, etc.) to form a complete sensing node network. Each sensing node is responsible for collecting environmental data and scenario data in the area in real time. Each sensing node can transmit the collected data to the central processing unit of the system in real time.

[0056] Among them, perception nodes refer to intelligent devices deployed in the target area, which can collect environmental data (such as temperature, humidity, and dust concentration) and situational data (such as worker location and equipment status) in real time. Perception nodes usually include multimodal sensors, data processing units, wireless communication modules, etc.

[0057] The target construction site refers to the entire management area of ​​the construction site, including multiple different target areas.

[0058] The target area may be a subdivided area of ​​the target construction site, each area having specific functions or characteristics, such as a construction area, an equipment area, a material storage area, etc. Another example is a subdivided area such as a first construction area and a second construction area.

[0059] For example, environmental data includes dust concentration, temperature and humidity, noise level, light intensity, etc.; situational data includes worker location, equipment operating status, material stacking, etc.

[0060] Regarding S102 above:

[0061] In the specific implementation, the central processing unit of the system receives environmental data and scenario data from multiple sensing nodes, and combines it with the static layout information of the target construction site to generate a dynamic environment map. The dynamic environment map will be continuously updated based on the real-time data of the sensing nodes to reflect the latest status of the construction site. The map is a multi-level construction site environment model that contains the following information:

[0062] Static mapping: The fixed layout of the construction site infrastructure, such as the location of buildings, installation locations of fixed equipment, etc.

[0063] Dynamic change mapping: Dynamic change factors within the construction site, such as equipment movement paths, real-time changes in workers’ activity areas, etc.

[0064] Environmental status mapping: current environmental data distribution, such as dust concentration distribution, temperature and humidity change trends, etc.

[0065] Among them, the target scenario state refers to the pre-defined state that needs to be handled, such as equipment failure, high-risk operations, and excessive environmental pollution.

[0066] Regarding S103 above:

[0067] In specific implementation, the system identifies the situational status of each target area by analyzing the dynamic environment mapping. For example, when the dust concentration in a target area exceeds the standard and the equipment is operating abnormally, the system identifies this state as a "high-risk situational state". Subsequently, the system will use the sensing nodes in the area to generate corresponding processing tasks.

[0068] For the above S104: Based on the generated processing tasks, the system will send instructions to the target equipment (such as ventilation systems, cleaning equipment, alarm devices), instructing them to start the corresponding processing process. For example, for a "high-risk scenario state", the system may start the ventilation equipment in the area to reduce the dust concentration, and notify the on-site management personnel to take further safety measures, or start the alarm system to remind workers to stay away from the dangerous area.

[0069] In this way, based on the perception node network, through dynamic environment mapping and scenario recognition, processing tasks are generated and executed, effectively improving the intelligence and safety of construction site management.

[0070] This application collects environmental data and scenario data in real time through a network of sensing nodes, and generates dynamic environmental mapping. Compared with traditional static monitoring systems, it can reflect the dynamic changes of the construction site environment and situation in real time, and provide more accurate construction site management information. Through dynamic environmental mapping, the situational status of the target area is identified, which solves the problem that it is difficult for traditional systems to fully perceive and respond to complex construction site environments in real time. After identifying a specific situational status, processing tasks are automatically generated, and related equipment is controlled to respond. This intelligent processing flow reduces manual intervention and improves the efficiency and safety of construction site management.

[0071] As an optional implementation, the dynamic environment mapping includes: static mapping information, dynamic mapping information, and environment mapping information; generating the dynamic environment mapping includes: obtaining the static layout information of the target construction site and generating static mapping information; based on the scenario data, determining the dynamic change factors and generating dynamic mapping information; based on the environment data, generating environment mapping information; wherein the mapping frequencies of the dynamic mapping information and the environment mapping information are respectively a preset first mapping frequency and a second mapping frequency.

[0072] In specific implementation, static layout information of the construction site can be obtained digitally, which can come from construction site design drawings, building planning documents or high-precision map data obtained through drone scanning. The layout information includes the specific location of buildings, installation points of fixed equipment, distribution of roads and passages, etc.

[0073] This layout information is entered into a static map. This data is combined into a static 3D site model that shows the basic structure and fixtures of the site. This map can be updated infrequently and is only regenerated when structural changes are made to the site. For example, if a building is completed and put into use, or if a piece of equipment is permanently removed, the system will regenerate the static map.

[0074] Static mapping helps determine the specific location and meaning of dynamic or environmental information, effectively managing and analyzing the overall status of the construction site. It provides a reference framework for dynamic changes and environmental status, ensuring the accurate location and association of all data.

[0075] Exemplarily, building information modeling (BIM) software, such as Autodesk Revit, Bentley Systems, or CAD tools, such as AutoCAD, can be used to create or obtain a detailed layout of the construction site. These tools can accurately depict the three-dimensional structure of buildings, roads, and fixed equipment. The input static layout data is converted into a three-dimensional construction site model using a three-dimensional graphics processing engine (such as Unity 3D, Unreal Engine, or WebGL technology). When the construction site layout changes (such as adding new buildings or equipment), the model can be updated through the BIM / CAD tool and re-imported into the system, and the system will automatically update the static mapping.

[0076] In the specific implementation, the situational data collected by the sensing nodes in real time is used to identify dynamic change factors. For example, the sensing nodes track the movement path of the equipment through infrared sensors and RFID tags, or analyze the real-time activity areas of workers through video to identify changes in material stacking.

[0077] Based on the above-mentioned changing factors, the system generates dynamic mapping information. This information shows the current activities taking place on the construction site, such as a piece of equipment moving from one area to another, or an increase in the density of worker activities in a certain area. Since dynamic factors change rapidly, the mapping frequency can be set to the first mapping frequency (such as once every 1 minute) to update the information in real time to ensure that the latest construction site conditions are reflected. It is understandable that the specific mapping frequency depends on the hardware configuration of the sensing node. If the central processor is used for the same scheduling, both the hardware configuration and the communication quality need to be considered.

[0078] For example, a data integration and analysis platform can be used to integrate the data collected by sensors. The data can be analyzed through a preset algorithm or machine learning model to identify information such as equipment movement paths, worker gathering areas, and dynamic distribution of materials. Visual analysis tools (such as ArcGIS, QGIS, or custom-developed construction site management software) can be used to convert the analysis results into dynamic mapping information.

[0079] After the system generates the dynamic map, it overlays it on the static map information and displays the dynamic activities of the construction site in real time through a three-dimensional visualization engine (such as Unity 3D, Unreal Engine or WebGL).

[0080] In the specific implementation, the sensing node collects the environmental data of the target construction site in real time through the installed sensors (such as temperature and humidity sensors, dust sensors, noise sensors, etc.). These data are automatically recorded by the system and transmitted to the central processing unit.

[0081] The system integrates the collected environmental data to generate environmental mapping information. This mapping shows the current environmental conditions of the construction site, such as the distribution of dust concentration in each area, temperature and humidity changes, etc. The update frequency of environmental data is determined by the second mapping frequency (such as once every 2 minutes) and can be dynamically adjusted according to actual needs. For example, when it is detected that the dust concentration continues to increase, the system can automatically shorten the update frequency of the environmental mapping to more intensively monitor and reflect environmental changes.

[0082] For example, the environmental mapping information may include real-time temperature and humidity distribution maps, dust concentration thermal maps, noise level distribution maps, etc. The generated environmental mapping information is superimposed with the static layout and dynamic change information of the construction site and displayed through a three-dimensional visualization platform.

[0083] In this way, through the generation and real-time update of static, dynamic and environmental three-layer mapping information, the system can fully and accurately reflect the real-time status of the construction site, ensuring that managers can grasp and respond to complex construction environments and dynamic changes in a timely manner. This multi-frequency and multi-level mapping mechanism provides an efficient means of site monitoring and management.

[0084] As an optional implementation, the generated control instruction includes: in response to the existence of a target scenario state in the scenario state of the target area, using the perception node equipped with the target area where the target scenario state exists, generating the current marking information of the target area when the target scenario state exists; wherein the marking information includes: target scenario state, time information, geographic information, equipment operation information in the area, and environmental data; uploading the marking information to the perception node network.

[0085] In the specific implementation, the situation state in the target area is identified through dynamic environment mapping. If the situation state of a certain area is identified as the target situation state (such as equipment failure, dust concentration exceeding the standard, etc.), the generation process of the control instruction will be triggered.

[0086] In the tag information, the target scenario status records the current scenario status of the area, such as "equipment failure" or "dust exceeds the standard". Time information: records the timestamp when the scenario status is detected. Geographic information: marks the specific geographical location of the scenario status (such as a specific area of ​​the construction site). Equipment operation information: records the operating status of the equipment in the area, such as whether the equipment is operating normally, whether there are any abnormalities, etc. Environmental data: includes environmental information such as the current temperature, humidity, and dust concentration of the area.

[0087] When a sensing node in a target area detects the target scenario state, it automatically collects all relevant information (including time, geographic location, equipment operating status, and environmental data) from the node. This information is integrated to generate a complete label information package. The generated label information package is uploaded to the central processing unit or cloud service through the sensing node network. The uploaded label information is distributed in the sensing node network, and other related nodes can access and share this information for subsequent local or global decision-making.

[0088] In this way, through real-time marking of situation status and information uploading, it is possible to respond quickly in the construction site environment, ensuring that managers can obtain key information in a timely manner and make effective decisions.

[0089] As an optional implementation, in response to the sensor node network detecting that a first number of the sensor nodes have uploaded the same target scenario state, all the sensor nodes are used to make a global decision to generate the control instruction.

[0090] In a specific implementation, the tag information uploaded by each node is monitored in real time through the sensor node network. When it is detected that a first number (such as three or more) of sensor nodes have uploaded the same target scenario status (such as excessive dust concentration, equipment failure, etc.), the system identifies this situation as a potential global problem.

[0091] The specific value of the first number can be defined according to the scale of the construction site and the system settings, for example, it can be set to more than three sensing nodes. When the system confirms that the first number of sensing nodes have uploaded the same target scenario state, the global decision process is automatically triggered. This indicates that the problem may affect the entire construction site, or the scenario state has spread to multiple areas.

[0092] Integrate the data of all sensing nodes (including but not limited to situation status, time, geographic location, equipment operation information, environmental data, etc.) to obtain a more comprehensive view of the construction site status. Use this data for comprehensive analysis to generate global control instructions. For example, if the global dust concentration exceeds the standard, the system may command the ventilation equipment in multiple areas to start synchronously, or issue a safe evacuation order for the entire construction site.

[0093] Based on the integrated global data, corresponding control instructions are generated, which may include activating the global early warning system, dispatching emergency resources, controlling or shutting down specific equipment, etc.

[0094] The generated control instructions are sent to each relevant device and control unit through the sensing node network to ensure that the instructions are quickly executed across the entire construction site. For example, when a global equipment failure is detected, the operation of multiple devices can be stopped at the same time to prevent the failure from expanding.

[0095] For example, the data can be analyzed using pattern recognition algorithms (such as cluster analysis or anomaly detection algorithms). For example, Python's Scikit-learn library, or machine learning platforms such as TensorFlow. A threshold is set (such as detecting that more than three nodes report the same situation state). Once this threshold is exceeded, the system automatically marks it as a potential global problem.

[0096] For example, a long short-term memory network (LSTM) can be used to identify potential global problems in construction site management in real time. The sensing nodes in the construction site continuously collect environmental data (such as dust concentration, temperature and humidity) and situational status data (such as equipment status and worker activities). These data are transmitted to the central system in real time for cleaning and standardization. The system segments the data into time series and divides them into training sets and test sets.

[0097] After training, the LSTM model is deployed on a cloud server or edge computing device. The system receives the situational status data uploaded by the sensing node in real time and inputs it into the LSTM model. When multiple nodes report the same or similar abnormal status, the LSTM model can quickly identify whether the situation may be a potential global problem.

[0098] When the LSTM model identifies a potential global problem, the system immediately triggers a global response mechanism. Based on the identification results, the system generates corresponding control instructions, such as starting the full-site ventilation system or sounding an emergency alarm, to ensure the safety of the entire construction site.

[0099] It is understandable that the task of global problem identification can also be completed by other models, for example, convolutional neural networks can also be used, and this application does not limit this.

[0100] In this way, by detecting that multiple sensing nodes upload the same target scenario status, the global decision-making process is triggered to ensure that when global problems occur on the construction site, a quick response can be made and necessary management measures can be taken. This global decision-making mechanism improves the system's ability to respond to large-scale emergencies and ensures the safety of the construction site and the stability of operation.

[0101] As an optional implementation manner, in response to the sensing node network detecting that a certain sensing node has uploaded the target scenario state, the sensing node that has uploaded the target scenario state and the sensing nodes directly adjacent to the sensing node that has uploaded the target scenario state in the sensing node network are used to make a local decision and generate the control instruction;

[0102] After the sensing node uploads the target scenario state, the method further includes:

[0103] Increasing the mapping frequency of the dynamic mapping information from the first mapping frequency to a third mapping frequency;

[0104] The mapping frequency of the environment mapping information is increased from the second mapping frequency to a fourth mapping frequency.

[0105] As an optional implementation manner, the making a local decision and generating the control instruction includes:

[0106] For the sensing nodes directly adjacent to the sensing node that has uploaded the target scenario state, performing a correlation evaluation of the target scenario state, and generating correlation information of each of the directly adjacent sensing nodes;

[0107] Based on the correlation information, make a local decision and generate the control instruction;

[0108] The correlation information includes: environmental data correlation and scenario data correlation.

[0109] In a specific implementation, when a sensing node (for example, node A) detects a target scenario state (such as excessive dust concentration or equipment failure), the node immediately uploads this state to the sensing node network.

[0110] Identify the sensing nodes (such as nodes B and C) directly adjacent to node A, which share the geographical or logical proximity of node A. Trigger a local decision-making process to perform local analysis using the data of node A and its neighboring nodes B and C to decide how to respond to the target situation state.

[0111] Compare the environmental data (such as temperature, humidity, noise level, etc.) of node A with those of its neighboring nodes to evaluate the correlation of environmental data between these nodes. For example, if the dust concentration of neighboring node B also increases, it indicates that environmental factors may have a wider impact.

[0112] At the same time, the system analyzes the contextual data (such as equipment status, worker location, etc.) of node A and adjacent nodes to determine the correlation between these data. For example, node A detects equipment failure, while nodes B and C detect abnormal equipment operation or increased worker density, indicating that the contextual status between these nodes is correlated.

[0113] Based on the above evaluation results, the system generates correlation information for each adjacent node. This information reflects the degree of association between each node and the target situation state, providing a basis for subsequent local decision-making.

[0114] In the specific implementation, local response strategies are formulated according to preset decision rules and combined with the correlation information of each node. For example, if multiple adjacent nodes show high correlation, the system can decide to start local equipment (such as ventilation equipment) or issue a regional alarm, and transmit the instructions to the relevant equipment for execution through the perception node network.

[0115] In addition, in order to monitor the changes in the target area more intensively, the update frequency of the dynamic mapping information is increased from the original first mapping frequency to the third mapping frequency. For example, it is adjusted from updating once every 1 minute to updating once every 10 seconds. At the same time, the system increases the update frequency of the environmental mapping information from the second mapping frequency to the fourth mapping frequency. For example, it is adjusted from updating once every 3 minutes to updating once every 10 seconds to more finely reflect the changes in the environmental status.

[0116] It is understandable that the specific mapping frequency setting here can be implemented in combination with the target scenario. For example, the frequency can be appropriately reduced for the detection of natural phenomena such as strong winds and heavy rains, while a higher mapping frequency can be set for phenomena with great hazards such as fires and dust.

[0117] Exemplarily, local decision-making can be performed using the LSTM model mentioned above. For example, after node A detects the target scenario state, its environmental data and scenario data (such as temperature and humidity, equipment status) are input into the LSTM model together with the data of adjacent nodes B and C. The LSTM model identifies the correlation between node A and its adjacent nodes B and C by analyzing time series data. Based on the analysis results of the LSTM model, the system can determine the potential impact range of the target scenario state. For example, if the LSTM model predicts that the equipment of node B may also suffer a similar failure, the system can issue an early warning and take preventive measures.

[0118] It should be noted that in the training and construction of this type of neural network model, the data needs to be labeled to identify which data sequences correspond to normal states and which correspond to abnormal scenario states (such as equipment failure and environmental exceeding the standard). In the design of the model input layer, multi-channel input can be introduced to input the environmental data and scenario data of different sensing nodes into different channels respectively. Each channel represents the data sequence of a sensing node, so that the time dependency and mutual influence between different nodes can be captured.

[0119] As an optional implementation manner, based on the correlation information, a data redundancy strategy between the sensing node that uploaded the target scenario state and a directly adjacent sensing node is determined; wherein the data redundancy strategy includes:

[0120] When the environmental data and the scenario data of the sensing node and its directly adjacent sensing nodes both show a first correlation, a difference redundancy strategy is adopted during data transmission; wherein the difference redundancy strategy includes: introducing data differences during transmission;

[0121] When the correlation of scenario data is high and the correlation of environmental data is low, a dynamic priority redundancy strategy is adopted in the data transmission process; wherein the dynamic priority redundancy strategy includes: prioritizing the transmission of scenario data and dynamically adjusting the frequency and order of data transmission.

[0122] In the specific implementation, the environmental data (such as dust concentration, temperature and humidity) and scenario data (such as equipment status, worker activities) of the sensing node A and its directly adjacent nodes B and C are monitored in real time. When it is detected that the environmental data and scenario data of node A and its adjacent nodes both show the first correlation (i.e., high correlation), the system automatically triggers the difference redundancy strategy.

[0123] During data transmission, differentiated data redundancy packets are generated by randomly changing the encoding method of data packets, data arrangement order, or introducing slight noise. These differentiated data packets can improve the robustness of data. Even if part of the data is lost or damaged during transmission, the complete information can be restored by reassembling other redundant packets.

[0124] For example, node A detects a sharp increase in dust concentration, and nodes B and C also report similar environmental changes. In this case, the system performs multi-version redundancy on the transmitted data and ensures the integrity of information transmission through differential encoding.

[0125] When the system detects that the situational data (such as equipment status or worker activities) of node A and adjacent nodes B and C show high correlation, but the environmental data correlation is low, the dynamic priority redundancy strategy is triggered.

[0126] Prioritize the transmission of critical information related to situational data (such as worker location, safety status, etc.) based on the importance of the situation, ensuring that these critical data are transmitted with higher priority.

[0127] The frequency and order of data transmission are dynamically adjusted according to real-time environmental changes. For example, when the density of worker activity is detected to increase, the system increases the redundancy level and shortens the data transmission interval, allowing critical information to reach its destination faster.

[0128] For example, if node A detects a device failure and node C reports an increase in worker concentration, the system decides to adopt a dynamic priority redundancy strategy to prioritize the transmission of worker safety-related data and adjust the transmission frequency to ensure timely delivery of safety information.

[0129] It is understandable that the determination and discrimination of specific correlations can be achieved through the LSTM mentioned above and will not be repeated here.

[0130] In this way, through the differential redundancy strategy and dynamic priority redundancy strategy, the system can intelligently select and execute the most suitable redundancy scheme according to the environmental and scenario data correlation between nodes, thereby improving the reliability of data transmission and emergency response capabilities.

[0131] As an optional implementation, the positions of the perception node that uploaded the target scenario state and its adjacent perception nodes can also be controlled based on the correlation information; wherein the perception node has mobility to adjust its own physical position based on the correlation information; and the monitoring angle of the perception node is changed based on the correlation information; wherein the perception node has an adjustable monitoring device.

[0132] In the specific implementation, the target scenario status uploaded by the sensing node A is monitored in real time, and its correlation with the environment and scenario data of the adjacent nodes B and C is evaluated. For example, if the dust concentration exceeding the standard information uploaded by node A is detected to be highly correlated with the scenario data of the adjacent node B, the system triggers the position adjustment of the sensing node according to the correlation information.

[0133] The sensing node is equipped with a mobile device, such as a motor-driven crawler base or a wheeled mobile platform, which can automatically adjust the position of the sensing node under system control.

[0134] Based on the correlation information, the system instructs Node A to move to a more suitable monitoring location. For example, when the dust concentration in the adjacent area increases, Node A can move to an area closer to the source to enhance monitoring coverage of the area.

[0135] For example, if the environmental data of a certain area changes significantly on a construction site, Node A will move autonomously to a higher risk area according to system instructions to ensure real-time monitoring of the changed area. This automated movement function reduces the need for manual adjustments and improves the monitoring flexibility of the system.

[0136] In addition, when the system detects that the monitoring range of sensing node A is not enough to cover the target area, based on the correlation information, the system instructs the sensing node to adjust its monitoring angle. For example, when node C detects that workers are gathering and the workers' activities are beyond the current camera monitoring range, the system will trigger the adjustment of the monitoring angle.

[0137] In the specific implementation, the sensing node is equipped with a rotatable camera or sensor, which can rotate horizontally or vertically under the control of the system to change the monitoring angle. The rotation angle can be achieved through an electric pan-tilt head or other mechanical structure to ensure that the node can cover a larger monitoring area.

[0138] For example, when it is detected that the change in the environment or scenario does not match the monitoring range of the node, the system will instruct the sensing node to adjust the monitoring angle. For example, the system can instruct the camera of the node to rotate 30 degrees to cover the new target area.

[0139] For example, at a construction site, if the worker's activity range monitored by a node exceeds the current camera's field of view, the system will instruct the camera at that node to adjust its angle to ensure real-time monitoring of the worker's activities. This function can improve the efficiency of construction site safety management while reducing blind spots.

[0140] In this way, by automatically adjusting the position of the sensing node and changing the monitoring angle, it can more flexibly respond to changes in the complex construction site environment and reduce human intervention. It can also automatically adjust the node position and monitoring angle according to the real-time situation to ensure key monitoring of high-risk areas and timely detection of potential risks.

[0141] Based on the same inventive concept, the embodiment of the present application also provides an Internet of Things-based smart construction site equipment control system corresponding to the Internet of Things-based smart construction site equipment control method. Since the principle of solving the problem by the system in the embodiment of the present application is similar to the above-mentioned method in the embodiment of the present application, the implementation of the system can refer to the implementation of the method, and the repeated parts will not be repeated.

[0142] Reference Figure 2 FIG. 1 is a schematic diagram of a smart construction site equipment control system based on the Internet of Things provided in an embodiment of the present application, the system comprising:

[0143] The sensing node unit 10 is used to collect environmental data and scenario data of a target construction site in real time using a sensing node network, and generate a dynamic environmental map; wherein the target construction site includes a plurality of target areas, and each of the target areas is equipped with at least one sensing node;

[0144] A scenario analysis unit 20, configured to determine a scenario state of each of the target areas based on the dynamic environment mapping;

[0145] A processing unit 30, configured to generate a processing task using a sensing node equipped with the target area having the target situation state in response to the target situation state existing in the situation state of the target area;

[0146] The control unit 40 is used to control the target equipment in the target construction site to process the target scenario state.

[0147] For descriptions of the processing flow of each module in the system and the interaction flow between each module, reference can be made to the relevant descriptions in the above method embodiments, which will not be described in detail here.

[0148] The present application also provides a computer device, such as Figure 3 FIG. 1 is a schematic diagram of a computer device structure provided in an embodiment of the present application, including:

[0149] A processor 41 and a memory 42; the memory 42 stores machine-readable instructions executable by the processor 41, and the processor 41 is used to execute the machine-readable instructions stored in the memory 42. When the machine-readable instructions are executed by the processor 41, the processor 41 performs the following steps:

[0150] Using a sensor node network to collect environmental data and scenario data of a target construction site in real time and generate a dynamic environmental map; wherein the target construction site includes a plurality of target areas, each of which is equipped with at least one sensor node;

[0151] Based on the dynamic environment mapping, determining the situational status of each of the target areas;

[0152] In response to the presence of a target context state in the context states of the target area, generating a processing task using a sensing node equipped with the target area having the target context state;

[0153] Based on the processing task, the target equipment in the target construction site is controlled to process the target situation state.

[0154] The above-mentioned memory 42 includes internal memory 421 and external memory 422; the memory 421 here is also called internal memory, which is used to temporarily store the calculation data in the processor 41 and the data exchanged with the external memory 422 such as the hard disk. The processor 41 exchanges data with the external memory 422 through the internal memory 421.

[0155] The specific execution process of the above instructions can refer to the steps of the smart construction site equipment control method based on the Internet of Things described in the embodiment of the present disclosure, which will not be repeated here.

[0156] The present disclosure also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the test method described in the above method embodiment are executed. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0157] If the technical solution of this application involves personal information, the product using the technical solution of this application has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using the technical solution of this application has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, the personal information processing rules are notified by obvious signs / information, and the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

[0158] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0159] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0160] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0161] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0162] The preferred embodiments of the present invention disclosed above are only used to help explain the present application. The preferred embodiments do not describe all the details in detail, nor do they limit the present application to specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can understand and use the present application well. The present application is limited only by the claims and their full scope and equivalents.

Claims

1. A smart construction site equipment control method based on the Internet of Things, characterized in that: include: Using a sensor node network to collect environmental data and scenario data of a target construction site in real time and generate a dynamic environmental map; wherein the target construction site includes a plurality of target areas, each of which is equipped with at least one sensor node; Based on the dynamic environment mapping, determining the situational status of each of the target areas; In response to the presence of a target context state in the context states of the target area, generating a processing task using a sensing node equipped with the target area having the target context state; Based on the processing task, the target equipment in the target construction site is controlled to process the target situation state.

2. The method for controlling smart construction site equipment based on the Internet of Things according to claim 1 is characterized in that: The dynamic environment mapping includes: static mapping information, dynamic mapping information, and environment mapping information; generating the dynamic environment mapping includes: Obtaining static layout information of the target construction site and generating static mapping information; Based on the scenario data, determining dynamic change factors and generating dynamic mapping information; Based on the environmental data, generate environmental mapping information; The mapping frequencies of the dynamic mapping information and the environment mapping information are respectively a preset first mapping frequency and a second mapping frequency.

3. The method for controlling smart construction site equipment based on the Internet of Things according to claim 2 is characterized in that: The generated control instructions include: In response to the existence of a target context state in the context state of the target area, using the sensing node equipped with the target area where the target context state exists, generating current marking information of the target area when the target context state exists; The tag information includes: target scenario status, time information, geographic information, equipment operation information in the area, and environmental data; The marking information is uploaded to the sensing node network.

4. The method for controlling smart construction site equipment based on the Internet of Things according to claim 3 is characterized in that: In response to the sensor node network detecting that a first number of the sensor nodes have uploaded the same target scenario state, all the sensor nodes are used to make a global decision to generate the control instruction.

5. The method for controlling smart construction site equipment based on the Internet of Things according to claim 4 is characterized in that: In response to the sensing node network detecting that a certain sensing node has uploaded the target scenario state, using the sensing node that has uploaded the target scenario state and the sensing nodes directly adjacent to the sensing node that has uploaded the target scenario state in the sensing node network to make a local decision and generate the control instruction; After the sensing node uploads the target scenario state, the method further includes: Increasing the mapping frequency of the dynamic mapping information from the first mapping frequency to a third mapping frequency; The mapping frequency of the environment mapping information is increased from the second mapping frequency to a fourth mapping frequency.

6. The method for controlling smart construction site equipment based on the Internet of Things according to claim 5 is characterized in that: The making of local decision and generating the control instruction comprises: For the sensing nodes directly adjacent to the sensing node that has uploaded the target scenario state, performing a correlation evaluation of the target scenario state, and generating correlation information of each of the directly adjacent sensing nodes; Based on the correlation information, make a local decision and generate the control instruction; The correlation information includes: environmental data correlation and scenario data correlation.

7. The method for controlling smart construction site equipment based on the Internet of Things according to claim 6 is characterized in that: Based on the correlation information, a data redundancy strategy between the sensing node that uploaded the target scenario state and a directly adjacent sensing node is determined; wherein the data redundancy strategy includes: When the environmental data and the scenario data of the sensing node and its directly adjacent sensing nodes both show a first correlation, a difference redundancy strategy is adopted during data transmission; wherein the difference redundancy strategy includes: introducing data differences during transmission; When the correlation of scenario data is high and the correlation of environmental data is low, a dynamic priority redundancy strategy is adopted in the data transmission process; wherein the dynamic priority redundancy strategy includes: prioritizing the transmission of scenario data and dynamically adjusting the frequency and order of data transmission.

8. The method for controlling smart construction site equipment based on the Internet of Things according to claim 6 is characterized in that: Also includes: Based on the correlation information, controlling the positions of the sensing node that uploaded the target situation state and its adjacent sensing nodes; The sensing node has a mobile capability and is used to adjust its own physical position based on the correlation information; Based on the correlation information, changing the monitoring angle of the sensing node; Wherein, the sensing node is provided with an adjustable monitoring device.

9. The smart construction site equipment control system based on the Internet of Things is characterized by: include: A sensing node unit is used to collect environmental data and scenario data of a target construction site in real time using a sensing node network, and generate a dynamic environmental map; wherein the target construction site includes a plurality of target areas, and each of the target areas is equipped with at least one sensing node; A scenario analysis unit, configured to determine a scenario state of each of the target areas based on the dynamic environment mapping; a processing unit, configured to generate a processing task using a sensing node provided in the target area where the target situation state exists, in response to the presence of a target situation state in the situation state of the target area; A control unit is used to control the target equipment in the target construction site to process the target scenario state based on the processing task.

10. A computer device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and the processor is used to execute the machine-readable instructions stored in the memory. When the machine-readable instructions are executed by the processor, the processor executes the steps of the smart construction site equipment control method based on the Internet of Things as described in any one of claims 1 to 8.

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