Coastal deep foundation pit multi-parameter cooperative monitoring system and method based on LoRa and distributed optical fiber sensing

Abstract

The invention relates to the technical field of coastal deep foundation pit monitoring, in particular to a coastal deep foundation pit multi-parameter collaborative monitoring system and method based on LoRa and distributed optical fiber sensing, and the system comprises a data acquisition layer, a data transmission layer, a data processing and analysis layer and an application layer. According to the coastal deep foundation pit multi-parameter cooperative monitoring system and method based on LoRa and distributed optical fiber sensing, the geological condition, the surrounding environment and the construction condition are known by conducting detailed exploration on a coastal deep foundation pit site, the types, the number and the arrangement positions of the sensors are determined according to monitoring requirements, and the monitoring accuracy is improved. The installation and debugging of a distributed optical fiber sensor, a displacement sensor and a seepage pressure sensor are completed, then a LoRa gateway is reasonably arranged around the deep foundation pit, the communication range of all sensor nodes can be covered, a LoRa wireless communication module is configured and tested, and the stability and reliability of data transmission are ensured.

Description

Technical Field

[0001] This invention relates to the field of coastal deep foundation pit monitoring technology, specifically to a multi-parameter collaborative monitoring system and method for coastal deep foundation pits based on LoRa and distributed optical fiber sensing. Background Technology

[0002] In urban construction in coastal areas, deep foundation pit engineering is a core component of underground space development. These pits often exceed 15 meters in depth and require construction in complex environments characterized by soft soil foundations, high groundwater levels, and salt spray corrosion. The stability of deep foundation pits directly determines project safety: soft soil foundations are prone to uneven settlement, high groundwater levels can lead to leakage and piping, and salt spray environments accelerate corrosion of the supporting structures. These factors can all potentially cause accidents such as foundation pit collapse, cracking of surrounding buildings, and damage to underground pipelines.

[0003] The existing multi-parameter collaborative monitoring system for deep coastal foundation pits based on LoRa and distributed optical fiber sensing cannot adapt and install various different sensors according to the needs of monitoring geological conditions, surrounding environment and construction conditions, resulting in poor practicality. Therefore, it is necessary to design a practical multi-parameter collaborative monitoring system and method for deep coastal foundation pits based on LoRa and distributed optical fiber sensing. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-parameter collaborative monitoring system and method for deep coastal foundation pits based on LoRa and distributed optical fiber sensing, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-parameter collaborative monitoring system and method for deep coastal foundation pits based on LoRa and distributed optical fiber sensing, including a data acquisition layer, a data transmission layer, a data processing and analysis layer, and an application layer.

[0006] The data acquisition layer includes distributed optical fiber sensors, displacement sensors, and seepage pressure sensors. Distributed optical fibers are laid along the perimeter and key internal parts of the deep coastal foundation pit. Utilizing the backscattering principle of optical fibers, multiple physical quantities such as temperature, strain, and vibration are measured simultaneously. By detecting changes in the light signal at different locations in the optical fibers, the deformation and stress distribution information of the deep foundation pit structure and the surrounding soil are accurately obtained. During installation, depending on the geometry and geological conditions of the deep foundation pit, the optical fibers are arranged in a ring, straight line, or grid pattern on the surface of the foundation pit support structure or buried in the soil to ensure comprehensive coverage of the monitoring area. The foundation pit support structure can be a pile wall and anchor bolts.

[0007] Preferably, some traditional displacement sensors are installed at key parts of the deep foundation pit. These sensors can be installed at the top and bottom of the support structure and at soil layers. The displacement sensors can be inclinometers and total stations. They are equipped with seepage pressure sensors to measure the lateral displacement, vertical settlement, and groundwater seepage pressure parameters of the deep foundation pit. This provides more direct and accurate local measurement data, which can be mutually verified with distributed fiber optic sensing data.

[0008] Preferably, the data transmission layer includes a LoRa wireless communication module and a LoRa gateway. Distributed fiber optic sensor signals are connected to the LoRa wireless communication module, displacement sensor signals are connected to the LoRa wireless communication module, and seepage pressure sensor signals are connected to the LoRa wireless communication module. Data collected by the distributed fiber optic sensor, seepage pressure sensor, and displacement sensor in the data acquisition layer is transmitted to the LoRa wireless communication module equipped on the sensor node. The collected data is packaged and sent to the nearby LoRa gateway in the form of wireless signals. The LoRa gateway is responsible for receiving the data from the sensor node, aggregating it, and transmitting it to the remote data processing and analysis layer via wired or wireless means.

[0009] Preferably, the data processing and analysis layer includes a data receiving and preprocessing module, a multi-parameter collaborative analysis module, and a data storage and management module. The monitoring system also includes a data inspection module. The LoRa gateway signal is connected to the data receiving and preprocessing module, and the data inspection module signal is connected to the data structure and preprocessing module. The LoRa gateway transmits the raw monitoring data to the data receiving and preprocessing module. The data receiving and preprocessing module decodes, verifies, and performs preliminary processing on the data, removing noise and abnormal data, and converting the data into a unified format for subsequent analysis and processing. The data inspection module performs integrity checks on the received data. If data is missing or incorrect, it sends a retransmission request to the sensor node; it filters the data to remove high-frequency noise; and it synchronizes the data collected by different sensors to ensure data consistency.

[0010] Preferably, the multi-parameter collaborative analysis module uses advanced algorithms and models to comprehensively analyze the preprocessed multi-parameter monitoring data, explore the inherent correlations and patterns between the data, assess the safety status of the deep foundation pit, and establish a multi-parameter collaborative analysis model using methods such as time series analysis, neural networks, and fuzzy logic to conduct real-time assessment and early warning of the safety status of the deep foundation pit. At the same time, it combines historical data and engineering experience to verify and correct the analysis results, thereby improving the accuracy of the assessment.

[0011] Preferably, the data storage and management module stores the processed and analyzed monitoring data in the database, establishes a complete data management system, realizes efficient data storage, query and retrieval, and at the same time, backs up and restores the data to ensure data security and integrity.

[0012] Preferably, the data storage and management module uses a relational database or a non-relational database to store the monitoring data. The relational database can be MySQL or Oracle, and the non-relational database can be MongoDB. The appropriate storage structure is selected according to the characteristics of the data and query requirements. The data is backed up regularly and the backup data is stored on an off-site server or in cloud storage to prevent data loss.

[0013] Preferably, the application layer includes a safety early warning module, a visualization module, and a decision support module. The data processing and analysis layer processes the data and transmits it to the application layer. Based on the safety assessment results of the data processing and analysis layer, the safety early warning module promptly issues early warning information when the monitored parameters exceed the preset safety threshold, notifying relevant personnel to take corresponding measures. The early warning information can be sent to project managers, construction personnel, and monitoring personnel via SMS, email, and APP push.

[0014] Preferably, after the processed data is sent to project managers, construction personnel, and monitoring personnel, the monitoring data and safety assessment results can be displayed on the monitoring terminal in the form of intuitive charts, curves, and 3D models. This allows project managers and monitoring personnel to understand the safety status of the deep foundation pit in real time. Through visualization, the changing trends and spatial distribution of parameters can be observed more clearly, providing an intuitive basis for decision-making. The charts, curves, and 3D models can include displacement cloud maps, strain distribution maps, temperature change curves, seepage pressure value lines, and real-time display of safety assessment results and early warning information for the deep foundation pit. At the same time, it supports the query and playback of historical data, facilitating the review and analysis of the safety status of the deep foundation pit. Finally, through the decision support module, combined with monitoring data, safety assessment results, and engineering experience, decision support is provided to project managers to help them formulate reasonable construction plans, adjust construction parameters, and take effective safety measures.

[0015] A method for applying the LoRa and distributed fiber optic sensing-based multi-parameter collaborative monitoring system for deep coastal foundation pits includes the following steps:

[0016] S1. Site survey and sensor deployment

[0017] A detailed on-site investigation of the coastal deep foundation pit was conducted to understand the geological conditions, surrounding environment, and construction status. Based on monitoring needs, the type, quantity, and placement of sensors were determined, and the installation and commissioning of distributed fiber optic sensors, displacement sensors, and seepage pressure sensors were completed.

[0018] S2 and LoRa network setup

[0019] LoRa gateways should be strategically deployed around the deep foundation pit to ensure coverage of the communication range for all sensor nodes. The LoRa wireless communication modules should be configured and tested to guarantee the stability and reliability of data transmission.

[0020] S3, Data Processing and Analysis Platform Construction

[0021] Build a data processing and analysis platform, including the development and integration of data receiving and preprocessing modules, multi-parameter collaborative analysis modules, and data storage and management modules. Debug and optimize the platform to ensure accurate and efficient processing and analysis of monitoring data.

[0022] S4, Application Layer Development

[0023] Develop a security early warning module, a visualization module, and a decision support module to enable timely delivery of early warning information, intuitive display of monitoring data, and scientific provision of decision-making suggestions. Test and verify the application layer to ensure the integrity and ease of use of its functions.

[0024] S5, System Integration and Trial Operation

[0025] The data acquisition layer, data transmission layer, data processing and analysis layer, and application layer are integrated and tested to check the coordination and compatibility between the various parts of the system. The system is then put into trial operation to collect, transmit, process, and analyze monitoring data in real time, thereby verifying the system's performance and reliability.

[0026] S6. Formal Operation and Maintenance

[0027] After successful trial operation, the system was officially put into use. During system operation, the sensors were calibrated and maintained regularly to ensure their measurement accuracy; the LoRa network was monitored and optimized to ensure the quality of data transmission; and the data processing and analysis platform was updated and upgraded to improve its analytical capabilities and decision support level.

[0028] Compared with the prior art, the beneficial effects of the present invention are:

[0029] 1. A multi-parameter collaborative monitoring system and method for deep coastal foundation pits based on LoRa and distributed optical fiber sensing. Through detailed on-site investigation of deep coastal foundation pits, the geological conditions, surrounding environment and construction status are understood. Based on the monitoring requirements, the type, quantity and layout of sensors are determined, and the installation and commissioning of distributed optical fiber sensors, displacement sensors and seepage pressure sensors are completed.

[0030] 2. This multi-parameter collaborative monitoring system and method for deep coastal foundation pits based on LoRa and distributed optical fiber sensing ensures coverage of the communication range of all sensor nodes by rationally deploying LoRa gateways around the deep foundation pit. The LoRa wireless communication module is configured and tested to guarantee the stability and reliability of data transmission. Then, a data processing and analysis platform is built, including the development and integration of data receiving and preprocessing modules, multi-parameter collaborative analysis modules, and data storage and management modules. The platform is debugged and optimized to ensure accurate and efficient processing and analysis of monitoring data.

[0031] 3. This multi-parameter collaborative monitoring system and method for deep coastal foundation pits based on LoRa and distributed optical fiber sensing, through the development of a safety early warning module, a visualization module, and a decision support module, enables timely transmission of early warning information, intuitive display of monitoring data, and scientific provision of decision-making suggestions. The application layer is tested and verified to ensure its functional integrity and ease of use. The data acquisition layer, data transmission layer, data processing and analysis layer, and application layer are jointly debugged to check the coordination and compatibility between the various parts of the system. The system is then put into trial operation, and monitoring data is collected, transmitted, processed, and analyzed in real time to verify the system's performance and reliability. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart of the multi-parameter collaborative monitoring system for deep coastal foundation pits according to the present invention. Detailed Implementation

[0034] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0035] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0036] Please see Figure 1 The present invention provides the following technical solutions:

[0037] A multi-parameter collaborative monitoring system and method for deep coastal foundation pits based on LoRa and distributed optical fiber sensing includes a data acquisition layer, a data transmission layer, a data processing and analysis layer, and an application layer.

[0038] The data acquisition layer includes distributed optical fiber sensors, displacement sensors, and seepage pressure sensors. Distributed optical fibers are laid along the perimeter and key parts inside the deep coastal foundation pit. Utilizing the backscattering principle of optical fibers, multiple physical quantities such as temperature, strain, and vibration are measured simultaneously. By detecting changes in the light signal at different locations in the optical fibers, the deformation and stress distribution information of the deep foundation pit structure and the surrounding soil are accurately obtained. During installation, the optical fibers are arranged in a ring, straight line, or grid pattern on the surface of the foundation pit support structure or buried in the soil, depending on the geometry and geological conditions of the deep foundation pit, to ensure comprehensive coverage of the monitoring area. The foundation pit support structure can be a pile wall or anchor bolts.

[0039] Traditional displacement sensors can be installed at key locations in deep foundation pits. These sensors can be installed at the top and bottom of the support structure and at soil layers. The displacement sensors can be inclinometers and total stations. They are equipped with seepage pressure sensors to measure the lateral displacement, vertical settlement, and groundwater seepage pressure parameters of the deep foundation pit. This provides more direct and accurate local measurement data, which can be cross-validated with distributed fiber optic sensing data.

[0040] The data transmission layer includes a LoRa wireless communication module and a LoRa gateway. Distributed fiber optic sensor signals are connected to the LoRa wireless communication module, displacement sensor signals are connected to the LoRa wireless communication module, and seepage pressure sensor signals are connected to the LoRa wireless communication module. In the data acquisition layer, the data collected by the distributed fiber optic sensor, seepage pressure sensor, and displacement sensor are transmitted to the LoRa wireless communication module equipped on the sensor node. The collected data is packaged and sent to the nearby LoRa gateway in the form of wireless signals. The LoRa gateway is responsible for receiving the data from the sensor node, aggregating it, and transmitting it to the remote data processing and analysis layer via wired or wireless means.

[0041] The data processing and analysis layer includes a data receiving and preprocessing module, a multi-parameter collaborative analysis module, and a data storage and management module. The monitoring system also includes a data inspection module. The LoRa gateway signal is connected to the data receiving and preprocessing module, and the data inspection module signal is connected to the data structure and preprocessing module. The LoRa gateway transmits the raw monitoring data to the data receiving and preprocessing module, which decodes, verifies, and performs preliminary processing on the data, removing noise and abnormal data, and converting the data into a unified format for subsequent analysis and processing. The data inspection module performs integrity checks on the received data; if data is missing or incorrect, it sends a retransmission request to the sensor nodes. It also filters the data to remove high-frequency noise and synchronizes data collected from different sensors to ensure consistency. The multi-parameter collaborative analysis module uses advanced algorithms and models to comprehensively analyze the preprocessed multi-parameter monitoring data, uncovering the inherent relationships between the data. To assess the safety status of deep foundation pits, a multi-parameter collaborative analysis model is established using time series analysis, neural networks, and fuzzy logic. This model provides real-time assessment and early warning of the safety status of deep foundation pits. Simultaneously, historical data and engineering experience are combined to verify and correct the analysis results, improving the accuracy of the assessment. The data storage and management module stores the processed and analyzed monitoring data in a database, establishing a comprehensive data management system to achieve efficient data storage, querying, and retrieval. Data backup and recovery are also performed to ensure data security and integrity. The data storage and management module uses either relational or non-relational databases to store the monitoring data. Relational databases can be MySQL and Oracle, while non-relational databases can be MongoDB. Appropriate storage structures are selected based on data characteristics and query requirements. Data is backed up regularly and stored on off-site servers or in cloud storage to prevent data loss.

[0042] The application layer includes a safety early warning module, a visualization module, and a decision support module. The data processing and analysis layer processes the data and transmits it to the application layer. Based on the safety assessment results from the data processing and analysis layer, the safety early warning module promptly issues early warning information when monitored parameters exceed preset safety thresholds, notifying relevant personnel to take appropriate measures. Early warning information can be sent to project managers, construction personnel, and monitoring personnel via SMS, email, and app push notifications. After the processed data is sent to project managers, construction personnel, and monitoring personnel, the monitoring data and safety assessment results can be displayed on the monitoring terminal in intuitive charts, curves, and 3D models, facilitating project managers and monitoring personnel. Personnel can monitor the safety status of deep foundation pits in real time. Through visualization, the changing trends and spatial distribution of parameters can be observed more clearly, providing an intuitive basis for decision-making. The charts, curves, and 3D models can include displacement cloud maps, strain distribution maps, temperature change curves, seepage pressure value lines, and real-time display of safety assessment results and early warning information. At the same time, it supports the query and playback of historical data, facilitating the review and analysis of the safety status of deep foundation pits. Finally, through the decision support module, which combines monitoring data, safety assessment results, and engineering experience, it provides decision support for engineering managers, helping them to formulate reasonable construction plans, adjust construction parameters, and take effective safety measures.

[0043] A method for a multi-parameter collaborative monitoring system for deep coastal foundation pits based on LoRa and distributed optical fiber sensing includes the following steps:

[0044] S1. Site survey and sensor deployment

[0045] A detailed on-site investigation of the coastal deep foundation pit was conducted to understand the geological conditions, surrounding environment, and construction status. Based on monitoring needs, the type, quantity, and placement of sensors were determined, and the installation and commissioning of distributed fiber optic sensors, displacement sensors, and seepage pressure sensors were completed.

[0046] S2 and LoRa network setup

[0047] LoRa gateways should be strategically deployed around the deep foundation pit to ensure coverage of the communication range for all sensor nodes. The LoRa wireless communication modules should be configured and tested to guarantee the stability and reliability of data transmission.

[0048] S3, Data Processing and Analysis Platform Construction

[0049] Build a data processing and analysis platform, including the development and integration of data receiving and preprocessing modules, multi-parameter collaborative analysis modules, and data storage and management modules. Debug and optimize the platform to ensure accurate and efficient processing and analysis of monitoring data.

[0050] S4, Application Layer Development

[0051] Develop a security early warning module, a visualization module, and a decision support module to enable timely delivery of early warning information, intuitive display of monitoring data, and scientific provision of decision-making suggestions. Test and verify the application layer to ensure the integrity and ease of use of its functions.

[0052] S5, System Integration and Trial Operation

[0053] The data acquisition layer, data transmission layer, data processing and analysis layer, and application layer are integrated and tested to check the coordination and compatibility between the various parts of the system. The system is then put into trial operation to collect, transmit, process, and analyze monitoring data in real time, thereby verifying the system's performance and reliability.

[0054] S6. Formal Operation and Maintenance

[0055] After successful trial operation, the system was officially put into use. During system operation, the sensors were calibrated and maintained regularly to ensure their measurement accuracy; the LoRa network was monitored and optimized to ensure the quality of data transmission; and the data processing and analysis platform was updated and upgraded to improve its analytical capabilities and decision support level.

[0056] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A coastal deep foundation multi-parameter collaborative monitoring system based on LoRa and distributed optical fiber sensing, comprising a data acquisition layer, characterized in that: It also includes a data transmission layer, a data processing and analysis layer, and an application layer; The data acquisition layer includes a distributed optical fiber sensor, a displacement sensor, and a seepage pressure sensor. The distributed optical fiber is laid along the periphery and key parts inside the coastal deep foundation pit. The backscattering principle of the optical fiber is used to measure multiple physical quantities such as temperature, strain, and vibration. By detecting the changes in the light signals at different positions in the optical fiber, the deformation and stress distribution information of the deep foundation pit structure and the surrounding soil can be accurately obtained. During installation, the optical fiber is arranged in a ring, straight line, or grid form on the surface of the foundation pit support structure or buried in the soil according to the geometric shape and geological conditions of the deep foundation pit, ensuring comprehensive coverage of the monitoring area. The foundation pit support structure can be a pile wall and an anchor rod.

2. The coastal deep foundation pit multi-parameter collaborative monitoring system based on LoRa and distributed optical fiber sensing according to claim 1, characterized in that: Some traditional displacement sensors are installed at key parts of the deep foundation pit, which can be installed on the top and bottom of the support structure and at the soil layering site. The displacement sensor can be an inclinometer and a total station. The seepage pressure sensor is installed to measure the lateral displacement, vertical settlement, and groundwater seepage pressure parameters of the deep foundation pit, providing more direct and accurate local measurement data, which can be verified with the distributed optical fiber sensing data.

3. The coastal deep foundation pit multi-parameter collaborative monitoring system based on LoRa and distributed optical fiber sensing according to claim 2, characterized in that: The data transmission layer includes a LoRa wireless communication module and a LoRa gateway. The distributed optical fiber sensor signal is connected to the LoRa wireless communication module, the displacement sensor signal is connected to the LoRa wireless communication module, and the seepage pressure sensor signal is connected to the LoRa wireless communication module. The data collected by the distributed optical fiber sensor, seepage pressure sensor, and displacement sensor in the data acquisition layer is transmitted to the LoRa wireless communication module equipped in the sensor node. The collected data is packaged and sent to the nearby LoRa gateway in the form of wireless signals. The LoRa gateway is responsible for receiving the data from the sensor node and transmitting it to the remote data processing and analysis layer through wired or wireless means after aggregation.

4. The coastal deep foundation pit multi-parameter collaborative monitoring system based on LoRa and distributed optical fiber sensing according to claim 3, characterized in that: The data processing and analysis layer includes a data receiving and preprocessing module, a multi-parameter collaborative analysis module, and a data storage and management module. The monitoring system also includes a data checking module. The LoRa gateway is connected to the data receiving and preprocessing module, and the data checking module is connected to the data structure and preprocessing module. The LoRa gateway transmits the original monitoring data to the data receiving and preprocessing module. The data receiving and preprocessing module decodes, checks, and preliminarily processes the data, removes noise and abnormal data, and converts the data to a unified format for subsequent analysis and processing. The data checking module checks the integrity of the received data. If data is missing or incorrect, a retransmission request is sent to the sensor node. The data is filtered to remove high-frequency noise. The data collected by different sensors is time-synchronized to ensure consistency.

5. The coastal deep foundation pit multi-parameter collaborative monitoring system based on LoRa and distributed optical fiber sensing according to claim 4, characterized in that: The multi-parameter collaborative analysis module uses advanced algorithms and models to comprehensively analyze the pre-processed multi-parameter monitoring data, excavates the internal correlation and rules between the data, evaluates the safety state of the deep foundation pit, and uses time series analysis, neural networks, fuzzy logic, etc. Method, establish a multi-parameter collaborative analysis model, real-time evaluation and early warning of the safety state of the deep foundation pit, at the same time, combined with historical data and engineering experience, verify and correct the analysis results, improve the accuracy of the evaluation. 6.The coastal deep foundation pit multi-parameter collaborative monitoring system based on LoRa and distributed optical fiber sensing of claim 5, characterized in that: The data storage and management module stores the processed and analyzed monitoring data in the database, establishes a perfect data management system, realizes efficient storage, query and retrieval of data, and at the same time, backs up and recovers the data, ensures the safety and integrity of the data.

7. The coastal deep foundation pit multi-parameter collaborative monitoring system based on LoRa and distributed optical fiber sensing according to claim 6, characterized in that: The data storage and management module uses a relational database or a non-relational database to store monitoring data. The relational database can be MySQL and Oracle, and the non-relational database can be MongoDB. According to the characteristics and query requirements of the data, select the appropriate storage structure, back up the data regularly, and store the backup data in off-site servers or cloud storage to prevent data loss.

8. The coastal deep foundation pit multi-parameter collaborative monitoring system based on LoRa and distributed optical fiber sensing according to claim 7, characterized in that: The application layer includes a safety warning module, a visualization display module and a decision support module. The data processing and analysis layer transmits the processed data to the application layer, and uses the safety warning module to issue warning information in a timely manner when the monitoring parameters exceed the preset safety threshold based on the safety evaluation results of the data processing and analysis layer, and notify relevant personnel to take appropriate measures. Warning information can be sent to project managers, construction personnel and monitoring personnel through SMS, email and APP push.

9. The coastal deep foundation pit multi-parameter collaborative monitoring system based on LoRa and distributed optical fiber sensing according to claim 7, characterized in that: After the processed data is sent to project managers, construction personnel and monitoring personnel, monitoring data and safety evaluation results can be displayed on monitoring terminals in the form of intuitive charts, curves and three-dimensional models, which can help project managers and monitoring personnel understand the safety status of deep foundation pits in real time. Through visualization, the trend and spatial distribution of parameter changes can be observed more clearly to provide intuitive basis for decision-making. The chart curves and three-dimensional models drawn can include deep foundation pit displacement cloud map, strain distribution map, temperature change curve, seepage pressure value line graph, real-time display of safety evaluation results and warning information, while supporting historical data query and playback, which is convenient for reviewing and analyzing the safety status of deep foundation pits. Finally, through the decision support module, combined with monitoring data, safety evaluation results and engineering experience, decision support is provided for project managers to help them develop reasonable construction plans, adjust construction parameters and take effective safety measures.

10. A method applied to the coastal deep foundation pit multi-parameter collaborative monitoring system based on LoRa and distributed optical fiber sensing according to any one of claims 1-9, characterized in that, The steps include: S1, field investigation and sensor arrangement Detailed investigation of the coastal deep foundation pit site, understand the geological conditions, surrounding environment and construction situation, determine the type, number and arrangement position of the sensor according to the monitoring requirements, complete the installation and debugging of distributed optical fiber sensor, displacement sensor and seepage pressure sensor; S2, LoRa network construction LoRa gateways are strategically deployed around the deep foundation pit to ensure coverage of the communication range of all sensor nodes. The LoRa wireless communication modules are configured and tested to ensure the stability and reliability of data transmission. S3, Data Processing and Analysis Platform Construction Build a data processing and analysis platform, including the development and integration of data receiving and preprocessing modules, multi-parameter collaborative analysis modules, and data storage and management modules. Debug and optimize the platform to ensure accurate and efficient processing and analysis of monitoring data. S4, Application Layer Development Develop a security early warning module, a visualization module, and a decision support module to enable timely delivery of early warning information, intuitive display of monitoring data, and scientific provision of decision-making suggestions. Test and verify the application layer to ensure the integrity and ease of use of its functions. S5, System Integration and Trial Operation The data acquisition layer, data transmission layer, data processing and analysis layer, and application layer are jointly tested to check the coordination and compatibility between the various parts of the system. The system is then put into trial operation to collect, transmit, process, and analyze the monitoring data in real time, and to verify the system's performance and reliability. S6. Formal Operation and Maintenance After the system passed the trial run, it was officially put into use. During the operation of the system, the sensors were calibrated and maintained regularly to ensure their measurement accuracy. Monitor and optimize the LoRa network to ensure data transmission quality; update and upgrade the data processing and analysis platform to improve its analytical capabilities and decision support level.

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