Limited space monitoring system based on LoRa communication and UWB positioning

Abstract

The invention relates to the technical field of wireless communication and positioning, and discloses a limited space monitoring system based on LoRa communication and UWB positioning, and the system comprises a plurality of sensor nodes which are configured to collect environment data and carry out data transmission through a LoRa communication module; the at least one base station is configured to receive the environment data from the plurality of sensor nodes and position the position data through the UWB positioning module; the signal propagation model module is configured to simulate signal attenuation, path loss and multi-path effect between the sensor node and the base station; and the multi-objective optimization module is configured to perform multi-objective optimization of network stability, power consumption, signal coverage and interference according to the node position, power distribution and path selection. According to the invention, the LoRa communication and UWB positioning technologies are combined, and a multi-target optimization and adaptive adjustment mechanism is adopted, so that the problems of unstable communication, insufficient positioning precision and energy efficiency optimization in a complex and limited space in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication and positioning technology, and in particular to a limited space monitoring system based on LoRa communication and UWB positioning. Background Art

[0002] With the development of the Internet of Things and intelligent monitoring technology, confined space monitoring systems are widely used in complex environments such as mines, underground facilities, and industrial plants. These systems rely on wireless communication and positioning technologies to achieve data transmission, equipment monitoring, and personnel positioning. Although traditional wireless communication technologies, such as Wi-Fi and ZigBee, can provide basic monitoring functions in simple environments, they face many technical challenges in complex confined space environments, especially in terms of signal coverage, positioning accuracy, and energy efficiency optimization. In addition, most of the existing positioning technologies are based on lower-precision Wi-Fi or Bluetooth, which is difficult to meet the application requirements of high precision and high reliability.

[0003] Existing limited space monitoring systems use a single communication or positioning technology, most of which cannot optimize multiple key performance indicators at the same time. For example, although traditional wireless communication technologies (such as Wi-Fi and ZigBee) can provide certain communication functions, in complex environments, the signals will be severely attenuated and interfered, resulting in unstable communication. Most of the existing positioning technologies rely on low-precision solutions, with large positioning errors, and it is difficult to meet the precise positioning needs, especially in environments such as mines and underground pipelines. In addition, traditional systems focus on a single goal in optimizing energy efficiency, such as extending node life by reducing power consumption, but ignore network stability and signal quality, resulting in the inability to ensure communication reliability while improving energy efficiency. In general, existing technologies lack comprehensive multi-objective optimization capabilities and cannot simultaneously balance multiple goals such as power consumption, signal quality, and positioning accuracy, so they cannot give full play to their advantages in complex environments. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a limited space monitoring system based on LoRa communication and UWB positioning, which solves the problem in the prior art that communication stability, positioning accuracy and energy efficiency cannot be simultaneously optimized in a complex limited space environment, and provides a comprehensive multi-objective optimization problem.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a limited space monitoring system based on LoRa communication and UWB positioning, comprising: Multiple sensor nodes configured to collect environmental data and transmit data via LoRa communication modules; at least one base station configured to receive environmental data from a plurality of sensor nodes and locate position data through a UWB positioning module; a signal propagation model module configured to simulate signal attenuation, path loss, and multipath effects between sensor nodes and a base station; A multi-objective optimization module configured to perform multi-objective optimization of network stability, power consumption, signal coverage and interference based on node location, power allocation and path selection; A node deployment and power control module, configured to dynamically adjust the location and power configuration of the sensor nodes according to the optimization results; Data transmission and communication module, configured to realize the transmission and processing of LoRa communication and UWB positioning data; A positioning and path selection optimization module is configured to optimize the data transmission path between the sensor node and the base station to ensure positioning accuracy and communication stability; The optimization algorithm and adaptive adjustment module are configured to perform adaptive optimization according to environmental changes and real-time data to ensure continuous and stable operation of the system.

[0006] Preferably, the multi-objective optimization module further comprises: An optimization unit for minimizing system power consumption, which manages power consumption according to the transmission distance, power requirements and energy consumption of each sensor node; An optimization unit for maximizing network coverage, which ensures that each node can establish effective communication with at least one base station by optimizing the layout of nodes and signal propagation paths; An optimization unit for minimizing signal interference and path loss, which adjusts the communication links between nodes through shortest path selection and interference minimization strategy.

[0007] Preferably, the signal propagation model module uses a Rayleigh attenuation model or a Rician attenuation model to model signal path loss, and dynamically adjusts propagation parameters according to the signal propagation environment.

[0008] Preferably, the signal propagation model module calculates signal attenuation through random field theory, dynamically updates the signal strength of each node, and provides accurate prediction of signal attenuation and path loss.

[0009] Preferably, the node deployment and power control module adjusts the power of the sensor nodes according to the optimization algorithm, so that the power allocation of the nodes meets the required network coverage requirements and communication quality, while reducing unnecessary energy consumption.

[0010] Preferably, the data transmission and communication module includes: LoRa communication module, used for long-distance, low-power data transmission between sensor nodes and base stations; The UWB positioning module is used to accurately locate the sensor nodes and transmit the positioning data to the base station.

[0011] Preferably, the optimization algorithm and adaptive adjustment module optimize the sensor nodes, path selection and power allocation in the network based on the particle swarm optimization algorithm or the quantum particle swarm optimization algorithm to ensure that the system maintains optimal performance in a dynamically changing environment.

[0012] Preferably, the multi-objective optimization module further dynamically adjusts the data transmission rate and transmission frequency according to the real-time load and channel conditions of the network to improve the efficiency of data transmission and reduce network congestion.

[0013] Preferably, the node deployment and power control module automatically adjusts the position and power of the node through an adaptive adjustment mechanism when the communication quality of the sensor node is poor or encounters signal interference, thereby ensuring the stability and communication quality of the network.

[0014] The present invention provides a limited space monitoring system based on LoRa communication and UWB positioning. It has the following beneficial effects: 1. The present invention optimizes communication quality and positioning accuracy by adopting a limited space monitoring system based on LoRa communication and UWB positioning, combined with multi-objective optimization and dynamic adaptive adjustment technology. This technical solution not only ensures network coverage and signal quality, but also greatly reduces power consumption by intelligently optimizing node layout and power allocation. Compared with the prior art solutions that rely solely on fixed configuration or static optimization, the present invention effectively overcomes the stability problems caused by node position and communication interference, and significantly improves the adaptability and efficiency of the system in complex environments.

[0015] 2. The present invention improves the accuracy of path selection by adopting a signal propagation model and path loss prediction technology in a complex environment, combined with the Rayleigh and Rician attenuation models. By updating the path loss and signal strength in real time, the system can dynamically optimize the communication path and ensure the stability of data transmission. Compared with the path planning in traditional technologies that ignores the dynamic changes of the environment, the present invention fully considers the impact of environmental factors on the signal, thereby solving the problems of path instability and network discontinuity in traditional methods.

[0016] 3. The present invention introduces particle swarm optimization or quantum particle swarm optimization algorithm to perform multi-objective optimization calculations, which greatly improves the system optimization efficiency and calculation accuracy. This technical solution enables the system to simultaneously optimize multiple objectives such as power consumption, coverage, path selection and signal interference. Compared with the local optimization method in the prior art, the present invention achieves a more sophisticated balance between multiple objectives and avoids the performance degradation of the system when facing complex environments.

[0017] 4. The present invention combines the optimization algorithm with the adaptive adjustment module to ensure that the system can automatically adjust according to environmental changes during long-term operation, thereby ensuring the stability and efficiency of the network. Compared with the traditional technology that requires manual intervention and adjustment, the present invention realizes a fully automated optimization process, reduces labor costs, and can maintain high reliability and adaptability in variable practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Please see attached Figure 1 ,The embodiment of the present invention provides a limited space monitoring system based on LoRa communication and UWB positioning, a plurality of sensor nodes are configured to collect environmental data and transmit data through the LoRa communication module; at least one base station configured to receive environmental data from a plurality of sensor nodes and locate position data through a UWB positioning module; a signal propagation model module configured to simulate signal attenuation, path loss, and multipath effects between sensor nodes and a base station; In this embodiment, the signal propagation model module adopts two common attenuation models, namely the Rayleigh attenuation model and the Rician attenuation model. Through these two models, the signal strength and path loss can be predicted respectively under different environmental conditions, thereby providing necessary input data for the subsequent multi-objective optimization module. In practical applications, these two models are respectively applicable to different scenarios: the Rayleigh attenuation model is used in a multipath propagation environment, while the Rician attenuation model is applicable to an environment with a direct path.

[0021] Specifically, the signal propagation model module predicts signal attenuation based on the various propagation paths and interference sources in the environment by combining physical attenuation models, random field theory, and path loss models. The important function of this module is to consider and process the impact of multipath effects on signal propagation caused by obstacles, reflections, and diffraction. The module calculates the path loss between each node and the base station and outputs the signal strength of each communication link. These data will be used in subsequent steps for power consumption optimization, path selection, and network coverage calculation.

[0022] As an option, the signal propagation model module uses the Rayleigh attenuation model to simulate a multipath propagation environment. In this environment, the signal experiences reflection, scattering, and refraction along multiple different paths and eventually reaches the receiving end. The Rayleigh model calculates the path loss between each node and the base station by modeling the coherent superposition of multipath signals. In this model, the attenuation of signal strength is logarithmically related to the path distance, which can be expressed by the following formula:

[0023] in: is the path loss from the transmitting source to the receiver; is the reference distance Signal loss on is the path loss exponent, which usually ranges from 2 to 5 and varies depending on the specific environment; is the distance between the source and the receiver; It is an attenuation random variable caused by multipath effect and usually obeys Rayleigh distribution.

[0024] In another implementation, when there is a significant direct path in the signal transmission environment, the Rician attenuation model is used. The Rician model assumes that there is a strong direct path, while other paths are propagated through reflection and diffraction. Under this model, the attenuation of the signal is not only related to the distance of the transmission path, but also affected by the direct path. The path loss of the Rician attenuation model can be expressed as:

[0025] in: is the path loss; is the reference signal loss; is the path loss exponent; is the distance between the node and the base station; It is the random attenuation caused by reflection and diffraction, usually obeying the Rician distribution.

[0026] Generally speaking, when there is a direct path in the transmission path, the Rician attenuation model can provide a more accurate path loss estimation. This is because when a direct path exists, the system can make full use of the strong direct signal, thereby improving signal quality and reducing attenuation.

[0027] In one possible implementation, in order to better adapt to the various signal propagation characteristics in a confined space environment, the signal propagation model module combines random field theory to further refine the calculation of path loss by modeling the random process of signal attenuation. Specifically, the module simulates the attenuation of signal strength through the following formula:

[0028] in: For location Signal strength at is the initial signal strength; is the attenuation constant, which represents the signal loss in spatial propagation; is the path length of signal propagation.

[0029] This method models the signal strength through random field theory, further improving the adaptability to complex environments, especially when the number of nodes is large and widely distributed, which can effectively improve the accuracy of signal prediction.

[0030] In general, the signal propagation model module can accurately estimate the path loss and signal strength between each sensor node and the base station through the combination of physical modeling and random process theory. These calculation results will be passed as input to the subsequent multi-objective optimization module to help with power consumption management, network coverage optimization and path selection. This model not only takes into account traditional propagation losses, but also effectively considers the impact of complex factors such as reflection and scattering on signal propagation, so that the system can operate in a variety of limited space environments and optimize network performance.

[0031] In other embodiments, if the environment changes or severe signal attenuation occurs under some extreme conditions, the system will update the signal propagation model in real time through an adaptive adjustment mechanism. Specifically, the system can dynamically adjust the parameters of the signal propagation model based on real-time measured data to ensure that the communication path between each node and the base station remains stable at all times.

[0032] In summary, the signal propagation model module is one of the core modules of the present invention, which accurately simulates the signal propagation process and provides an accurate data basis for subsequent optimization. By comprehensively using the Rayleigh attenuation model, the Rician attenuation model and the random field theory, this module can operate efficiently in various complex environments, ensuring that the overall performance of the system is optimized and meeting the communication and positioning requirements in limited space.

[0033] A multi-objective optimization module configured to perform multi-objective optimization of network stability, power consumption, signal coverage and interference based on node location, power allocation and path selection; In this embodiment, the multi-objective optimization module uses the Lagrange multiplier method and the particle swarm optimization (PSO) algorithm. First, the Lagrange multiplier method helps optimize the mutual influence between multiple objectives by introducing constraints. Then, through the particle swarm optimization algorithm, the system can search for the optimal solution in the high-dimensional parameter space, thereby improving the efficiency and accuracy of the optimization.

[0034] Specifically, the optimization objectives of the multi-objective optimization module include: Minimize power consumption: Reduce unnecessary energy consumption by adjusting the power allocation and data transmission rate of the nodes. This goal is achieved by: The system calculates the energy consumption based on the distance, signal strength and transmission frequency of each sensor node to minimize the power consumption as much as possible.

[0035] During the optimization process, power and frequency are dynamically adjusted to ensure that each node transmits data only when needed, avoiding unnecessary waste of electricity.

[0036] This goal can be expressed as:

[0037] in, Indicates The power consumption of each sensor node is is the total number of nodes.

[0038] Maximize network coverage: Ensure that each sensor node can establish effective communication with at least one base station. This goal optimizes the node layout and transmission path so that the signal can cover the widest possible area, thereby improving the stability and reliability of the overall network.

[0039] The network coverage target is expressed by the following formula:

[0040] in, It is The node and The distance between base stations, is the maximum distance covered by communication, is an indicator function, and its value is 1 when the distance between the node and the base station is less than the maximum coverage range.

[0041] Minimize signal interference and path loss: Through the shortest path selection algorithm and interference minimization strategy, the system can reduce the impact of signal reflection and multipath effects. This goal is the key to ensuring the quality of network communication.

[0042] The optimization objectives of signal interference and path loss are expressed by the following formula:

[0043] in, Indicates Node to The path loss of each base station. Path loss is a key influencing factor. Too high path loss will cause signal attenuation and reduce communication quality.

[0044] As an option, during the optimization process, the module introduces weight coefficients to balance the priorities between different objectives. For example, the system may adjust the weights of various objectives according to different application scenarios to ensure that the network can provide sufficient coverage and communication quality with minimal power consumption.

[0045] Generally, the multi-objective optimization module will dynamically adjust these weights according to different network environments and the real-time status of sensor nodes. For example, when the network load is low, the optimization process may give priority to reducing power consumption; while when the network load is high, the system may focus on improving network coverage or reducing signal interference.

[0046] In one possible implementation, the optimization module combines multiple objectives into a unified objective function through the Lagrange multiplier method and introduces constraints. These constraints include: Each sensor node in the network must be able to receive signals from at least one base station; Signal strength should meet certain minimum requirements to ensure communication quality; The path loss must not exceed a certain threshold to ensure that the signal is not excessively attenuated.

[0047] The constrained optimization problem can be expressed by the following Lagrangian function:

[0048] in, , and γ is the corresponding target weight, is the Lagrange multiplier, which represents the influence of the constraint on the objective function.

[0049] In other embodiments, to further improve the optimization efficiency, the system can also combine the particle swarm optimization (PSO) algorithm or the quantum particle swarm optimization (QPSO) algorithm. PSO searches for the optimal solution by simulating the behavior of a particle group in the search space. In the PSO process, each particle represents a possible solution, and its position and speed are continuously updated, and finally the optimal node layout and path selection solution are found.

[0050] The update formula of PSO is:

[0051]

[0052] in, It is The particle in The speed at the iteration, It is The position of a particle, is the best historical position of the particle is the global optimal position of the group.

[0053] As another option, quantum particle swarm optimization (QPSO) can further improve the efficiency of optimization. QPSO introduces the superposition property of quantum bits, allowing particles to search in parallel at multiple locations, thereby finding the optimal solution more quickly. The update formula of QPSO is:

[0054] in, is the quantum rotation factor, which controls the particle's ability to jump. It is the global optimal solution.

[0055] In other possible implementations, the optimization module can dynamically adjust the parameters in the optimization process according to real-time environmental changes and the status of sensor nodes. For example, the network may increase the weight of path selection optimization when it is under high load, while the system may prioritize energy efficiency when it has low power consumption requirements.

[0056] In summary, the multi-objective optimization module effectively integrates multiple optimization objectives, uses the Lagrange multiplier method and PSO / QPSO algorithm to ensure the balance of power consumption, network coverage and signal quality in different network environments. This optimization process not only improves the efficiency of the system, but also ensures its stability and reliability in complex limited space environments.

[0057] A node deployment and power control module, configured to dynamically adjust the location and power configuration of the sensor nodes according to the optimization results; In this embodiment, the node deployment and power control module first determines the optimal location and appropriate power allocation of each node based on the signal strength and path loss data provided by the signal propagation model module. The signal propagation model module provides an estimate of the signal loss and propagation strength for the communication path between the node and the base station through the Rayleigh and Rician attenuation model. Based on this information, the multi-objective optimization module proposes the optimal node layout and power control scheme, and the node deployment and power control module then performs actual configuration according to the scheme.

[0058] Specifically, the workflow of the node deployment and power control module is as follows: Node location optimization: The node deployment and power control module determines the optimal location of each sensor node in a limited space by analyzing the output of the multi-objective optimization module. In order to ensure the maximum network coverage, the nodes will be reasonably distributed in the network area so as to establish a stable communication link with at least one base station. In this process, the communication distance between the node and the base station is taken into account to avoid the occurrence of signal coverage blind spots.

[0059] The distribution of nodes takes the following factors into consideration: The minimum communication distance between each node and at least one base station.

[0060] The distance between nodes should be set to avoid excessive signal overlap and interference.

[0061] The rationality of node location ensures that the coverage area is maximized and there are no blank areas.

[0062] For example, in a mine environment, the node can be placed in an appropriate location based on the mine's structural layout and equipment distribution to ensure that the signal covers the entire mine.

[0063] Power optimization: Power control is another important task of node deployment and power control module. After the layout of each node is determined, the module adjusts the power of each node according to the results of the multi-objective optimization module. The optimization goal of power allocation is to minimize power consumption while maintaining good signal quality and communication distance.

[0064] The basic principle of power optimization is to minimize the energy consumption of nodes while ensuring that each node can communicate effectively with the base station. This process is achieved by adjusting the transmission power and communication frequency of the nodes. Through dynamic power adjustment, nodes only transmit data when necessary, avoiding ineffective power consumption.

[0065] The formula for power optimization is:

[0066] in, For the The power consumption of each node, is the total number of nodes. The goal of power allocation is to minimize the power consumption of all nodes and ensure that the energy efficiency of the system is maximized.

[0067] As an option, the power control module can dynamically adjust the power allocation strategy based on real-time channel conditions and network load. When the network load is low or the communication distance between nodes is close, the system can reduce the transmission power to save energy; when the network load is high or the distance between nodes is far, the system will increase the power appropriately to ensure that the signal can penetrate obstacles and achieve the required communication quality.

[0068] In one possible implementation, the power control module works closely with the data transmission and communication module. The data transmission and communication module uses LoRa communication and UWB positioning technology to transmit and locate data, and the power control module adjusts the transmission power of the node according to real-time feedback during the data transmission process (such as signal strength, interference, etc.). Through this adaptive control, the system can always maintain stable communication quality under changing environmental conditions.

[0069] In general, the design of node deployment and power control module takes the following factors into consideration: Network coverage: Ensure that all nodes are within the coverage of the base station to avoid communication blind spots.

[0070] Power requirements: The power consumption of each node should be adjusted according to the actual needs of the network to reduce unnecessary energy consumption.

[0071] Signal interference: Avoid signal overlap between nodes that are too close to each other and reduce mutual interference.

[0072] In other embodiments, the node deployment and power control module can also introduce an adaptive algorithm so that the system can automatically adjust the node position and power when facing different environmental changes. For example, in an environment that suddenly changes (such as some obstacles are moved or added), the system can re-evaluate the node position through an adaptive algorithm and optimize the signal transmission path without increasing additional energy consumption.

[0073] In another embodiment, in order to further improve the stability and coverage quality of the network, the node deployment and power control module can also be combined with a path selection optimization algorithm. When the network load is high, the path selection optimization algorithm will determine how to adjust the node's transmission path to ensure maximum reliability of data transmission under a limited power budget.

[0074] In short, the node deployment and power control module plays a vital role in the present invention. It not only reasonably arranges the nodes according to the signal propagation model and the results of the optimization algorithm, but also dynamically adjusts the node power to ensure the stability and energy efficiency of the system. Through this flexible optimization method, the system can provide efficient and reliable communication services in complex environments, especially in application scenarios in limited spaces, such as mines, basements and complex industrial environments, demonstrating its great application value.

[0075] Data transmission and communication module, configured to realize the transmission and processing of LoRa communication and UWB positioning data; In this embodiment, the data transmission and communication module includes two main submodules: LoRa communication module and UWB positioning module. The LoRa communication module uses LoRa technology for low-power, long-distance data transmission, which is suitable for environmental data exchange between sensor nodes and base stations; the UWB positioning module provides high-precision positioning function to ensure that the position of each sensor node can be fed back in real time and accurately. The two work together to support the efficient operation of the system.

[0076] Specifically, the LoRa communication module is mainly used to transmit environmental data (such as temperature, humidity, gas concentration, etc.) collected by sensor nodes to the base station. The advantages of LoRa are its low power consumption and long communication range, which is suitable for environments that require long-term operation and difficult wiring. The working principle of the LoRa communication module is to modulate the data into a wireless signal through frequency modulation spread spectrum (FSK) or spread spectrum modulation technology, and then transmit it to the receiving end through radio waves. Since the LoRa network supports a star topology, communication between nodes does not require a relay station, so stable communication quality can be maintained over a wide geographical area.

[0077] During data transmission, the LoRa module also uses the **Adaptive Data Rate (ADR) technology. ADR can automatically adjust the data transmission rate according to the quality of the communication link, thereby improving communication efficiency while ensuring low power consumption. The LoRa communication module also includes a power control function, which dynamically adjusts the transmission power according to the network conditions to further reduce energy consumption.

[0078] The goal of data transmission is to minimize signal transmission delay and improve the communication stability of the system through the optimized transmission path. Through the signal strength and path loss data provided by the multi-objective optimization module, the LoRa module will dynamically adjust the signal transmission path so that data can be transmitted with the lowest power consumption and the best transmission efficiency.

[0079] As an option, the modulation method used by the LoRa communication module can be adjusted according to the actual application scenario. For example, in a more complex environment, a stronger spread spectrum technology may be selected to enhance anti-interference capabilities, while in a simpler environment, a lower data transmission rate can be selected to further save energy.

[0080] In one possible implementation, when the network is congested or the signal interference is severe, the LoRa communication module will avoid the interference channel through frequency hopping technology. Frequency hopping technology can jump between multiple channels to avoid the interference caused by static channels and ensure the smooth transmission of data.

[0081] On the other hand, the UWB positioning module is responsible for providing accurate location data of the sensor node. UWB technology can achieve centimeter-level positioning accuracy, which is essential for position tracking in a limited space. The UWB module sends short pulse signals and calculates the time difference (ToF) of signal propagation to achieve accurate location calculation. The base station calculates the specific location of the node based on the arrival time difference of the received UWB signal and provides real-time positioning data through the UWB positioning algorithm.

[0082] In general, the UWB module uses the time difference positioning algorithm (TDOA) to estimate the location of the node based on the signal delay between the base station and the sensor node. Through the synergy of multiple base stations, the system can achieve three-dimensional positioning of the node. In complex environments, UWB technology has strong anti-interference capabilities, especially when the signal is affected by reflection or multipath effects, the UWB system can still provide relatively accurate positioning data.

[0083] As an option, the UWB module may dynamically adjust the signal transmission frequency and power according to the real-time situation of the network during operation. If the node is in a relatively narrow or confined space, the system can enhance the transmission power of the UWB signal to ensure that the signal can penetrate obstacles for effective transmission.

[0084] In other possible implementations, the data transmission and communication modules can combine the advantages of LoRa and UWB technologies for joint optimization. In this implementation, the UWB module is used to provide accurate real-time positioning, while the LoRa module is used to transmit these positioning data and environmental monitoring data to the base station. The system will decide when to use LoRa for data transmission and when to use UWB for positioning based on the specific situation, ensuring efficient and reliable communication in different application scenarios.

[0085] In summary, the data transmission and communication module realizes efficient and stable data transmission and positioning functions in the limited space monitoring system through the combination of LoRa communication technology and UWB positioning technology. The LoRa module ensures low power consumption and long-distance communication capabilities, while the UWB module provides high-precision positioning services. The two complement each other and jointly support the real-time monitoring and data transmission of the system. In the present invention, the data transmission and communication module ensures the efficient operation of the system and adapts to different environmental requirements by dynamically adjusting the communication parameters, transmission path and power configuration.

[0086] A positioning and path selection optimization module is configured to optimize the data transmission path between the sensor node and the base station to ensure positioning accuracy and communication stability; In this embodiment, the positioning and path selection optimization module works closely with the signal propagation model module and the multi-objective optimization module. The signal propagation model module provides path loss and signal strength information between each node and the base station, which provides the basis for path selection optimization. The multi-objective optimization module provides specific requirements for path optimization based on multiple objectives such as positioning accuracy, power consumption, and signal quality. The positioning and path selection optimization module selects and adjusts the path based on this information to ensure the efficient operation of the system.

[0087] Specifically, the module uses the shortest path algorithm and dynamic routing protocol to optimize the data transmission path between the node and the base station. The goal of the shortest path algorithm is to select the path with the shortest communication distance between the node and the base station to reduce signal attenuation and communication delay, and ensure that the data can be transmitted to the base station as soon as possible. The dynamic routing protocol is used to automatically adjust the communication path when changes occur in the network (such as node movement, failure, etc.) to ensure smooth data transmission.

[0088] In the process of path selection, the block generally considers factors such as path loss, signal interference, and distance between nodes. For example, in areas with strong signals or low path loss, the system prefers shorter paths for communication; in environments with severe signal attenuation or multipath effects, the system will choose a path that bypasses the interference source to ensure accurate data transmission.

[0089] As an option, the path selection optimization module can use the Dijkstra algorithm or the A* algorithm for path calculation. These two algorithms can quickly find the optimal path in a complex network environment and take into account real-time signal strength and interference factors during the path selection process.

[0090] The path loss is calculated as follows:

[0091] in: is the path loss from the transmitting source to the receiver; The reference distance Signal loss when is the path loss index, which usually ranges from 2 to 5 depending on the environment; is the transmission distance; is the random attenuation caused by multipath effect, which conforms to Rayleigh distribution.

[0092] Specifically, the positioning and path selection optimization module needs to dynamically adjust the path selection according to the path loss in real-time transmission. For example, when the path loss between nodes is greater than the set threshold, the system will use the optimization algorithm to find a new path to ensure that the data can be transmitted to the base station accurately.

[0093] In one possible implementation, in order to further improve the stability and anti-interference ability of the system, the positioning and path selection optimization module combines the advantages of UWB positioning and LoRa communication. Based on the high-precision location data provided by the UWB positioning module, the path selection optimization module can accurately calculate the best path for each node. Taking advantage of the long distance and low power consumption of LoRa communication, nodes communicate by selecting the shortest path, avoiding unnecessary power consumption and signal interference.

[0094] In other embodiments, when the node position changes or communication is interrupted in the network, the system will dynamically adjust the path to ensure the reliability of the network. At this time, the module will use an adaptive routing algorithm to automatically select the optimal path according to the current network conditions to ensure the stable operation of the system in a complex environment.

[0095] As another option, the system can also optimize path selection through load balancing strategies. Load balancing strategies allow data flows to be distributed through different paths when the network load is high, thereby reducing the pressure on a single path and improving the overall network throughput and reliability.

[0096] Generally, the positioning and path selection optimization module will regularly update the path selection and adjustment strategy to adapt to changes in the environment. This adaptive mechanism ensures that the system always operates in the best state, especially in scenarios where nodes move frequently or the environment changes greatly.

[0097] In other possible implementations, the module can also work in conjunction with multiple base stations in the positioning system. Through the coordination between base stations, the system can optimize path selection based on the precise location of each node and reduce redundancy and signal interference in the path. In this way, the system can not only maintain high communication stability in a more complex environment, but also improve positioning accuracy.

[0098] In summary, the positioning and path selection optimization module plays a vital role in the present invention. It automatically selects and adjusts the communication path according to the real-time signal strength, path loss and multi-objective optimization results to ensure the efficient operation of the system in a limited space. Through the shortest path algorithm, dynamic routing protocol and load balancing strategy, the system can flexibly respond to various challenges in a complex environment and maintain the stability and efficiency of communication.

[0099] Optimization algorithm and adaptive adjustment module, configured to perform adaptive optimization according to environmental changes and real-time data to ensure continuous and stable operation of the system; In this embodiment, the optimization algorithm and adaptive adjustment module use a particle swarm optimization (PSO) algorithm or a quantum particle swarm optimization (QPSO) algorithm to perform a global search for multi-objective optimization. The particle swarm optimization algorithm is a widely used global optimization algorithm that simulates the behavior of a particle group in a search space to find the optimal solution. In the present invention, PSO is used to optimize the layout, power allocation, and path selection of sensor nodes.

[0100] Specifically, the working principle of the PSO algorithm is to represent each solution as a particle, and iteratively update the speed and position of the particle to gradually approach the global optimal solution. The position of each particle represents a possible optimization solution, and the speed of the particle determines how it moves in the search space. The PSO algorithm can perform global search in a high-dimensional complex space, so it is suitable for optimization problems involving multiple objectives and constraints in the present invention.

[0101] The update formula of PSO is as follows:

[0102]

[0103] in: For the The particle in The speed at the iteration; For the The position of a particle; For the The best historical position of a particle; is the global optimal position of the group; are learning factors, which control the particle's dependence on historical experience and global experience respectively; is a random number between [0,1].

[0104] By continuously updating the positions and velocities of particles, PSO can find the best compromise between multiple objectives, thereby optimizing node deployment, power allocation, and path selection.

[0105] As an option, the quantum particle swarm optimization (QPSO) algorithm can also be used in the process of achieving multi-objective optimization. QPSO is similar to PSO, but the difference is that QPSO introduces the superposition characteristics of quantum bits, allowing particles to search in parallel at multiple locations, thereby accelerating the convergence of the global optimal solution. QPSO can optimize the search process through quantum operations, avoid the local optimal problem that the particle swarm algorithm may encounter, and enhance the global search capability in complex environments.

[0106] The update formula of QPSO is:

[0107] in: For the The particle in The position at the iteration; It is the quantum rotation factor, which controls the particle's ability to jump; It is the global optimal solution.

[0108] In one possible implementation, the system dynamically updates the parameters in the optimization process through an adaptive adjustment mechanism. Specifically, in areas with high network load or dense nodes, the system may prioritize path selection and power control to improve network performance and positioning accuracy; in scenarios with high power requirements, the system will reduce energy consumption by adjusting data transmission frequency and power allocation.

[0109] Generally, the optimization algorithm and adaptive adjustment module will adjust the weight of the objective function according to real-time environmental changes. These environmental changes may include network congestion, node communication quality and transmission distance, and signal interference. Based on these changes, the optimization algorithm will flexibly adjust between multiple objectives such as power consumption, network coverage, path loss, positioning accuracy, etc. to ensure the overall performance of the system is maximized.

[0110] As another option, the optimization module can also combine the historical operation data of the network to perform predictive optimization on path selection and node layout. Through machine learning technology, the system can continuously learn and accumulate experience after the initial optimization, so as to automatically improve the optimization effect in the long-term operation process.

[0111] Specifically, in order to ensure the stable operation of the system in different environments, the optimization algorithm and adaptive adjustment module will regularly perform performance evaluations. The system adjusts the optimization strategy based on the evaluation results and dynamically adjusts the layout and power of the nodes. This adaptive mechanism ensures that during the operation of the system, whether the location of the node changes or the network load changes, the system can automatically adapt and provide the best network performance.

[0112] In other possible implementations, in order to improve the robustness and anti-interference ability of the system, the optimization module can also adjust the key parameters in the algorithm based on real-time feedback. For example, when some nodes encounter severe interference or failure, the system can automatically adjust the power allocation and path selection of these nodes to ensure the normal operation of other parts of the system.

[0113] In summary, the optimization algorithm and adaptive adjustment module, by combining the PSO or QPSO algorithm, ensure the efficiency and stability of the system in limited space monitoring under the framework of multi-objective optimization. This module can flexibly adjust the optimization strategy according to environmental changes, which not only improves the performance of the system, but also ensures its adaptability in different application scenarios. Through the dynamic adjustment of this module, the system can self-optimize and run stably for a long time, providing solid technical support for monitoring tasks in mines, underground spaces and other complex environments.

[0114] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Limited space monitoring system based on LoRa communication and UWB positioning, characterized in that: include: Multiple sensor nodes configured to collect environmental data and transmit data via LoRa communication modules; at least one base station configured to receive environmental data from a plurality of sensor nodes and locate position data through a UWB positioning module; a signal propagation model module configured to simulate signal attenuation, path loss, and multipath effects between sensor nodes and a base station; A multi-objective optimization module configured to perform multi-objective optimization of network stability, power consumption, signal coverage and interference based on node location, power allocation and path selection; A node deployment and power control module, configured to dynamically adjust the location and power configuration of the sensor nodes according to the optimization results; Data transmission and communication module, configured to realize the transmission and processing of LoRa communication and UWB positioning data; A positioning and path selection optimization module is configured to optimize the data transmission path between the sensor node and the base station to ensure positioning accuracy and communication stability; The optimization algorithm and adaptive adjustment module are configured to perform adaptive optimization according to environmental changes and real-time data to ensure continuous and stable operation of the system.

2. The limited space monitoring system based on LoRa communication and UWB positioning according to claim 1, characterized in that: The multi-objective optimization module further comprises: An optimization unit for minimizing system power consumption, which manages power consumption according to the transmission distance, power requirements and energy consumption of each sensor node; An optimization unit for maximizing network coverage, which ensures that each node can establish effective communication with at least one base station by optimizing the layout of nodes and signal propagation paths; An optimization unit for minimizing signal interference and path loss, which adjusts the communication links between nodes through shortest path selection and interference minimization strategy.

3. The limited space monitoring system based on LoRa communication and UWB positioning according to claim 1, characterized in that: The signal propagation model module uses the Rayleigh attenuation model or the Rician attenuation model to model the signal path loss and dynamically adjusts the propagation parameters according to the signal propagation environment.

4. The limited space monitoring system based on LoRa communication and UWB positioning according to claim 1, characterized in that: The signal propagation model module calculates signal attenuation through random field theory, dynamically updates the signal strength of each node, and provides accurate prediction of signal attenuation and path loss.

5. The limited space monitoring system based on LoRa communication and UWB positioning according to claim 1, characterized in that: The node deployment and power control module adjusts the power of the sensor nodes according to the optimization algorithm, so that the power allocation of the nodes meets the required network coverage requirements and communication quality, while reducing unnecessary energy consumption.

6. The limited space monitoring system based on LoRa communication and UWB positioning according to claim 1, characterized in that: The data transmission and communication module comprises: LoRa communication module, used for long-distance, low-power data transmission between sensor nodes and base stations; The UWB positioning module is used to accurately locate the sensor nodes and transmit the positioning data to the base station.

7. The limited space monitoring system based on LoRa communication and UWB positioning according to claim 1, characterized in that: The optimization algorithm and adaptive adjustment module optimize the sensor nodes, path selection and power allocation in the network based on the particle swarm optimization algorithm or the quantum particle swarm optimization algorithm to ensure that the system maintains the best performance in a dynamically changing environment.

8. The limited space monitoring system based on LoRa communication and UWB positioning according to claim 1, characterized in that: The multi-objective optimization module further dynamically adjusts the data transmission rate and transmission frequency according to the real-time load and channel conditions of the network to improve the efficiency of data transmission and reduce network congestion.

9. The limited space monitoring system based on LoRa communication and UWB positioning according to claim 1, characterized in that: The node deployment and power control module uses an adaptive adjustment mechanism to automatically adjust the location and power of the node when the communication quality of the sensor node is poor or encounters signal interference, thereby ensuring the stability and communication quality of the network.

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