Combustible gas multipoint on-line monitoring system based on ZigBee

Abstract

The invention relates to the technical field of wireless sensing and industrial safety monitoring, in particular to a ZigBee-based combustible gas multipoint online monitoring system. The system comprises a data acquisition module, a neighborhood interaction module, a gradient arbitration module, a transmission control module and a collaborative coding module. The system calculates parameter gradient characteristics by comparing neighbor node data; the core of the method is that a channel strategy is dynamically adjusted according to gradient extremum attributes, a local maximum node is endowed with a high priority, and a non-extremum node is forced to delay transmission and perform differential compression; according to the method, a gradient-based flow shaping mechanism is utilized, the channel avalanche effect is effectively restrained, and the problem of communication paralysis caused by large-scale concurrent alarm is solved.

Description

Technical Field

[0001] This invention relates to the field of wireless sensor networks and industrial safety monitoring technology, specifically to a ZigBee-based multi-point online monitoring system for combustible gases. Background Technology

[0002] In the current field of industrial safety monitoring, ZigBee-based wireless sensor networks are widely used for combustible gas monitoring due to their low power consumption and self-organizing network characteristics. However, in practical applications, when a combustible gas leak occurs, multiple sensor nodes located in the diffusion area often detect that the concentration exceeds the standard at the same time and attempt to send an alarm transmission to the central coordinator at almost the same moment. Such sudden large-scale concurrent communication can cause instantaneous overload of the wireless channel, triggering severe broadcast storms or channel avalanche effects. Because the ZigBee protocol uses the CSMA / CA mechanism, the simultaneous competition for the channel by a large number of nodes can lead to a surge in the probability of collisions, causing data packets containing critical alarm information to be lost or experience significant delays due to repeated collisions. Existing random backoff mechanisms are unable to distinguish the urgency of data, resulting in high-risk nodes in high-concentration core areas not being able to obtain priority transmission rights. Therefore, how to ensure the real-time transmission of critical alarm data and effectively suppress redundant traffic in high-concurrency scenarios has become an urgent technical problem to be solved. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a ZigBee-based multi-point online monitoring system for combustible gases. Specifically, the technical solution of this invention includes: The data acquisition module collects physical field parameters from sensor nodes distributed within the monitoring area, obtaining the node status data of the current monitoring node, which includes real-time values ​​of the physical field parameters. The neighborhood interaction module establishes local interaction links between the sensor node and neighboring nodes within a preset communication range, controlling the sensor node to broadcast short-frame signals containing node status data to neighboring nodes and receiving neighboring status data from neighboring nodes. The gradient arbitration module calculates the parameter gradient characteristics of the current monitoring location based on the node status data and neighboring status data, comparing the node status data value with the values ​​of all received neighboring status data to determine the sensor node's position within the local space. The gradient extremum attribute includes local maximum and non-local maximum states. The transmission control module dynamically generates channel access strategies based on the gradient extremum attribute. When a sensor node is determined to be in a local maximum state, it assigns a high-priority channel contention weight and generates an immediate transmission command. When a sensor node is determined to be in a non-local maximum state, it generates a suppress transmission command or a delay transmission command to limit the sensor node's occupation of the communication channel. The cooperative coding module, after generating the delay transmission command, performs differential processing on the node state data, calculates the gradient vector of the node state data relative to neighboring nodes in local maximum states, and generates compressed differential data packets.

[0004] Preferably, the physical field parameters acquired by the data acquisition module are combustible gas concentration data, the monitoring area is an industrial plant environment, and the sensor node is a wireless sensing device based on the ZigBee protocol; the data acquisition module is also used to preprocess the combustible gas concentration data, filter out environmental noise fluctuations, and ensure that the continuously sampled node status data reflects the continuous characteristics of gas diffusion.

[0005] Preferably, the transmission control module includes: a reverse backoff unit, used to calculate the backoff time for the sensor node to access the wireless channel, and establish an inverse mapping relationship between the backoff time and the value of the node state data. When the value of the node state data is larger, the calculated backoff time is shorter, thereby giving the sensor node in the high-concentration area a higher probability of preempting the channel; and a state locking unit, used to send a spatial correlation suppression lock signal to the neighboring node in response to the determination result of the local maximum state. The spatial correlation suppression lock signal is used to force the neighboring node that receives the signal and is in the non-local maximum state to enter the passive aggregation mode.

[0006] Preferably, the gradient arbitration module executes the following logic: extracting the self-concentration value from the node state data and extracting the set of neighbor concentration values ​​from the neighbor state data; determining whether the self-concentration value is greater than the maximum value in the set of neighbor concentration values; if the determination result is yes, then determining that the sensor node is a local diffusion core and marking the gradient extreme value attribute as the local maximum state; if the determination result is no, then determining that the sensor node is a local diffusion edge and marking the gradient extreme value attribute as the non-local maximum state.

[0007] Preferably, the operation of the state locking unit further includes: after the sensor node enters the passive aggregation mode, stopping the independent alarm transmission process; monitoring the communication time slots of neighboring nodes in the local maximum state; attaching the node status data or the differential data packet of the sensor node to the transmission frame of the neighboring node in the local maximum state, and sending it to the central coordinator through data aggregation.

[0008] Preferably, the system further includes: a source localization module, deployed at the central coordinator, for receiving data packets from each sensor node; a topology reconstruction unit, for reverse deducing the spatial distribution topology of the physical field parameters based on the received location information of the sensor nodes in the local maximum state and the gradient vector in the differential data packet; and a path backtracking unit, for constructing a dynamic trajectory of gas diffusion and locating the starting source coordinates of the physical field anomaly based on the received timestamp sequence of high-priority alarm nodes when some node data is lost due to channel congestion.

[0009] Preferably, the inverse mapping relationship established in the reverse backoff unit follows a preset nonlinear weighting function. The nonlinear weighting function uses the power of the node status data value as the denominator to ensure that when the value is close to the preset alarm threshold, the reduction of the backoff time changes exponentially, thereby achieving millisecond-level channel preemption under sudden high-concentration conditions.

[0010] Preferably, the neighborhood interaction module further includes: a silent monitoring unit, used to, after the sensor node detects that the node status data exceeds a preset safety threshold, not immediately send an alarm to the central coordinator, but instead initiate a competition window of a preset length; during the competition window, the sensor node only performs low-power broadcasting and monitoring operations to the neighboring nodes to complete the determination of the gradient extreme value attribute, and the duration of the competition window is less than the maximum alarm response delay time specified by the system.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. This system implements a flow shaping mechanism based on the local physical field gradient through a gradient arbitration module. By comparing the node state data with the neighbor state data, it accurately determines the gradient extreme value attribute of the sensor node in the local space. This mechanism assigns the highest channel priority to the node in the local maximum state, while forcing neighbor nodes in the non-local maximum state to generate suppression or delay transmission commands. This design utilizes the spatial continuity characteristics of gas diffusion to effectively curb the channel avalanche effect caused by large-scale concurrent alarms in traditional monitoring networks, reduce the transmission of redundant data from the source, and solve the problem of processing paralysis caused by the surge in data volume when facing large-area anomalies in existing technologies. 2. This system introduces a reverse backoff unit in the transmission control module, establishing an inverse mapping relationship between backoff time and the magnitude of node status data. Through a nonlinear weighting function, sensor nodes in high-concentration areas can obtain extremely short backoff times, thereby seizing channel resources within milliseconds. This technique breaks through the limitations of traditional random backoff mechanisms, ensuring that the most dangerous alarm nodes can jump the queue and send first, significantly improving the system's real-time response speed to sudden high-concentration emergencies, and effectively solving the technical problem of delayed fault detection caused by lengthy processes in existing batch processing architectures. 3. This system utilizes the cooperation of the cooperative coding module and the state locking unit, and adopts a gradient-based differential compression and passive aggregation transmission strategy. For nodes in non-local maximum states, the system does not directly send the original data, but instead calculates its gradient vector relative to the local maximum node and generates compressed differential data packets, or attaches them to the transmission frames of high-priority nodes. This processing method significantly reduces the effective payload length of data packets and the overhead of air interface headers, and achieves significant compression of network traffic while ensuring the accuracy of data description, thereby improving the effective throughput of the channel. 4. This system combines the source localization module with the topology reconstruction unit, introducing a gradient-based backpropagation algorithm on the server side. Even when some node data is lost due to channel congestion, the system can still reconstruct the spatial distribution topology of physical field parameters and construct the dynamic trajectory of gas diffusion using the inverse distance weighting algorithm based on the location information of the received high-priority alarm nodes and the gradient vector in the differential data packets. This enables the system to accurately trace back and lock the coordinates of the anomaly's origin even in low-bandwidth or packet-loss environments, making up for the shortcomings of traditional solutions that rely heavily on complete data, leading to difficulties in localization. Attached Figure Description

[0012] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0014] Example 1: Please see Figure 1 A ZigBee-based multi-point online monitoring system for combustible gases includes: a data acquisition module for acquiring physical field parameters through sensor nodes distributed within the monitoring area, obtaining node status data of the current monitoring node, the node status data including real-time values ​​of physical field parameters; a neighborhood interaction module for establishing local interaction links between sensor nodes and neighboring nodes within a preset communication range, controlling sensor nodes to broadcast short frame signals containing node status data to neighboring nodes, and receiving neighboring status data fed back by neighboring nodes; and a gradient arbitration module for calculating the parameter gradient characteristics of the current monitoring location based on the node status data and neighboring status data, and comparing the value of the node status data with the values ​​of all received neighboring status data one by one. The system determines the gradient extremum attribute of sensor nodes in the local space, including local maximum and non-local maximum states. A transmission control module dynamically generates channel access strategies based on the gradient extremum attribute. When a sensor node is determined to be in a local maximum state, it is assigned a high-priority channel contention weight and an immediate transmission command is generated. When a sensor node is determined to be in a non-local maximum state, a suppress transmission command or a delay transmission command is generated to limit the sensor node's occupation of the communication channel. A cooperative coding module, after generating the delay transmission command, performs differential processing on the node state data, calculates the gradient vector of the node state data relative to neighboring nodes in local maximum states, and generates compressed differential data packets.

[0015] This embodiment details the core architecture and operation process of the system, aiming to solve the broadcast storm problem caused by a large number of nodes simultaneously alarming when traditional wireless sensor networks face large-area gas leaks. The data acquisition module is implemented as a microcontroller and its peripheral circuitry integrated on the ZigBee terminal node. It periodically collects physical field parameters through sensor nodes distributed in the monitoring area and generates node status data locally on the node. This data structure includes real-time values ​​of physical field parameters, timestamps, node ID, and spatial coordinate information preset by the node or obtained through positioning algorithms, which serve as the basic input for subsequent gradient calculations. The acquisition frequency is set to meet the requirements of Shannon's sampling theorem. Within a preset time window after a node is woken up, the neighborhood interaction module establishes a local interaction link based on the MAC layer broadcast mechanism of the ZigBee protocol. This module controls the sensor node to broadcast short frame signals containing core values ​​of node status data to surrounding neighboring nodes within a single-hop range. This signal adopts a custom frame format of the IEEE 802.15.4 MAC layer, and the specific frame structure is defined as follows: 2-byte frame control field, 1-byte sequence number, 2-byte source PANID, 2-byte source short address, and 4-byte payload. The payload directly carries a 32-bit floating-point concentration value and a 2-byte frame check sequence. All network layer and application layer packet headers are removed, and the total frame length is compressed to 13 bytes. While maintaining physical layer compatibility, the long application layer header and redundant protocol overhead in traditional data packets are removed. At the same time, it receives and parses neighbor status data fed back from neighboring nodes within the communication range, thereby constructing a local neighborhood concentration view locally. It is worth noting that before the gradient arbitration module executes its logical judgments, the system must strictly adhere to a silent listening process; that is, after data collection is completed, the system does not directly enter the arbitration phase, but instead forcibly initiates a period of [duration missing]. The contention window period; the initiation of the contention window period strictly follows the network-wide beacon synchronization mechanism: the central coordinator periodically sends beacon frames containing network timestamps, and all sensor nodes calibrate their local RTC clocks after receiving the beacon frames, and synchronize at a preset global synchronization time. A unified triggering window for data collection and subsequent listening ensures that the timelines of all neighboring nodes are aligned. Within this window, nodes only broadcast their own state and collect neighbor information, and are strictly prohibited from initiating long frame transmissions to the coordinator. This timing arrangement ensures that the set of neighboring state data upon which gradient calculation depends is complete and synchronized, avoiding false maxima misjudgments caused by some nodes preemptively starting. The gradient arbitration module executes distributed extreme value determination logic. After acquiring its own node state data and the state data of all surrounding neighbors, it calculates the parameter gradient characteristics of the current monitoring location. This step does not rely on complex differential calculations but instead determines the gradient extreme value attributes of the sensor node within a local, small spatial range through numerical comparison, distinguishing between local maxima states at the center of local leakage and non-local maxima states at the diffusion edge. Based on this, the transmission control module executes differentiated channel access strategies according to the above determination results. In response to determining that a sensor node is in a local maximum state, this module identifies the data of that node as having the highest descriptive value. It assigns a high-priority channel contention weight, but does not execute an immediate transmission command. Instead, it generates an aggregation window opening command. This command drives the radio frequency unit to send a short-frame spatial correlation suppression lock signal, then switches to receive mode and starts an aggregation waiting window of a preset duration. Within this window, it receives and buffers differential data packets uploaded by neighboring nodes. After the window closes, it packages its own data with the buffered data and then drives the radio frequency unit to preempt air interface resources at maximum power and send them to the central coordinator. Conversely, in response to the determination that it is in a non-local maximum state, this module generates a suppress transmission command or a delay transmission command to actively limit the sensor node's occupation of the public communication channel. The collaborative coding module processes the delayed data by performing differential processing on the node state data. Specifically, it calculates the direction vector and Euclidean distance based on the spatial coordinates of the current node and its neighbors, combines the concentration difference, and calculates the directional gradient vector relative to the local maximum node based on the principle of discrete spatial difference. Set the current node The coordinates are The concentration is Neighboring nodes in a local maximum state The coordinates are The concentration is Considering that a single direction cannot fully represent the entire gradient, this embodiment selects the direction of the steepest concentration decrease as the principal gradient direction, and its calculation formula adopts the following directional gradient calculation model based on Euclidean distance normalization:

[0016] in, This represents the Euclidean distance between nodes; the formula, by dividing the concentration difference by the square of the distance and then multiplying by the displacement vector, effectively achieves... The gradient vectorization operation, where, Using a unit vector as the direction, weighted inversely proportional to the square of the distance, the scalar concentration field is transformed into a directional vector field, ensuring that the differential data packet can represent the direction of the fastest concentration decrease; when there are multiple neighboring nodes, the least squares method can also be used for multiple... The components are weighted and synthesized to obtain a more accurate two-dimensional full gradient estimate. ; The system performs fixed-point compression on the calculated gradient components, quantizing them into 8-bit signed integers and generating compressed differential data packets. The low bit width of the differential data significantly reduces the effective payload length. This embodiment introduces an arbitration mechanism based on local physical field gradients at the network edge, utilizing the spatial continuity of gas diffusion to achieve content-based network traffic shaping. Nodes in local maxima are given the highest say, while neighboring nodes in non-local maxima automatically degenerate into auxiliary roles. This mechanism makes the network's ability to organize the channel more orderly even as the leakage range expands and the number of triggered nodes increases. This solves the problem of network paralysis caused by large-scale concurrent alarms and ensures that critical alarm information is delivered within milliseconds.

[0017] Example 2: The data acquisition module collects physical field parameters, namely combustible gas concentration data, and monitors the industrial plant environment. The sensor nodes are wireless sensing devices based on the ZigBee protocol. The data acquisition module is also used to preprocess the combustible gas concentration data, filter out environmental noise fluctuations, and ensure that the continuously sampled node status data reflects the continuous characteristics of gas diffusion.

[0018] This embodiment specifically defines the application scenarios and preprocessing logic of the data acquisition module. The data acquisition module explicitly limits the acquisition object to combustible gas concentration data, such as methane or hydrogen concentration, in units of LEL% or PPM, suitable for industrial plant environments with electromagnetic interference and multipath effects. The sensor node hardware uses a low-power system-on-a-chip conforming to the IEEE 802.15.4 standard, such as the CC2530 or EFR32 series chips. Considering the noise characteristics of industrial environments, the data acquisition module integrates preprocessing logic, employing an exponentially weighted moving average algorithm to smooth the raw signal, aiming to balance noise filtering with signal change response speed. Its discrete-time recursive formula is:

[0019] in, Indicates the current The output value after filtering at any given time; This represents the raw concentration value collected by the sensor at the current moment; This is the filtered value from the previous time step; In this embodiment, the preset smoothing weighting coefficient is set as follows: This causes the filtering result to be mainly affected by the proximity. The influence of each sampling point is addressed by digitally filtering the original signal. This process aims to filter out environmental noise fluctuations, such as instantaneous airflow disturbances or sensor temperature drift, ensuring that the continuously sampled node status data reflects the continuous characteristics of gas diffusion rather than random noise. This embodiment avoids misjudging local maxima caused by sensor signal glitches by performing targeted noise filtering at the source, ensuring the reliability of the subsequent gradient arbitration module input, thereby significantly improving the overall alarm accuracy of the system, reducing the waste of valuable channel resources caused by false alarms, and enabling the system to maintain robust monitoring performance even in harsh industrial environments.

[0020] Example 3: The transmission control module includes: a reverse backoff unit, used to calculate the backoff time for sensor nodes to access the wireless channel, and establish an inverse mapping relationship between the backoff time and the value of the node state data. When the value of the node state data is larger, the calculated backoff time is shorter, thereby giving sensor nodes in high-concentration areas a higher probability of preempting the channel; and a state locking unit, used to send a spatial correlation suppression lock signal to neighboring nodes in response to the determination result of the local maximum state. The spatial correlation suppression lock signal is used to force neighboring nodes that receive the signal and are in a non-local maximum state to enter the passive aggregation mode.

[0021] The inverse mapping relationship established in the reverse backoff unit follows a preset nonlinear weighting function. The nonlinear weighting function uses the power of the node status data value as the denominator to ensure that when the value is close to the preset alarm threshold, the reduction of the backoff time changes exponentially, thereby achieving millisecond-level channel preemption under sudden high concentration conditions.

[0022] This embodiment further refines the channel contention mechanism in the transmission control module, especially the priority guarantee for high-density nodes; the reverse backoff unit breaks the CSMA / CA random backoff mechanism of the ZigBee standard and establishes an inverse mapping relationship between backoff time and node state data values; in order to implement the nonlinear weighting function and ensure the consistency of dimensions, this unit uses a nonlinear attenuation model with lower limit constraints to determine the backoff time before accessing the channel. :

[0023]

[0024] in, These are theoretically calculated values. The competition backoff time for final execution; The baseline retreat coefficient, whose physical dimension is defined as concentration unit. Used to offset the denominator The influence of dimensions, to ensure The calculation result is a pure time scalar; among them, in order to implement exponential operations on a low-power MCU. The system pre-creates a lookup table based on piecewise linear interpolation in the Flash storage area; this lookup table stores concentration values. This is mapped to the corresponding dimensionless attenuation factor. Simultaneously, the symbols for physical quantities are standardized throughout the text: here... This is the filtered output value in Example 2. This ensures that the data used in the calculations is smooth and reliable. The current node concentration value, in this embodiment, Specifically refers to the numerical portion of combustible gas concentration in the node status data; To prevent zero bias; The urgency index for issuing an alarm; The unit time slot length; The minimum frame interval time allowed by the hardware physical layer is introduced to prevent the calculation result from approaching 0 or causing numerical overflow under high concentration conditions, which would lead to abnormal RF chip driving. The reverse backoff unit calculates the required number of backoff time slots using the above formula, and then multiplies it by the duration of a single time slot to obtain the total time. This causes the denominator to increase exponentially when the concentration rises sharply, resulting in a sharp decrease in the calculated number of backoff time slots, which then approaches 0. After a node successfully preempts the channel, the state-locking unit consolidates local order. Responding to the local maximum state determination and successfully transmitting data, it immediately broadcasts a spatial correlation suppression lock signal to neighboring nodes. This signal sets the 7th bit of the 7-9 reserved bits in the frame control field of the MAC frame header to... As the unique hardware identifier for the high-priority suppression lock, in its specific implementation, the transmission control module does not send data through the standard ZigBee application layer API, but instead calls the underlying radio frequency driver interface to directly access the baseband processor's transmit buffer; by setting the 7th bit mask of the address where the frame control field is located through direct memory access, it bypasses the protocol stack's legality check on reserved bits and achieves fast signaling identification at the physical layer. This signal contains the sender's ID and the current concentration value, forcing any neighboring node that receives the signal and has a lower concentration to switch from contention mode to passive aggregation mode. This embodiment introduces a nonlinear reverse backoff mechanism based on power functions to achieve millisecond-level channel preemption capability under sudden high-concentration conditions, ensuring that the most dangerous node can jump the queue and send first. In conjunction with the state locking unit, this mechanism quickly selects the node in the local maximum state and suppresses the disorderly competition of surrounding nodes, effectively preventing the channel avalanche effect in the scenario of multiple points leaking at the same time, and constructing an adaptive communication order based on the danger level.

[0025] Example 4: The gradient arbitration module executes the following logic: extract the self-concentration value from the node state data and extract the set of neighbor concentration values ​​from the neighbor state data; determine whether the self-concentration value is greater than the maximum value in the set of neighbor concentration values; if the determination result is yes, then the sensor node is determined to be a local diffusion core, and the gradient extremum attribute is marked as a local maximum state; if the determination result is no, then the sensor node is determined to be a local diffusion edge, and the gradient extremum attribute is marked as a non-local maximum state.

[0026] This embodiment details the internal execution logic of the gradient arbitration module, which forms the basis for node self-decision. The module performs data extraction, retrieving its own concentration value from its registers and extracting a set of neighboring concentration values ​​from a list maintained by the neighborhood interaction module. The module then executes extreme value judgment logic, setting the judgment condition as whether its own concentration value is greater than the maximum value in the neighboring concentration value set. This step is computationally lightweight and suitable for resource-constrained ZigBee nodes. Based on the judgment result, attribute marking is performed. In response to a yes judgment result, the system infers that the node is located at the center of the air mass, determining the sensor node as a local diffusion core, and marking the internal gradient extreme value attribute as a local maximum state. Conversely, in response to a no judgment result, the system infers that the node is located on the outer diffusion path of the air mass, determining the sensor node as a local diffusion edge, and marking the gradient extreme value attribute as a non-local maximum state. This embodiment employs lightweight logic based on local comparisons, avoiding complex global routing table maintenance or heavy CPU computation, enabling low-cost sensor nodes to possess spatial self-awareness capabilities. This design can quickly and adaptively identify its relative role in the gas diffusion field without GPS positioning or complex network topology knowledge, laying a solid logical foundation for subsequent differentiated transmission and realizing intelligent decision-making at the edge.

[0027] Example 5: The operation of the state locking unit also includes: stopping the independent alarm transmission process after the sensor node enters the passive aggregation mode; monitoring the communication time slots of neighboring nodes in the local maximum state; and attaching the node status data or differential data packet of the sensor node to the transmission frame of the neighboring node in the local maximum state and sending it to the central coordinator through data aggregation.

[0028] This embodiment, combined with the state locking mechanism, further elaborates on the specific operation of the state locking unit in passive aggregation mode. When a node receives a spatial correlation suppression lock signal and enters passive aggregation mode, it immediately stops independently initiating alarm transmission processes. This means that the node suspends its own CSMA / CA contention algorithm and abandons sending data packets to the central coordinator as an independent source node. The node starts listening mode and locks the communication timing of its neighboring node, which is in a local maximum state and is sending the suppression lock signal. The node uses the data frame load margin in the ZigBee protocol or a specific aggregation frame format to send its node state data or differentially processed data packets to the local maximum node through a contention-free short time slot method after detecting the spatial correlation suppression lock signal broadcast by the neighboring node. Specifically, the aggregation waiting window is divided into... In each micro-slot, each node in passive aggregation mode calculates its corresponding transmission slot index using the low-order hash value of its MAC address. To resolve channel conflicts caused by hash collisions, the system introduces a linear probing mechanism: Let the total number of slots in the aggregation window be... The initial time slot index calculation formula is as follows: If a node detects that the time slot is already occupied, it will automatically execute... The delay strategy continues until an idle micro-slot is found; if the delay continues... If there is still no free space, the packet is dropped to protect the main channel; Time-division multiplexing is performed sequentially without collisions; at this time, local maxima nodes act as temporary cluster heads, temporarily storing the received data in the local buffer, rather than directly modifying the transmission frame in the air; The local maximum node serves as a temporary local aggregation point. It packages its own high-priority data and the mounted data of its neighbors through data aggregation and sends them to the central coordinator at once. In this embodiment, the alarm data of multiple physical nodes are merged into a single physical layer data packet for transmission through this hitch-and-run transmission mechanism. This not only significantly reduces the number of data packets on the air interface and lowers the probability of collisions, but also significantly improves the effective throughput of the channel by reducing packet header overhead. This allows the host to obtain a complete diffusion slice of a region at once, achieving a dual optimization of communication efficiency and data density.

[0029] Example 6: The system also includes: a source localization module, deployed at the central coordinator, for receiving data packets from various sensor nodes; a topology reconstruction unit, for inversely deducing the spatial distribution topology of physical field parameters based on the received location information of sensor nodes in a local maximum state and the gradient vector in the differential data packets; and a path backtracking unit, for constructing a dynamic trajectory of gas diffusion and locating the starting source coordinates of physical field anomalies based on the received timestamp sequence of high-priority alarm nodes when some node data is lost due to channel congestion.

[0030] This embodiment describes the data post-processing logic located at the network aggregation end, namely the implementation of the source localization module. This module is deployed at the central coordinator and receives data packets from various sensor nodes. Due to the suppression and differential strategies employed at the front end, data reconstruction is required. The topology reconstruction unit determines the peak points of the physical field based on the received absolute position information and absolute concentration values ​​of sensor nodes in local maxima states, and uses the gradient vector in the differential data packets transmitted along the path to reconstruct the physical field using an improved gradient-based inverse distance weighting algorithm. Specifically, the location of any unmonitored point... Parameter estimates at [location] Calculate using the following formula:

[0031] in, The number of neighbor anchor points involved in the calculation; parameters Defined as the first The combustible gas concentration value of each anchor point after pretreatment as described in Example 2 ,Right now , This is the corresponding gradient vector; These are the spatial coordinates of the anchor point; The weighting function is defined as follows: That is, the inverse square of the distance; by introducing a gradient term The system can accurately recover the non-flat concentration field distribution under sparse sampling and reverse-engineer the spatial distribution topology of physical field parameters. The path backtracking unit addresses the issue of some node data being lost due to channel congestion in extreme cases. Based on the timestamp sequence of successfully received high-priority alarm nodes, it constructs a least-squares regression model based on the physical laws of gas diffusion, using the assumption of linear drift. Before establishing the regression model, the source location module performs the DBSCAN density clustering algorithm on the coordinates of all received alarm nodes. If the clustering results show multiple spatially separated high-concentration clusters, it is determined to be a multi-point simultaneous leak. The system will then execute subsequent path backtracking logic for each independent cluster, thereby achieving multi-source separation. High concentration center coordinate sequence at each time point The trajectory error objective function is established as follows:

[0032] in, This is the diffusion start time calculated by the system based on the timestamp of the first alarm node. Let $\mathbf{a}$ be the overall drift velocity vector of the gas cloud caused by the influence of the ambient wind field; by applying the above objective function... Solve for the source coordinates and drift speed The partial derivatives of the source coordinates are set to zero. To ensure convergence of the iterative algorithm, the solver uses the following heuristic to set the initial values: [The initial values ​​of the source coordinates are then set to zero]. Set as the coordinates of the node with the highest concentration at the current time. The initial value of the drift speed The initial value is set as the average wind speed vector or zero vector within the region. Iteration is performed based on this initial value to avoid the algorithm getting trapped in local minima. The Gauss-Newton iteration method or the Levenberg-Marquardt algorithm is used to numerically iterate and solve the nonlinear least squares problem to calculate the optimal source location estimate. The coordinates of the highest concentration at each time point are connected to construct the dynamic trajectory of gas diffusion. By extending this trajectory backward along the time axis, the algorithm calculates the theoretical coordinates of the moment when diffusion begins, thereby locating the initial source coordinates of the physical field anomaly. This embodiment mitigates the potential risk of sacrificing data fineness for communication efficiency at the front end. By introducing a gradient-based reconstruction algorithm on the server side, the system can still reconstruct the combustible gas concentration distribution cloud map at the scene even when the data is incomplete. This reverse engineering capability ensures that the leak source can still be accurately located under low-bandwidth communication conditions, achieving a perfect balance between low-overhead communication and high-precision monitoring.

[0033] Example 7: The neighborhood interaction module also includes a silent listening unit, which, when a sensor node detects that the node status data exceeds a preset safety threshold, does not immediately send an alarm to the central coordinator, but instead initiates a competition window of a preset length. During the competition window, the sensor node only performs low-power broadcasting and listening operations to neighboring nodes to complete the determination of gradient extreme value attributes. The duration of the competition window is less than the maximum alarm response delay time specified by the system.

[0034] This embodiment is a concretization of the timing logic of the neighborhood interaction module, focusing on resolving the conflict risk caused by immediate alarms. The silent monitoring unit implements a counterintuitive control strategy. In response to a sensor node detecting a danger by exceeding a preset safety threshold, the unit does not immediately send an alarm to the central coordinator. Instead, it immediately triggers a contention window of a preset length. During this period, the operation of the sensor node is strictly restricted. It is prohibited to send long frames to the next hop routing node or coordinator and only performs low-power broadcasting and monitoring operations to neighboring nodes. The system uses this window to calculate and determine the gradient extreme value attribute. To ensure the timeliness of the alarm, the duration of the contention window is set to be strictly less than the maximum alarm response delay time specified by the system, for example, 200 milliseconds under a 30-second alarm threshold. Only after the window period ends and the judgment is completed will the sending or suppressing operation be performed based on the judgment result. This embodiment effectively staggers physically simultaneous events on the time axis and logical axis by forcibly introducing a silent listening window. This not only provides the necessary time window for gradient arbitration and eliminates the physical basis for synchronous concurrency, but also enables the network to remain calm and orderly in the face of sudden disasters, greatly enhancing the robustness and anti-interference ability of the system.

[0035] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A ZigBee-based combustible gas multi-point online monitoring system, characterized in that, The application relates to a physical field monitoring method and device. The data acquisition module is used for collecting physical field parameters through sensor nodes distributed in a monitoring area, obtaining node state data of a current monitoring node, and containing real-time values of the physical field parameters; the neighborhood interaction module is used for establishing a local interaction link between the sensor node and neighbor nodes within a preset communication range, controlling the sensor node to broadcast a short frame signal containing the node state data to the neighbor nodes, and receiving neighbor state data fed back by the neighbor nodes; The gradient arbitration module is used for calculating parameter gradient characteristics of the current monitoring position according to the node state data and the neighbor state data, comparing the value of the node state data with the values of all received neighbor state data one by one, judging gradient extreme value attributes of the sensor node in the local space, and the gradient extreme value attributes include a local maximum value state and a non-local maximum value state; the transmission control module is used for dynamically generating a channel access strategy according to the gradient extreme value attributes, giving the sensor node a high priority channel competition weight value when the sensor node is judged to be in the local maximum value state, and generating an immediate sending instruction; when the sensor node is judged to be in the non-local maximum value state, a sending inhibition instruction or a delay sending instruction is generated to limit the occupation of the communication channel by the sensor node; and the cooperative coding module is used for performing differential processing on the node state data after the delay sending instruction is generated, calculating a gradient vector of the node state data relative to the neighbor nodes in the local maximum value state, and generating compressed differential data packets.

2. The ZigBee-based combustible gas multi-point online monitoring system according to claim 1, characterized in that, The physical field parameters collected by the data acquisition module are combustible gas concentration data, the monitoring area is an industrial plant environment, and the sensor node is a wireless sensing device based on a ZigBee protocol; the data acquisition module is also used for preprocessing the combustible gas concentration data, filtering out environmental noise fluctuations, and ensuring that the node state data sampled continuously reflects the continuity characteristics of gas diffusion.

3. The ZigBee-based combustible gas multi-point online monitoring system according to claim 1, characterized in that, The transmission control module comprises a reverse backoff unit used for calculating a backoff time of the sensor node accessing a wireless channel, establishing a reverse mapping relationship between the backoff time and the value of the node state data, and making the backoff time calculated to be shorter when the value of the node state data is larger, so that the sensor node in a high concentration area obtains a probability of preferentially occupying a channel; and a state locking unit used for sending a space correlation inhibition lock signal to the neighbor nodes in response to the judgment result of the local maximum value state, and the space correlation inhibition lock signal is used for forcing the neighbor nodes receiving the signal and being in the non-local maximum value state to enter a passive aggregation mode.

4. The ZigBee-based combustible gas multi-point online monitoring system according to claim 1, characterized in that, The gradient arbitration module performs the following logic: extracting the self-concentration value in the node state data, and extracting a set of neighbor concentration values in the neighbor state data; judging whether the self-concentration value is greater than the maximum value in the set of neighbor concentration values; if the result of the judgment is yes, determining that the sensor node is a local diffusion core, and marking the gradient extremum attribute as the local maximum value state; if the result of the judgment is no, determining that the sensor node is a local diffusion edge, and marking the gradient extremum attribute as the non-local maximum value state.

5. The ZigBee-based combustible gas multi-point online monitoring system according to claim 3, characterized in that, The operation of the state locking unit further includes: after the sensor node enters the passive aggregation mode, stopping independently initiating an alarm transmission process; listening to a communication time slot of a neighbor node in the local maximum value state; and mounting the node state data or the differential data packet of the sensor node into a transmission frame of the neighbor node in the local maximum value state, and sending to a central coordinator by a data aggregation manner.

6. The ZigBee-based combustible gas multi-point online monitoring system according to claim 1, characterized in that, The system further includes: a source positioning module deployed at the central coordinator, configured to receive data packets from each sensor node; a topology reconstruction unit configured to inversely deduce a spatial distribution topology of a physical field parameter according to position information of the sensor node in the local maximum value state and a gradient vector in the differential data packet; and a path backtracking unit configured to, in a case where part of node data is lost due to channel congestion, construct a dynamic trajectory of gas diffusion according to a time stamp sequence of a received high-priority alarm node, and locate a starting source coordinate of a physical field anomaly.

7. The ZigBee-based combustible gas multi-point online monitoring system according to claim 3, characterized in that, The inverse mapping relationship established in the reverse backoff unit follows a preset nonlinear weighting function, and the nonlinear weighting function takes a power of the node state data value as a denominator, so as to ensure that the reduction amplitude of the backoff time changes exponentially when the value is close to a preset alarm threshold, thereby realizing millisecond-level channel preemption in a sudden high-concentration working condition.

8. The ZigBee-based combustible gas multi-point online monitoring system according to claim 1, characterized in that, The neighborhood interaction module further includes: a silent listening unit configured to, after the sensor node detects that the node state data exceeds a preset safety threshold, not immediately send an alarm to the central coordinator, but start a preset length of a contention window period; and in the contention window period, the sensor node only performs a low-power broadcast and listening operation on the neighbor node to complete the determination of the gradient extremum attribute, and a length of the contention window period is less than a maximum alarm response delay time specified by the system.

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