A method for monitoring the condition of construction equipment based on multi-frequency GNSS positioning

Abstract

This invention discloses a method for monitoring the status of construction equipment based on multi-frequency GNSS positioning. The method includes: deploying at least two sets of GNSS positioning and orientation monitoring nodes on the top of the construction equipment, and configuring these nodes to synchronously and periodically receive satellite signals; all GNSS positioning and orientation monitoring nodes synchronously transmitting the received signals to a gateway terminal via a local area network; the gateway terminal synchronously receiving data transmitted by all GNSS positioning and orientation monitoring nodes, parsing it, and then synchronously transmitting it to a cloud server via a wireless network; the cloud server analyzing and processing the multiple sets of data synchronously transmitted by the gateway terminal to generate real-time overall offset and torsion data of the construction equipment; and the cloud server comprehensively analyzing the generated offset and torsion data to generate risk warning commands, which are then sent to the gateway terminal. This solution achieves real-time and accurate monitoring of the overall offset and torsion status of construction equipment based on multi-frequency GNSS positioning.

Description

Technical Field

[0001] This invention relates to engineering construction monitoring technology, specifically to construction equipment displacement monitoring technology. Background Technology

[0002] Construction monitoring refers to the technical means of using monitoring instruments to monitor various control indicators of key parts during the construction process of a building. When the monitored value approaches the control value, an alarm is issued to ensure the safety of construction and to check whether the construction process is reasonable.

[0003] Monitoring the status of construction equipment during building construction provides a guarantee for construction safety. In particular, the status of construction equipment used in high-altitude operations needs to be monitored in real time to ensure the safety of personnel and structures.

[0004] Current condition monitoring solutions for construction equipment mainly involve real-time monitoring of stress, strain, deflection, tilt, wind speed, and other parameters during the construction period. These methods only monitor a portion of the equipment's condition and cannot effectively monitor overall displacement or torsion, thus affecting the accuracy of overall equipment condition monitoring. Furthermore, as construction progresses, the overall condition of the construction equipment constantly changes, further complicating the monitoring of displacement and torsion.

[0005] Therefore, how to effectively monitor the overall displacement and torsion of construction equipment during building construction is a problem that needs to be solved in this field. Summary of the Invention

[0006] In view of the problems existing in the current construction monitoring schemes for monitoring the status of construction equipment, the purpose of this invention is to provide a construction equipment status monitoring method based on multi-frequency GNSS positioning. This method can effectively monitor the overall offset and torsion of construction equipment during the construction process, thereby enabling comprehensive monitoring and risk warning of the performance status of construction equipment.

[0007] To achieve the above objectives, the present invention provides a construction equipment status monitoring method based on multi-frequency GNSS positioning, which specifically includes:

[0008] At least two sets of GNSS positioning and orientation monitoring nodes are deployed on top of the construction equipment, and the GNSS positioning and orientation monitoring nodes are configured to receive satellite signals synchronously and periodically;

[0009] All GNSS positioning and orientation monitoring nodes synchronously transmit the received signals to the gateway terminal via the local area network.

[0010] The gateway terminal synchronously receives data transmitted from all GNSS positioning and orientation monitoring nodes, parses it, and then transmits it synchronously to the cloud server via the wireless network. It also receives instructions from the cloud server, parses the received instructions, and executes them.

[0011] The cloud server analyzes and processes multiple sets of data synchronously transmitted from the gateway terminal to form real-time overall offset and torsion data of the construction equipment; based on the formed offset and torsion data of the construction equipment, the cloud server performs comprehensive analysis to generate risk warning instructions and sends them to the gateway terminal.

[0012] In some embodiments of the present invention, the construction equipment status monitoring method deploys an even array of GNSS positioning and orientation monitoring nodes on the top of the construction equipment, which are symmetrically distributed in pairs.

[0013] In some embodiments of the present invention, the GNSS positioning and orientation monitoring node and the gateway terminal in the construction equipment status monitoring method are connected by a wireless sensor network via a Bluetooth module.

[0014] In some embodiments of the present invention, the GNSS positioning and orientation monitoring node in the construction equipment status monitoring method is configured to receive signals according to a set timing sequence and first store the generated data signals in a local temporary register; after completing one cycle of data signal reception and storage, the data is read from the temporary register, converted to digital by ADC, and then transmitted to the gateway terminal via a local area network.

[0015] In some embodiments of the present invention, the gateway terminal in the construction equipment status monitoring method interacts with the cloud server via an NB-IoT wireless communication module.

[0016] In some embodiments of the present invention, the gateway terminal in the construction equipment status monitoring method realizes data communication with the NB-IoT module through the UART serial port, and realizes mode control of the NB-IoT module in the form of AT command set.

[0017] In some embodiments of the present invention, the cloud server in the construction equipment status monitoring method obtains real-time overall offset and torsion data of the construction equipment by integrating data collected from multiple GNSS positioning and orientation monitoring nodes.

[0018] In some embodiments of the present invention, the process by which the cloud server fuses and determines the real-time overall offset and torsion data of the construction equipment includes the following steps:

[0019] Data processing and fusion:

[0020] The cloud server first preprocesses the received sets of raw data; then, it uses the Kalman filter algorithm, a fusion algorithm, to process the data from each node to obtain more accurate and reliable location and orientation information.

[0021] State calculation:

[0022] The cloud server calculates and analyzes the real-time data obtained from the data processing and fusion steps to determine the current overall position information of the construction equipment; based on this, it calculates the overall offset of the construction equipment by comparing the current overall position information with the initial position information.

[0023] At the same time, based on the real-time data obtained from the data processing and fusion steps, the cloud server extracts the directional information of each node and calculates the torsion angle of the construction equipment in each axis accordingly. Then, based on the torsion calculation mathematical model, the position and direction information of each node are used as input to calculate and transform the torsion angle in the overall coordinate system.

[0024] In some embodiments of the present invention, the construction equipment status monitoring method further includes a step of user terminal interacting with cloud server to obtain and display data.

[0025] In some embodiments of the present invention, the construction equipment status monitoring method further includes a construction equipment status video recognition step based on edge computing.

[0026] The construction equipment status monitoring scheme provided by this invention is based on multi-frequency GNSS positioning to achieve real-time and accurate monitoring of the overall offset and torsional status of construction equipment. Based on the real-time offset and torsional monitoring data, the overall status of construction equipment is effectively monitored, thereby enabling comprehensive monitoring and risk warning of the performance status of construction equipment.

[0027] The construction equipment status monitoring solution provided by this invention can combine multiple structural performance indicators to ensure personnel and construction safety when applied. Attached Figure Description

[0028] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0029] Figure 1 This is a schematic diagram illustrating the configuration of a construction equipment status monitoring system based on multi-frequency GNSS positioning in an example of the present invention.

[0030] Figure 2 This is an example diagram illustrating the configuration of a GNSS positioning and orientation monitoring node in an embodiment of the present invention;

[0031] Figure 3This is a flowchart of the construction equipment status monitoring based on multi-frequency GNSS positioning in an example of the present invention. Detailed Implementation

[0032] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below with reference to specific illustrations.

[0033] This example uses a building construction machine to illustrate the implementation process of the construction equipment status monitoring method based on multi-frequency GNSS positioning in this invention to monitor the overall offset and torsional state of the building construction machine.

[0034] The use of building construction machines involves high-altitude operations, requiring real-time monitoring to ensure the safety of personnel and the structure. Furthermore, the machines continuously ascend during construction, and the use of concrete placing booms and the stacking of steel reinforcement and formwork during construction result in uneven load distribution and complex structural stress. Therefore, effective monitoring of the overall offset and torsional state of the building construction machine is necessary during actual construction.

[0035] In this example, a construction equipment status monitoring system based on multi-frequency GNSS positioning is built for the building construction machine to monitor the overall status of the machine.

[0036] The construction equipment status monitoring system 100 based on multi-frequency GNSS positioning here is specifically composed of several groups of GNSS positioning and orientation monitoring nodes 110, gateway terminals 120, cloud servers 130 and user terminals 140 working together.

[0037] Among them, several groups of GNSS positioning and orientation monitoring nodes 110 are deployed on the top of the building machine, and the several groups of GNSS positioning and orientation monitoring nodes are configured to synchronously and periodically receive satellite signals from different directions on the top of the building machine.

[0038] The gateway terminal 120 in the system serves as a relay device for data transmission between the GNSS positioning and orientation monitoring nodes 110 and the cloud server 130. The gateway terminal 120 is configured to establish a data link with each group of GNSS positioning and orientation monitoring nodes 110 via a local area network, and simultaneously establish a data link with the cloud server 130 via a wireless network. This is used to receive data transmitted by each group of GNSS positioning and orientation monitoring nodes 110, parse and process it, and then transmit it to the cloud server 130. At the same time, the gateway terminal 120 also receives instructions issued by the cloud server, parses the received instructions, and executes them.

[0039] The cloud server 130 in the system is deployed in the cloud and is configured to analyze and process multiple sets of data synchronously transmitted by the gateway terminal 120 in pairs to form real-time overall offset and torsion data of the building machine; at the same time, the cloud server 130 performs comprehensive analysis based on the generated offset and torsion data of the building machine to form risk warning instructions and sends them to the gateway terminal.

[0040] The user terminal 140 in the system establishes a data link with the cloud server 130, enabling data interaction with the cloud server 130, and obtaining and displaying data.

[0041] The following section details the composition of this construction equipment status monitoring system based on multi-frequency GNSS positioning.

[0042] In this example, in order to accurately detect the overall offset and torsion data of the building machine, it is preferable to deploy an even array of GNSS positioning and orientation monitoring nodes on the top of the building machine, and distribute them symmetrically in pairs.

[0043] As an example, this instance deploys four sets of GNSS positioning and orientation monitoring nodes on the top of the building machine, arranged symmetrically in pairs.

[0044] Furthermore, such as Figure 2 As shown, the GNSS positioning and orientation monitoring node 110 in this example is specifically composed of a GNSS positioning and orientation chip 111, an MCU 112, and a main control chip 113 working together.

[0045] The GNSS positioning and orientation chip 111 is used to receive positioning signals transmitted by GNSS satellites. This GNSS positioning and orientation chip 111 is a conventional, stable, and reliable GNSS positioning chip, and will not be described in detail here.

[0046] The MCU 112 is connected to the GNSS positioning and orientation chip 111 to set the working status of the GNSS positioning and orientation chip 111, such as the working timing, initial settings, etc.

[0047] Specifically, the MCU 112 is equipped with an advanced timer to meet the timing delay requirements of single-bus communication. Before engaging in any data communication with the GNSS positioning and orientation chip 111, the MCU 112 is configured to send an initialization timing sequence. Afterward, the MCU and the GNSS positioning and orientation chip 111 follow a read-write timing sequence and exchange data through read-write commands.

[0048] Furthermore, the MCU 112 addresses the GNSS positioning and orientation chip 111 on the bus via the 0xCC instruction, and initiates a single data conversion via the 0x44 instruction. After the GNSS positioning and orientation chip 111 completes the conversion of the received positioning signal, it stores it in a local temporary register. The MCU 112 obtains the permission to read the data in the temporary register via the 0xBE instruction, and reads it sequentially and converts it into specific positioning data.

[0049] The main control chip 113 in this GNSS positioning and orientation monitoring node 110 is used to cooperate with the GNSS positioning and orientation chip 111 to complete the ADC analog-to-digital conversion processing of the data received by the GNSS positioning and orientation chip 111.

[0050] As an example, the main control chip 113 here uses the FM33LG025 main control chip, which is equipped with an ADC analog-to-digital converter module. This ADC module supports offset self-calibration, enabling the program to perform a calibration operation first after the chip is powered on to obtain better accuracy. In addition, a high-precision internal reference source is used as a reference signal to further improve sampling accuracy.

[0051] The ADC (Analog-to-Digital) conversion module first performs clock initialization, interrupt priority setting, and parameter configuration. Then, it performs offset self-calibration to improve sampling accuracy. During data sampling, the data after ADC conversion is stored in the DR (Digital Recording) register, and the value in the register is read using the FL_ADC_ReadConversionData() function. The accuracy of the acquired sampling data is ensured by calling a high-precision reference source for sampling calibration and by averaging multiple samples.

[0052] When multiple analog quantities need to be converted from analog to digital by ADC, multi-channel sampling can be achieved by setting different external input channels and using the same ADC module in a time-division multiplexing manner.

[0053] The GNSS positioning and orientation monitoring node 110 thus formed initializes the GNSS positioning and orientation chip 111 based on the MCU, and configures the working state of the GNSS positioning and orientation chip 111. When multiple groups of GNSS positioning and orientation monitoring nodes 110 are deployed together, the timing and period of receiving positioning signals by each group of GNSS positioning and orientation monitoring nodes 110 are configured based on the MCU in each group of monitoring nodes, thereby ensuring that multiple groups of GNSS positioning and orientation monitoring nodes can synchronously and periodically receive satellite signals.

[0054] Based on this, the GNSS positioning and orientation chip 111 in each group of GNSS positioning and orientation monitoring nodes 110 first stores the received and generated data signals in a local temporary register. After completing one cycle of data signal reception and storage, the main control chip reads the data from the temporary register, performs ADC analog-to-digital conversion, and then transmits it to the gateway terminal via the local area network.

[0055] Furthermore, for the GNSS positioning and orientation monitoring node 110, in this example, a Bluetooth module 114 is configured for each group of GNSS positioning and orientation monitoring nodes 110 to form a local area network between the GNSS positioning and orientation monitoring node 110 and the gateway terminal 120. Based on this local area network, after the node monitoring terminal collects relevant parameters, the data is first transmitted to the gateway terminal 120 that can connect to the cloud server, and then the gateway terminal 120 transmits the data to the cloud server 130.

[0056] As an example, the Bluetooth module 114 in this instance is preferably composed of the PB-02 module of Anxinke. The PB-02 module 114 and the MCU in the GNSS positioning and orientation monitoring node 110 communicate via UART (Universal Asynchronous Receiver / Transmitter) serial port, and the Bluetooth module’s transmit and receive functions are implemented in the form of AT command set and interrupt.

[0057] Therefore, several groups of GNSS positioning and orientation monitoring nodes 110 form a wireless sensor network through their respective configured PB-02 modules. The specific wireless sensor network construction process is as follows:

[0058] After the peripheral circuit design of the PB-02 module is completed, the first step is to flash the Mesh firmware onto the module. The module is then put into a configuration state via serial port commands. The module in this configuration state can be searched using a mobile PHY Mesh application. Each terminal corresponds to a different Address number, and the node terminal name can be modified for management purposes.

[0059] Furthermore, create new groups on the Groups page. Each group corresponds to a Mesh network. Similarly, modify the group name for easy identification and management. By setting the node terminals that need to be configured to the same Group address, you can easily achieve BLE Mesh networking.

[0060] When a node sends data to the address of a gateway terminal, if the gateway is outside the node's signal coverage area, the data can be transmitted to the gateway via multiple hops by other node terminals within the same network whose signals can reach the gateway.

[0061] As further explanation, the PB-02 module selected here, after actual testing, automatically pads the last bit of data with "0" if the data bit length is odd when sending data through the Mesh network. This can affect the accuracy of data parsing in practical applications. Therefore, after the node monitoring terminal collects relevant parameters and performs calibration, a data verification step is added to ensure the accuracy of data transmission. Node terminal 110 uses its own address as the source address and the address of gateway terminal 120 as the destination address. It combines and encodes multiple monitored data packets and sends the data packets to the PB-02 module via serial port using AT commands. The PB-02 module then sends the data to gateway terminal 120 through the Mesh network.

[0062] In this example, the gateway terminal 120 is mainly used to receive and parse the data transmitted by the node terminal 110 through the wireless Mesh network, and transmit the data to the cloud server 130. When it receives instructions from the cloud, the gateway terminal parses and executes them.

[0063] In order to establish a stable and reliable wireless data link with the cloud server 130, an NB-IoT wireless communication module 121 is configured for the gateway terminal 120 in this example, and the data link is established with the cloud server 130 through the NB-IoT wireless communication module 121.

[0064] Furthermore, the main control MCU of the gateway terminal 120 realizes data communication with the NB-IoT wireless communication module 121 through the UART serial port, and realizes mode control of the module in the form of AT command set.

[0065] Furthermore, the data communication between the NB-IoT wireless communication module 121 and the cloud server is mainly achieved through "topics". The MCU sends the "AT+QMTPUB" command to the NB-IoT module through the UART serial port to send information to a specific topic on the server. It also subscribes to a specific topic on the server through the "AT+QMTSUB" command. When the server receives information for that topic, it will forward it synchronously to the devices that have subscribed to that topic, thus enabling the receiving of information from the server.

[0066] Therefore, by customizing different themes, the NB-IoT module can control the data stream sent and received by the server. After subscribing to the corresponding theme, when the NB-IoT module receives data from the server, it returns the data to the MCU via the UART serial port. The data is cached in the program array, and the data sent from the cloud can be parsed and the next action can be executed by parsing the array.

[0067] Based on this, the gateway terminal 120 and the node terminal 110 are configured in the same mesh network through PHY Mesh. When the PB-02 module receives data, the MCU triggers a serial port interrupt. The received data is stored in the buffer array. The system locates the data packet position by keyword, performs reverse decoding according to the designed encoding principle, and then converts the data into the data format required by the MQTT_PUBdata() function to prepare for uploading to the cloud. After the conversion is completed, the buffer array is cleared to prepare for the next data reception.

[0068] In this example, cloud server 130 serves as the data processing center for the entire system. Specifically, the cloud server is configured to process and fuse data from multiple GNSS positioning and orientation monitoring nodes to obtain real-time overall offset and torsion data of the construction equipment.

[0069] To further explain, cloud server 130 here completes the relevant data processing through the following steps:

[0070] (1) Data processing and fusion:

[0071] The cloud server 130 first performs real-time preprocessing on the raw data received from the gateway terminal 120, performs data verification processing on the received raw data in sequence, then performs noise reduction processing on the verified data, and finally performs time synchronization processing on the noise-reduced data.

[0072] Specifically, firstly, a median filtering algorithm is used to remove outlier data and noise to improve data quality; at the same time, since there may be slight differences in the data collection time of different nodes, time synchronization is further performed to ensure the consistency of data at the same point in time.

[0073] Next, the preprocessed data is processed using the Kalman filter algorithm to obtain more accurate and reliable location and orientation information from each node. Here, by combining historical and current data using the Kalman filter algorithm, dynamic estimation is effectively achieved, reducing measurement errors.

[0074] (2) State calculation:

[0075] The cloud server calculates and analyzes the real-time data obtained from the data processing and fusion steps to determine the current overall position information of the construction equipment (i.e., the building machine); based on this, it calculates the overall offset of the construction equipment by comparing the current overall position information with the initial position information.

[0076] At the same time, based on the real-time data obtained from the data processing and fusion steps, the cloud server extracts the directional information of each node and calculates the torsion angle of the construction equipment (i.e., the building machine) in each axis. Then, based on the torsion calculation mathematical model, the position and direction information of each node are used as input to calculate and transform it into the torsion angle in the overall coordinate system.

[0077] Based on this, the cloud server 130 is also configured to perform real-time monitoring and feedback.

[0078] Specifically, when implementing real-time monitoring and feedback, the cloud server 130 updates the calculated overall offset and torsion data in real time and displays it to the user through a visual interface.

[0079] Based on this, the cloud server 130 is also equipped with an alarm mechanism. Based on this alarm mechanism, the cloud server 130 analyzes the real-time status data of the construction equipment (i.e., the building machine) obtained by calculation, and forms real-time monitoring data on the status of the construction equipment (i.e., the building machine). The monitoring data is compared and analyzed with preset thresholds. When the monitoring data exceeds the preset thresholds, an early warning message is generated. If necessary, an early warning signal can be further sent to the construction management personnel.

[0080] As a further configuration, the cloud server 130 in this example is also configured to analyze the real-time overall offset and torsion data of the construction equipment (i.e., the building machine) after processing and fusing the data from multiple GNSS positioning and orientation monitoring nodes, in order to generate corresponding control commands and send them to the gateway terminal.

[0081] Specifically, the process by which the cloud server 130 processes data from multiple GNSS positioning and orientation monitoring nodes transmitted by the gateway terminal 120 and generates corresponding control commands is as follows:

[0082] (1) Data analysis and result generation;

[0083] The cloud server 130 preprocesses and merges multiple sets of field data collected from the gateway terminal 120 to obtain the real-time overall offset and torsion data of the construction equipment (i.e., the building machine). The calculated overall offset and torsion data are further analyzed to determine whether control commands need to be generated.

[0084] (2) Status assessment and decision-making;

[0085] The cloud server 130 performs status assessment and decision-making based on the analysis results: it assesses whether the current status of the construction equipment (i.e., the building machine) is normal based on real-time offset and torsion data.

[0086] Specifically, in this step, the cloud server 130 sets predetermined thresholds for the overall status (such as overall offset and torsion angle) of the construction equipment (i.e., the building machine); it analyzes the calculated real-time status data (such as overall offset and torsion angle) of the construction equipment (i.e., the building machine) to form real-time monitoring data for the status of the construction equipment (i.e., the building machine), and compares this monitoring data with the preset thresholds: if the monitoring data exceeds the preset thresholds, the equipment status is abnormal; if the monitoring data does not exceed the preset thresholds, the equipment status is normal; and based on this analysis result, it generates a status assessment result for the construction equipment (i.e., the building machine).

[0087] Furthermore, based on the generated status assessment results of the construction equipment (i.e., the building machine), the cloud server 130 determines whether to generate control commands according to preset logic rules. If the construction equipment is detected to deviate beyond the safe range, the cloud server 130 will generate corresponding control commands to correct the posture or position of the equipment.

[0088] (3) Control command generation;

[0089] Based on the status assessment results and decision-making logic, the cloud server 130 determines the types of control commands that need to be generated, namely position adjustment commands and attitude adjustment commands.

[0090] Furthermore, this step generates specific control instructions, including the required operating parameters, adjustment direction, amplitude, and speed.

[0091] Furthermore, the cloud server 130 formats the generated control commands according to the requirements of the communication protocol with the gateway terminal to ensure that the commands can be correctly parsed and executed.

[0092] (4) Command sending and execution;

[0093] The cloud server 130 sends the generated control commands to the gateway terminal 120 via the network. Data transmission between the cloud server 130 and the gateway terminal 120 is preferably conducted via a wireless communication network, using a unified communication protocol to ensure correct data transmission. For example, a 4G wireless communication network or a WiFi network can be used for data transmission.

[0094] Based on this, to ensure the security of data transmission, the control commands are encrypted during the transmission process, and the formatted control commands are transmitted to the gateway terminal via the network.

[0095] (5) Execution by the gateway terminal;

[0096] In conjunction with this, the gateway terminal 120 parses and executes the control commands received from the cloud server 130.

[0097] Specifically, the gateway terminal 120 parses the received control commands and extracts the specific operating parameters. Based on the parsed operating parameters, the gateway terminal controls the actuators, hydraulic system, and motor of the construction equipment to make corresponding adjustments to ensure that the equipment returns to normal or achieves the expected operating effect.

[0098] (6) Feedback and monitoring;

[0099] To ensure the effective execution and timely adjustment of control commands, a two-way communication mechanism is preferably established between the cloud server 130 and the gateway terminal 120.

[0100] Specifically, after executing the control command, the gateway terminal 120 feeds back the execution result and current status to the cloud server 130; and the cloud server 130 continuously monitors the status of the construction equipment based on the feedback information and new monitoring data, and adjusts and optimizes the control command as needed, so that the construction equipment is always in a safe working state.

[0101] Through the above process, the cloud server 130 can generate and send control commands to the gateway terminal based on the analysis results, thereby enabling real-time monitoring and adjustment of the construction equipment status.

[0102] In this example, user terminal 140 is specifically configured to send a request to cloud server 130 via a web browser. After receiving the request, cloud server 130 responds and returns the requested data to user terminal 140.

[0103] As an example, the user terminal 140 here consists of a PC terminal running user terminal software.

[0104] In the actual implementation, Tomcat was chosen as the web server, and the front-end pages were mainly implemented using HTML, CSS, and JavaScript. HTML was used to implement the basic page layout, CSS was used to beautify the page style, and JS code was used to implement various dynamic effects of the web pages.

[0105] Furthermore, Echarts, a JavaScript-based data visualization chart library, was introduced into the real-time visualization monitoring interface design to provide users with a more aesthetically pleasing human-computer interaction interface.

[0106] User terminal 140 mainly needs to implement real-time data visualization monitoring and historical data query functions.

[0107] For example, a user accesses the system via a web browser using user terminal 140. After authentication, they can select the specific functions they wish to use. Opening the real-time data visualization monitoring interface, by selecting the monitoring machine, the corresponding area's monitoring page can be accessed. The browser then sends a data request to the web server, using descending time sorting and data volume as filtering criteria to query the database for recent monitoring data. The returned data is visually displayed using Echarts library graphics components in the form of line charts, bar charts, pie charts, etc. During monitoring, Ajax (Asynchronous JavaScript and XML) technology is used to periodically send data query requests to the server at set intervals to obtain data and update the charts, achieving real-time data refresh.

[0108] In this example, in order to more accurately obtain data on the overall offset and torsional state of the building machine, a status video recognition auxiliary device 150 is deployed for the building machine. The status video recognition auxiliary device 150 is configured to view the overall state of the building machine through video and transmit the recognition results to the cloud server 130, thereby assisting the cloud server 130 in further analyzing and evaluating the overall state of the building machine.

[0109] The status video recognition auxiliary device 150 here is specifically composed of several cameras 151 and an edge computing host 152 working together.

[0110] Among them, several cameras 151 are set up to correspond to the building machine and are used to acquire the overall video image of the building machine; the edge computing host 152 performs model inference processing on the video image and transmits the processed image data to the cloud server.

[0111] Specifically, several cameras 151 are deployed around the building machine at different angles to acquire video images of the building machine from different angles in real time.

[0112] The edge computing host 152 is equipped with an algorithm model for recognizing the overall offset and torsion state of the building machine. The image data acquired by the camera is used as real-time input to the algorithm model for recognizing the overall offset and torsion state of the building machine to perform model inference and thus identify the displacement data.

[0113] Furthermore, in this example, the following steps are used to deploy the overall offset and torsional state recognition algorithm model of the building machine on the edge computing host 152:

[0114] (1) Based on machine learning or deep learning models, a convolutional neural network (CNN) is used to construct an algorithm model for recognizing the overall offset and torsional state of a building machine. The model is trained using a large dataset of labeled features of the overall offset and torsional state of the building machine. The model learns to extract the features of the overall offset and torsional state of the building machine from the image and accurately predict the state of the building machine.

[0115] (2) Optimize the model parameters of the overall offset and torsional state recognition algorithm of the building machine to ensure that the model has good performance on the validation set or test set. Based on the hardware conditions of the edge computing host, NVIDIA Jetson series GPU, the model is transformed and optimized, and TensorFlow Lite tools are used for model compression and quantization to reduce model size and inference time.

[0116] (3) Install the necessary operating system and software environment on the edge computing host, including Linux operating system, Python interpreter, deep learning framework PyTorch, and configure hardware accelerators (such as GPU, TPU) to accelerate model inference.

[0117] (4) Deploy the trained model to the edge computing host. For example, transfer the model file (.h5) to the host and install the corresponding model loading and inference code on the host.

[0118] (5) Configure a camera to transmit relevant data to the edge computing host in real time; at the same time, write data preprocessing code on the host to clean, transform and enhance the received raw data as necessary to meet the requirements of the model input.

[0119] (6) Write model inference code, load the deployed model, and perform real-time inference on the received data. This typically involves inputting preprocessed data into the model, obtaining the model's output, and calculating the displacement estimation results.

[0120] (7) Smooth the output of the model and perform threshold judgment to improve the accuracy and reliability of the results.

[0121] After deploying the overall offset and torsion state recognition algorithm model of the building construction machine in the edge computing host 152 based on the above steps, the process of recognizing the overall offset and torsion state of the building construction machine based on the video images transmitted by the camera 151 is as follows:

[0122] (1) The deployed cameras capture the overall video image of the building machine in real time and send it to the edge computing host via a wireless transmission protocol.

[0123] (2) The edge computing host performs noise reduction, scaling, cropping and normalization on the received image data to improve image quality and adapt to the input requirements of the model;

[0124] (3) The edge computing host calls the building machine’s overall offset and torsional state recognition algorithm model. It uses a deep learning model (such as a convolutional neural network CNN) to learn how to extract effective features from the image during the training process to process the preprocessed image data and output information related to the building machine’s overall offset and torsional state.

[0125] (4) Smooth the displacement data output by the model and make threshold judgments to improve the accuracy and reliability of the results, and output the processed displacement data to a specified location or system for subsequent use or analysis.

[0126] See Figure 3 The process of real-time monitoring of the overall offset and torsional state of the building construction machine based on the construction equipment status monitoring of multi-frequency GNSS positioning in this example is as follows:

[0127] (1) Data Acquisition:

[0128] Multiple GNSS positioning and orientation monitoring nodes are installed on the stress-bearing and easily deformable parts of the building machine. Each node collects precise position and orientation information through a multi-frequency GNSS receiver. Simultaneously, the GNSS positioning and orientation monitoring nodes are configured to synchronously and periodically collect precise position and orientation information.

[0129] (2) Data transmission:

[0130] All GNSS positioning and orientation monitoring nodes synchronously transmit the received signals to the gateway terminal via the local area network. Each GNSS positioning and orientation monitoring node is configured to receive signals according to a set timing sequence and first store the generated data signals in a local buffer. After completing one cycle of data signal reception and storage, the data is read from the buffer, converted into analog-to-digital signals by an ADC, and then transmitted to the gateway terminal via the local area network.

[0131] At the same time, the gateway terminal synchronously receives data transmitted by all GNSS positioning and orientation monitoring nodes, parses it, and then transmits it synchronously to the cloud server via the wireless network. It also receives instructions from the cloud server, parses the received instructions, and executes them.

[0132] Furthermore, during the real-time data transmission between each GNSS positioning and orientation monitoring node and the gateway terminal, as well as between the gateway terminal and the cloud server, the integrity and accuracy of the verification code data are checked, and the security of data transmission is ensured by using encryption and data verification mechanisms.

[0133] The encryption mechanism here incorporates timestamps, ensuring data security while also guaranteeing the accuracy of the cloud server's synchronization processing of various data sets.

[0134] (3) Data processing and fusion:

[0135] The cloud server first performs real-time preprocessing on the received raw data, and then performs real-time verification, noise reduction, and time synchronization on multiple sets of data transmitted from the gateway terminal. Specifically, algorithms are used to remove abnormal data and noise to improve data quality; and time synchronization is performed to ensure data consistency at the same point in time, given that there may be slight differences in the data collection time of different nodes.

[0136] Next, after preprocessing, the data from each node is processed using the Kalman filter algorithm to obtain more accurate and reliable position and orientation information.

[0137] (4) State calculation:

[0138] The cloud server calculates the real-time overall position information of the building machine based on the data processing and fusion steps, and calculates the overall offset of the construction equipment by comparing the current overall position information and the initial position information of the construction equipment.

[0139] Meanwhile, based on the real-time overall position information of the building machine obtained from the data processing and fusion steps, the cloud server determines the directional information of each node of the building machine, and further calculates the torsion angle of the building machine in each axis.

[0140] Based on this, the cloud server uses a mathematical model for calculating the state of a building machine to convert the position and orientation information of each node into a torsion angle in the overall coordinate system.

[0141] (5) Status assessment and decision-making;

[0142] The cloud server performs status assessment and decision-making based on the analysis and calculation results: It evaluates whether the current state of the building machine is normal based on real-time offset and torsion data. Based on preset thresholds, it analyzes and judges whether the corresponding monitoring data exceeds the threshold. If the building machine's offset exceeds the safe range, the cloud server generates corresponding control commands to correct the building machine's attitude or position.

[0143] (6) Control command generation;

[0144] Based on the status assessment results and decision-making logic, the cloud server determines the type of control command to be generated, such as position adjustment command and attitude adjustment command; and generates specific control command content, including the required operating parameters, adjustment direction, amplitude, and speed.

[0145] The cloud server formats the generated control commands according to the requirements of the communication protocol with the gateway terminal to ensure that the commands can be correctly parsed and executed.

[0146] (7) Command sending and execution;

[0147] The cloud server sends the generated control commands to the gateway terminal via the network. The cloud server and the gateway terminal typically use a unified communication protocol to ensure correct data transmission. To ensure data transmission security, the control commands are usually encrypted during transmission, and the formatted control commands are then transmitted to the gateway terminal via the network.

[0148] (8) Execution by the gateway terminal;

[0149] After receiving the control command, the gateway terminal parses and executes it: the gateway terminal parses the received control command and extracts the specific operating parameters; based on the parsed operating parameters, the gateway terminal controls the building machine's actuators, hydraulic system, and motor to make corresponding adjustments to ensure that the equipment returns to normal or achieves the expected operating effect.

[0150] (9) Feedback and monitoring;

[0151] Based on the bidirectional communication mechanism established between the cloud server and the gateway terminal, the gateway terminal sends the execution result and current status back to the cloud server after executing control commands. The cloud server continuously monitors the status of the construction equipment based on the feedback information and new monitoring data, adjusting and optimizing control commands as needed to ensure the construction equipment is always in a safe operating state.

[0152] Through the above process, the cloud server can generate and send control commands to the gateway terminal based on the analysis results, thereby enabling real-time monitoring and adjustment of the construction equipment status.

[0153] (10) Real-time monitoring and feedback:

[0154] The cloud server updates the calculated overall offset and torsion data in real time and displays it to users through a visual interface; at the same time, according to the alarm mechanism, when the monitored data exceeds the preset threshold, an early warning signal is sent to the construction management personnel.

[0155] (11) User data retrieval and display;

[0156] The user sends a request to the cloud server through a web browser. After receiving the request, the cloud server responds and returns the requested data to the user.

[0157] As can be seen from the above, the construction equipment status monitoring scheme presented in this example is based on multi-frequency GNSS positioning to achieve real-time and accurate monitoring of the overall offset and torsional status of the construction equipment. Based on the real-time offset and torsional monitoring data, the overall status of the construction equipment is effectively monitored, thereby enabling comprehensive monitoring and risk warning of the performance status of the construction equipment.

[0158] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the status of construction equipment based on multi-frequency GNSS positioning, characterized in that, include: At least two sets of GNSS positioning and orientation monitoring nodes are deployed on top of the construction equipment, and the GNSS positioning and orientation monitoring nodes are configured to receive satellite signals synchronously and periodically; The GNSS positioning and orientation monitoring node is also configured to receive signals according to a set timing sequence and first store the generated data signals in a local buffer. After completing one cycle of data signal reception and storage, the data is read from the buffer, converted into analog and digital data by an ADC, and then transmitted to the gateway terminal via a local area network. All GNSS positioning and orientation monitoring nodes synchronously transmit the received signals to the gateway terminal via the local area network; The gateway terminal synchronously receives data transmitted from all GNSS positioning and orientation monitoring nodes, parses it, and then transmits it synchronously to the cloud server via the wireless network. It also receives instructions from the cloud server, parses the received instructions, and executes them. The cloud server analyzes and processes multiple sets of data synchronously transmitted from the gateway terminal to generate real-time overall offset and torsion data of the construction equipment. Based on this data, the cloud server performs comprehensive analysis to generate risk warning commands, which are then sent to the gateway terminal. The cloud server obtains real-time overall offset and torsion data of the construction equipment by fusing data collected from multiple GNSS positioning and orientation monitoring nodes, including the following steps: Data processing and fusion: The cloud server first preprocesses the received sets of raw data; then, it uses the Kalman filter algorithm, a fusion algorithm, to process the data from each node to obtain more accurate and reliable location and orientation information. State calculation: The cloud server calculates and analyzes the real-time data obtained from the data processing and fusion steps to determine the current overall position information of the construction equipment; based on this, it calculates the overall offset of the construction equipment by comparing the current overall position information with the initial position information. At the same time, based on the real-time data obtained from the data processing and fusion steps, the cloud server extracts the directional information of each node and calculates the torsion angle of the construction equipment in each axis accordingly. Then, based on the torsion calculation mathematical model, the position and direction information of each node are used as input to calculate and transform the torsion angle in the overall coordinate system.

2. The construction equipment status monitoring method based on multi-frequency GNSS positioning according to claim 1, characterized in that, In the construction equipment status monitoring method, an even array of GNSS positioning and orientation monitoring nodes are deployed on the top of the construction equipment, and they are symmetrically distributed in pairs.

3. The construction equipment status monitoring method based on multi-frequency GNSS positioning according to claim 1, characterized in that, In the construction equipment status monitoring method, a wireless sensor network is established between the GNSS positioning and orientation monitoring node and the gateway terminal via a Bluetooth module.

4. The method for monitoring the status of construction equipment based on multi-frequency GNSS positioning according to claim 1, characterized in that, In the construction equipment status monitoring method, the gateway terminal interacts with the cloud server via an NB-IoT wireless communication module.

5. The construction equipment status monitoring method based on multi-frequency GNSS positioning according to claim 4, characterized in that, In the construction equipment status monitoring method, the gateway terminal communicates with the NB-IoT module via a UART serial port and controls the NB-IoT module's mode using an AT command set.

6. The method for monitoring the status of construction equipment based on multi-frequency GNSS positioning according to claim 1, characterized in that, The construction equipment status monitoring method also includes the step of user terminal interacting with cloud server to obtain and display data.

7. The method for monitoring the status of construction equipment based on multi-frequency GNSS positioning according to claim 1, characterized in that, The construction equipment status monitoring method also includes a construction equipment status video recognition step based on edge computing.

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