Forest tree diameter at breast height real-time accurate measurement method and system based on low-power-consumption wireless network

Abstract

The invention provides a real-time accurate measurement method and system for the diameter at breast height of a forest based on a low-power-consumption wireless network, and relates to the technical field of forestry monitoring, and the method comprises the steps: deploying wireless measurement nodes in a forest region, constructing a network topology structure based on a signal intensity value, calculating a data transmission path, distributing a transmission time slot, and collecting the data of the diameter at breast height in real time; optimizing a transmission path according to the transmission delay and the congestion degree, monitoring the node electric quantity and dynamically adjusting the network; and finally, the data is transmitted to a gateway and a report is generated and uploaded to the cloud. According to the invention, real-time and accurate acquisition and reliable transmission of forest tree diameter at breast height data under the condition of low power consumption can be realized, and the efficiency and accuracy of forestry monitoring are improved.

Description

Technical Field

[0001] This invention relates to the field of forestry monitoring technology, and in particular to a method and system for real-time and accurate measurement of tree diameter at breast height (DBH) based on a low-power wireless network. Background Technology

[0002] Tree diameter at breast height (DBH) is a crucial parameter in forestry resource surveys and forest management, reflecting changes in forest growth and the ecological environment. Traditional DBH measurement relies primarily on periodic manual measurements using tools such as measuring tapes or calipers. This method is resource-intensive and difficult to implement in real-time. With the development of Internet of Things (IoT) technology, tree growth monitoring methods based on wireless sensor networks are gaining attention, providing new technological means for forestry research and production management.

[0003] Existing tree diameter at breast height (DBH) monitoring technologies still have some shortcomings and deficiencies. Traditional wireless sensor networks are inefficient in energy management in complex forest environments, and sensor node batteries have short lifespans, requiring frequent battery replacements, which increases maintenance costs and workload. Existing DBH monitoring systems lack effective path optimization mechanisms during data transmission, making them prone to data loss or severe delays in complex forest terrain and vegetation interference. Existing systems generally lack the ability to monitor and dynamically adjust node power status in real time, and cannot adaptively adjust network topology and transmission strategies based on node energy conditions, resulting in insufficient network stability and reliability. Summary of the Invention

[0004] This invention provides a method and system for real-time and accurate measurement of tree diameter at breast height (DBH) based on a low-power wireless network, which can solve the problems in the prior art.

[0005] A first aspect of this invention provides a method for real-time and accurate measurement of tree diameter at breast height (DBH) based on a low-power wireless network, comprising:

[0006] Wireless measurement nodes are deployed in forest areas, including diameter at breast height (DBH) sensors, data processors, and wireless communication modules, and a wireless measurement network is established through the wireless communication modules.

[0007] Obtain the signal strength values ​​of each wireless measurement node in the wireless measurement network, construct the network topology based on the signal strength values, calculate the data transmission path of each wireless measurement node, and allocate transmission time slots;

[0008] Based on the transmission time slot, the diameter at breast height (DBH) sensor collects DBH data of trees, and the data processor combines the DBH data with the collection time and node location to form a measurement data packet, which is then sent to the adjacent wireless measurement node based on the data transmission path.

[0009] Record the local clock information of the wireless measurement node and the data packet acquisition timestamp, calculate the transmission delay value, determine the path optimization parameters based on the transmission delay value and the congestion level value of the data transmission path, and update the data transmission path;

[0010] Monitor the battery level of each wireless measurement node. When the battery level is lower than a preset battery threshold, recalculate the data transmission path of the surrounding wireless measurement nodes and update the transmission time slot.

[0011] The measurement data packet is transmitted to the gateway along the updated data transmission path, a chest diameter growth trend report is generated, and uploaded to the cloud server.

[0012] In one optional embodiment, obtaining the signal strength values ​​of each wireless measurement node in the wireless measurement network and constructing the network topology based on the signal strength values ​​includes:

[0013] By transmitting probe frames at different transmission power levels through wireless measurement nodes, and receiving probe frames from other wireless measurement nodes, the signal strength value, reception time and signal-to-noise ratio of the probe frames are recorded to obtain initial signal data.

[0014] The temperature, humidity and terrain parameters of the location of the wireless measurement node are collected, and the environmental correction factor is determined by multiplying the corresponding attenuation influence coefficients. The initial signal data is then corrected according to the environmental correction factor to obtain the corrected signal data.

[0015] Multiple sets of corrected signal data are continuously collected within a preset time window. Abnormal samples that deviate from the mean by more than a preset range are removed, and a weighted moving average is calculated to generate a signal quality score.

[0016] A connection weight matrix is ​​constructed based on signal quality scoring. The relative positional relationship and connection stability between wireless measurement nodes are calculated. Wireless measurement nodes with connection stability higher than a preset stability threshold are identified as key nodes.

[0017] An initial backbone network is constructed with the key nodes as the core. Redundant links are added to the initial backbone network. The number of redundant links is determined according to the network connection density threshold to generate an optimized network topology.

[0018] The system continuously monitors and optimizes changes in link quality and network load distribution in the network topology. When a link quality is detected to be lower than a preset quality threshold or a network load is detected to exceed a preset load threshold, the connection weight matrix is ​​recalculated and the network topology is updated.

[0019] In one optional embodiment, calculating the data transmission path for each wireless measurement node and allocating transmission time slots includes:

[0020] Based on the network topology, any two wireless measurement nodes are selected as the source node and the target node in sequence. All node combinations traversed from the source node to the target node are calculated, and the node sequence and path hop count for each node combination are recorded to generate a set of optional paths.

[0021] The signal quality, bandwidth utilization, and packet loss rate of each link in the set of optional paths are obtained. The reliability score of each link is calculated. The weighted sum of the reliability scores of each link is used as the link quality coefficient. The remaining energy and forwarding load of each wireless measurement node are obtained. The path cost coefficient is calculated based on the energy consumption rate and the path hop count. The product of the link quality coefficient and the path cost coefficient is determined as the comprehensive path score.

[0022] The paths with the highest and second-highest overall scores are selected as the primary transmission path and the backup transmission path, respectively.

[0023] The network is divided into multiple layers based on the number of hops from the source node to the target node in the main transmission path. Basic time slots are allocated based on the data generation rate and forwarding data volume of each layer node, and emergency time slots are added after the basic time slots to form a time slot allocation table.

[0024] In one optional embodiment, the network is divided into multiple layers based on the path hop count from the source node to the target node in the main transmission path. Basic time slots are allocated based on the data generation rate and forwarding data volume of each layer's nodes. An emergency time slot is appended after the basic time slots to form a time slot allocation table, including:

[0025] The network is divided into multiple layers based on the number of hops from the source node to the target node in the main transmission path, and the node set corresponding to each layer is recorded.

[0026] Calculate the data generation rate of each node in the node set, and count the number of lower-level nodes forwarded by each node. Calculate the forwarded data volume of each node based on the product of the number of lower-level nodes and the data generation rate of the lower-level nodes. Add the data generation rate and forwarded data volume of each node to obtain the node transmission load value.

[0027] The load fluctuation coefficient is determined based on the historical load standard deviation of each node, and the sum of the node's transmission load value and the load fluctuation coefficient is determined as the corrected load value.

[0028] Based on the preset minimum time slot unit, the basic time slot number of each node is calculated according to the ratio of the modified load value to the scheduling cycle, and a corresponding number of basic time slots are allocated to each node.

[0029] Obtain the communication range profile of each node, determine the node pairs with overlapping time slot positions in the basic time slot based on the communication range profile, use the percolation diffusion algorithm to reallocate different time slot positions to the node pairs, and perform the time slot position exchange operation of the nodes.

[0030] The upper limit of the confidence interval is determined based on the historical burst data of each node, and the length of the emergency time slot is determined according to the ratio of the upper limit of the confidence interval to the scheduling period. The emergency time slot is then appended to the basic time slot of each level node in an interleaved distribution manner.

[0031] The deviation between the actual transmission load of each node and the corrected load value is periodically detected. When the deviation exceeds a preset deviation threshold, the load fluctuation coefficient and the historical burst data are updated and the time slot allocation table is regenerated.

[0032] In one optional embodiment, the communication range profile of each node is obtained, and node pairs with overlapping time slot positions in the basic time slot are determined based on the communication range profile. A percolation diffusion algorithm is used to reallocate different time slot positions to the node pairs, and the time slot position exchange operation of the nodes is performed, including:

[0033] The transmit power and received signal strength of each node are obtained, the communication range profile of each node is established, the spatial distance relationship between node pairs is calculated based on the communication range profile, and the geometric features of the overlapping area of ​​the communication range are determined.

[0034] A time slot occupancy matrix is ​​constructed to record the basic time slot distribution of each node. Based on the time slot occupancy matrix, node pairs with overlapping time slot positions are detected. The duration of time slot overlap of the node pairs is calculated. The permeation probability of the node pairs is determined according to the geometric characteristics of the overlapping area of ​​the communication range and the duration of time slot overlap.

[0035] The initial diffusion coefficient of the node pair is set according to the seepage probability and the duration of time slot overlap. The diffusion gradient between the node combination is calculated based on the initial diffusion coefficient, and the probability distribution function of node time slot migration is constructed.

[0036] The node combination with the longest duration of time slot overlap is selected as the starting point. The time slot migration direction of the node is calculated based on the seepage probability and diffusion gradient, and the candidate time slot position of the node is updated.

[0037] The local congestion level at the candidate time slot location is detected, and the diffusion coefficient is dynamically adjusted according to the local congestion level. When the adjustment of the diffusion coefficient exceeds the preset adjustment range, a time slot location rollback operation is performed.

[0038] The load balancing degree between node pairs is calculated, the optimal switching node pair is determined, the time slot switching execution order of the optimal switching node pair is planned, and the node time slot position switching operation is completed under the guidance of the time slot switching execution order.

[0039] In one optional embodiment, the local clock information of the wireless measurement node and the data packet acquisition timestamp are recorded, a transmission delay value is calculated, and path optimization parameters are determined based on the transmission delay value and the congestion level value of the data transmission path. Updating the data transmission path includes:

[0040] Record the local clock information of the wireless measurement node and the data packet acquisition timestamp, calculate the clock deviation value between nodes, and calibrate the local clock of the measurement node based on the clock deviation value;

[0041] Calculate the processing time of the data packet within the node, the access time required to pass through the channel, the transmission time on the link, and the waiting time in the queue. Then, sum the processing time, access time, transmission time, and waiting time to obtain the transmission delay value.

[0042] A delay distribution function is constructed by statistically analyzing the mean and variance of historical transmission delay values. A delay threshold is set based on the delay distribution function, and transmission delay values ​​exceeding the delay threshold are marked as abnormal delay points.

[0043] Obtain the data buffer queue length of the node, calculate the processing capacity saturation of the node, monitor the packet loss rate and channel occupancy rate of data transmission, and calculate the congestion level value based on the processing capacity saturation, packet loss rate and channel occupancy rate;

[0044] A delay weighting factor is set based on the transmission delay value, a congestion penalty coefficient is determined based on the congestion level value, the delay weighting factor and the congestion penalty coefficient are used as path optimization parameters, and the optimal transmission path is selected based on the path optimization parameters.

[0045] After the optimal transmission path is determined, the transmission path of the data packet is redirected, the routing table of the node is updated, and the data transmission path is switched.

[0046] A second aspect of the present invention provides a real-time accurate measurement system for tree diameter at breast height (DBH) based on a low-power wireless network, comprising:

[0047] The first unit is used to deploy wireless measurement nodes in forest areas, including a diameter at breast height sensor, a data processor, and a wireless communication module, which establishes a wireless measurement network through the wireless communication module;

[0048] The second unit is used to obtain the signal strength value of each wireless measurement node in the wireless measurement network, construct the network topology based on the signal strength value, calculate the data transmission path of each wireless measurement node, and allocate transmission time slots.

[0049] The third unit is used for the diameter at breast height (DBH) sensor to collect DBH data of trees based on the transmission time slot, and the data processor to form a measurement data packet with the DBH data, the collection time, and the node location, and send it to the adjacent wireless measurement node based on the data transmission path.

[0050] The fourth unit is used to record the local clock information of the wireless measurement node and the data packet acquisition timestamp, calculate the transmission delay value, determine the path optimization parameters based on the transmission delay value and the congestion level value of the data transmission path, and update the data transmission path.

[0051] The fifth unit is used to monitor the power level of each wireless measurement node. When the power level is lower than a preset power threshold, the data transmission path of the surrounding wireless measurement nodes is recalculated and the transmission time slot is updated.

[0052] The sixth unit is used to transmit the measurement data packet to the gateway along the updated data transmission path, generate a chest diameter growth trend report, and upload it to the cloud server.

[0053] A third aspect of the present invention provides an electronic device, comprising:

[0054] processor;

[0055] Memory used to store processor-executable instructions;

[0056] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0057] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0058] In this embodiment of the invention, by deploying wireless measurement nodes to construct a network topology, automatic collection and transmission of tree diameter at breast height (DBH) data is achieved. This overcomes the problems of low efficiency and large errors in traditional manual measurement methods, improving the accuracy and real-time performance of DBH monitoring. Based on signal strength values, the data transmission path is dynamically calculated and transmission time slots are allocated. The transmission path is optimized by combining transmission delay and congestion levels, effectively solving the problem of unstable wireless communication in complex forest environments and improving the reliability of data transmission and the overall network performance. By monitoring node power and dynamically adjusting the transmission path, low-power operation is achieved, extending the system's lifespan. Simultaneously, the measurement data is uploaded to a cloud server to generate a DBH growth trend report, providing a scientific basis for forestry management decisions and possessing significant ecological and economic value. Attached Figure Description

[0059] Figure 1This is a flowchart illustrating the method for real-time and accurate measurement of tree diameter at breast height (DBH) based on a low-power wireless network according to an embodiment of the present invention.

[0060] Figure 2 The flowchart shows the node time slot optimization algorithm based on seepage diffusion. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0063] Figure 1 This is a flowchart illustrating the real-time accurate measurement method for tree diameter at breast height (DBH) based on a low-power wireless network according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0064] Wireless measurement nodes are deployed in forest areas, including diameter at breast height (DBH) sensors, data processors, and wireless communication modules, and a wireless measurement network is established through the wireless communication modules.

[0065] Obtain the signal strength values ​​of each wireless measurement node in the wireless measurement network, construct the network topology based on the signal strength values, calculate the data transmission path of each wireless measurement node, and allocate transmission time slots;

[0066] Based on the transmission time slot, the diameter at breast height (DBH) sensor collects DBH data of trees, and the data processor combines the DBH data with the collection time and node location to form a measurement data packet, which is then sent to the adjacent wireless measurement node based on the data transmission path.

[0067] Record the local clock information of the wireless measurement node and the data packet acquisition timestamp, calculate the transmission delay value, determine the path optimization parameters based on the transmission delay value and the congestion level value of the data transmission path, and update the data transmission path;

[0068] Monitor the battery level of each wireless measurement node. When the battery level is lower than a preset battery threshold, recalculate the data transmission path of the surrounding wireless measurement nodes and update the transmission time slot.

[0069] The measurement data packet is transmitted to the gateway along the updated data transmission path, a chest diameter growth trend report is generated, and uploaded to the cloud server.

[0070] In one optional implementation, obtaining the signal strength values ​​of each wireless measurement node in the wireless measurement network and constructing the network topology based on the signal strength values ​​includes:

[0071] By transmitting probe frames at different transmission power levels through wireless measurement nodes, and receiving probe frames from other wireless measurement nodes, the signal strength value, reception time and signal-to-noise ratio of the probe frames are recorded to obtain initial signal data.

[0072] The temperature, humidity and terrain parameters of the location of the wireless measurement node are collected, and the environmental correction factor is determined by multiplying the corresponding attenuation influence coefficients. The initial signal data is then corrected according to the environmental correction factor to obtain the corrected signal data.

[0073] Multiple sets of corrected signal data are continuously collected within a preset time window. Abnormal samples that deviate from the mean by more than a preset range are removed, and a weighted moving average is calculated to generate a signal quality score.

[0074] A connection weight matrix is ​​constructed based on signal quality scoring. The relative positional relationship and connection stability between wireless measurement nodes are calculated. Wireless measurement nodes with connection stability higher than a preset stability threshold are identified as key nodes.

[0075] An initial backbone network is constructed with the key nodes as the core. Redundant links are added to the initial backbone network. The number of redundant links is determined according to the network connection density threshold to generate an optimized network topology.

[0076] The system continuously monitors and optimizes changes in link quality and network load distribution in the network topology. When a link quality is detected to be lower than a preset quality threshold or a network load is detected to exceed a preset load threshold, the connection weight matrix is ​​recalculated and the network topology is updated.

[0077] In one specific implementation, multiple wireless measurement nodes are deployed in a wireless measurement network, each with signal transmission and reception capabilities at different power levels. The wireless measurement nodes take turns transmitting probe frames at five different power levels: 1mW, 5mW, 10mW, 15mW, and 20mW. At each power level, the node transmits 10 probe frames, each containing information such as the transmitting node ID, transmission power level, and timestamp. Upon receiving a probe frame, surrounding wireless measurement nodes record the signal strength index (RSSI, in dBm), reception time (accurate to milliseconds), and signal-to-noise ratio (SNR, in dB). For example, if node A transmits a probe frame at 10mW power, node B, upon receiving the frame, records an RSSI of -75dBm, a reception time of 2023-05-15 10:30:25.342, and an SNR of 12dB. In this way, each node acquires initial signal data from other nodes, forming an M×N signal strength matrix, where M is the number of nodes and N is the number of detection frames for each node at different power levels.

[0078] To improve the accuracy of signal data, the impact of environmental factors on signal transmission needs to be considered. Temperature and humidity sensors and terrain parameter acquisition devices are deployed at the location of each wireless measurement node to collect real-time temperature (°C), humidity (relative humidity, %), and terrain parameters (including terrain relief, vegetation cover, etc.) at the node's location. Based on experimental data, the attenuation coefficients for each environmental factor are determined as follows: temperature attenuation coefficient Ct is set to 0.05 dB / °C (when the temperature exceeds 25°C); humidity attenuation coefficient Ch is set to 0.03 dB / % (when the humidity exceeds 60%); terrain relief attenuation coefficient Cg is set to 0.1 dB / meter (relative height difference); and vegetation cover attenuation coefficient Cv is set to 0.2 dB / 10% (the attenuation amount for every 10% increase in coverage). The environmental correction factor Ce is determined by multiplying these attenuation influence coefficients: Ce = (1 + ΔT × Ct) × (1 + ΔH × Ch) × (1 + ΔG × Cg) × (1 + ΔV × Cv), where ΔT, ΔH, ΔG, and ΔV are the deviations of temperature, humidity, topographic relief, and vegetation cover, respectively. Taking node B as an example, when the temperature at the node's location is 30℃ (ΔT = 5℃), the relative humidity is 80% (ΔH = 20%), the topographic relief is 2 meters (ΔG = 2), and the vegetation cover is 50% (ΔV = 5), the calculated environmental correction factor Ce = 1.525. Using this correction factor to correct the signal strength received by node B (-75dBm), the corrected signal strength value is approximately -75dBm ÷ 1.525 ≈ -49.18dBm.

[0079] To eliminate the impact of signal fluctuations, 10 sets of corrected signal data were continuously collected within a preset time window of 60 seconds. Statistical analysis was used to remove outlier samples that deviated from the mean by more than two standard deviations. A weighted moving average was calculated for the remaining samples, with more recent data having a higher weight than earlier data. The weight allocation was as follows: the weight of the 5 most recent data sets was 0.15 each, and the weight of the 5 earlier data sets was 0.05 each. For example, if node B received corrected signal strength values ​​from node A at 10 time points of [-49.2, -48.9, -49.1, -56.8, -49.3, -49.0, -48.8, -49.2, -49.1, -48.9] dBm, the value of -56.8 dBm deviated significantly from the mean and was identified as an outlier and removed. The weighted average of the remaining 9 data sets was calculated, resulting in a final signal strength of -49.06 dBm. Based on the signal strength and signal-to-noise ratio, a signal quality score Q was generated, ranging from 0 to 100. The signal quality score calculation takes into account signal strength, signal-to-noise ratio, and data stability. For example, the signal quality score of node B receiving signal from node A is 78 points.

[0080] Based on the signal quality scores between nodes, an N×N connection weight matrix W is constructed, where N is the number of nodes. Each element Wij in the matrix represents the connection weight between node i and node j, which is calculated based on the signal quality score, distance estimation, and energy consumption. For example, the connection weight between node A and node B is calculated to be 0.85. Using the connection weight matrix, the relative positional relationships and connection stability between wireless measurement nodes are calculated. The connection stability index comprehensively considers signal quality fluctuations, packet loss rate, and connection duration. Wireless measurement nodes with connection stability higher than a preset stability threshold (0.75) are identified as key nodes. In a network containing 20 nodes, five key nodes—node 1, node 4, node 7, node 12, and node 16—were identified.

[0081] An initial backbone network is constructed around these key nodes, with direct connections established between them. To improve network reliability, redundant links are added to the initial backbone network. The number of redundant links is determined based on the network connection density threshold (set to 2.5, i.e., the average number of connections per node). For example, based on the initial backbone network, eight redundant links are added, such as the connection between node 2 and node 7, and the connection between node 4 and node 16, forming a more robust and optimized network topology.

[0082] The system continuously monitors and optimizes link quality changes and network load distribution in the network topology, with a monitoring frequency of once every 10 seconds. When a link's quality is detected to be below a preset quality threshold (70 points) or the network load exceeds a preset load threshold (80%), a topology update mechanism is triggered to recalculate the connection weight matrix and update the network topology. For example, when the link quality between node 4 and node 12 drops to 65 points, the system automatically disconnects the link and establishes a new connection between node 4 and node 7, thereby maintaining the overall performance and reliability of the network.

[0083] The method in this embodiment establishes an adaptive network topology based on signal strength. This structure can dynamically adjust according to environmental changes and network status, ensuring the stable operation of the wireless measurement network and data transmission efficiency.

[0084] In one optional implementation, calculating the data transmission path for each wireless measurement node and allocating transmission time slots includes:

[0085] Based on the network topology, any two wireless measurement nodes are selected as the source node and the target node in sequence. All node combinations traversed from the source node to the target node are calculated, and the node sequence and path hop count for each node combination are recorded to generate a set of optional paths.

[0086] The signal quality, bandwidth utilization, and packet loss rate of each link in the set of optional paths are obtained. The reliability score of each link is calculated. The weighted sum of the reliability scores of each link is used as the link quality coefficient. The remaining energy and forwarding load of each wireless measurement node are obtained. The path cost coefficient is calculated based on the energy consumption rate and the path hop count. The product of the link quality coefficient and the path cost coefficient is determined as the comprehensive path score.

[0087] The paths with the highest and second-highest overall scores are selected as the primary transmission path and the backup transmission path, respectively.

[0088] The network is divided into multiple layers based on the number of hops from the source node to the target node in the main transmission path. Basic time slots are allocated based on the data generation rate and forwarding data volume of each layer node, and emergency time slots are added after the basic time slots to form a time slot allocation table.

[0089] In one specific implementation, efficient and reliable transmission of tree diameter at breast height (DBH) data is achieved through an optimized path selection algorithm and time slot allocation mechanism.

[0090] After deploying wireless measurement nodes in a forest area, it is necessary to establish efficient and reliable data transmission paths. Based on the network topology, any two wireless measurement nodes are selected sequentially as the source and target nodes, and all possible node combination paths are calculated. Assume that eight wireless measurement nodes, numbered N1 to N8, are deployed in a certain forest area, where N1 is the sink node (target node). When N5 is the source node, possible paths include: N5-N3-N1 (2 hops), N5-N4-N2-N1 (3 hops), N5-N7-N6-N2-N1 (4 hops), etc. Through a traversal algorithm, the node sequence and corresponding hop count of all possible paths can be obtained, forming a set of optional paths P = {P1, P2, ..., Pn}, where each path Pi contains the node sequence and hop count information.

[0091] Obtain the signal quality, bandwidth utilization, and packet loss rate of each link in the set of available paths, and calculate the link reliability score. For each link Lj in each path Pi, measure its signal quality index RSSI (Received Signal Strength Indicator), bandwidth utilization BU, and packet loss rate PR. For example, the RSSI of link N5-N3 is -75dBm, the bandwidth utilization is 35%, and the packet loss rate is 2%; the RSSI of link N3-N1 is -70dBm, the bandwidth utilization is 40%, and the packet loss rate is 1.5%. Calculate the reliability score RS for each link, which is a weighted function of the three indicators: RS = 0.4 × (normalized RSSI) + 0.3 × (1 - BU) + 0.3 × (1 - PR). Taking the above links as an example, the RSSI is normalized to the 0-1 range (1 for above -60dBm, 0 for below -90dBm, with linear interpolation in between). The reliability score of the N5-N3 link is 0.4×0.5+0.3×0.65+0.3×0.98=0.684; the reliability score of the N3-N1 link is 0.4×0.67+0.3×0.6+0.3×0.985=0.743. The Link Quality Coefficient (LQC) of path Pi is calculated by summing the reliability scores of each link on the path and dividing by the number of hops. Taking path P1 (N5-N3-N1) as an example, its Link Quality Coefficient is (0.684+0.743) / 2=0.7135.

[0092] Obtain the remaining energy and forwarding load of each wireless measurement node, and calculate the path cost coefficient. Measure the remaining battery power and the number of data packets currently being forwarded by each node. For example, node N3 has 75% remaining battery power and forwards 30 data packets per minute; node N2 has 65% remaining battery power and forwards 25 data packets per minute. Calculate the path cost coefficient (PCC) based on the node's energy consumption rate (ECR) (related to remaining battery power and forwarding load) and the path hop count (h). Energy consumption rate (ECR) = (1 - remaining battery percentage) × forwarding load, Path cost coefficient (PCC) = (1 - average ECR of all nodes on the path) / h. Taking path P1 (N5-N3-N1) as an example, the ECR of N3 is (1-0.75)×30=7.5, and N1 is the sink node and is not included in the path cost. The cost coefficient of path P1 is (1-7.5 / 100) / 2=0.4625. Taking path P2 (N5-N4-N2-N1) as an example, assuming that the ECR of N4 is 5 and the ECR of N2 is 8.75, the cost coefficient of path P2 is (1-(5+8.75) / 200) / 3=0.3229.

[0093] The product of the link quality coefficient and the path cost coefficient is used to determine the overall path score. For example, path P1 has an overall score of 0.7135 × 0.4625 = 0.33; path P2 has an overall score of 0.7 × 0.3229 = 0.226 (assuming the link quality coefficient of P2 is 0.7). Comparing the overall scores of all paths, the path with the highest score is selected as the primary transmission path, and the path with the second highest score is selected as the backup transmission path. In this example, path P1 (N5-N3-N1) is the primary transmission path, and path P2 (N5-N4-N2-N1) is the backup transmission path.

[0094] The network is divided into multiple layers based on the number of hops from the source node to the target node in the main transmission path. Taking path P1 (N5-N3-N1) as an example, the network is divided into 3 layers: layer 0 contains the target node N1, layer 1 contains the intermediate node N3, and layer 2 contains the source node N5. The data generation and forwarding of nodes in each layer are different, requiring different communication time slots to be allocated.

[0095] Basic time slots are allocated based on the data generation rate and forwarding volume of each level of node. The source node N5 in level 2 generates a 64-byte data packet for measuring tree diameter at breast height (DBH) every 5 minutes. Transmitting this packet takes 5 milliseconds. Considering wireless channel contention and collision avoidance, N5 is allocated a basic time slot of 15 milliseconds. The intermediate node N3 in level 1 needs to forward data from N5 and also generates a data packet itself every 5 minutes; therefore, it is allocated a basic time slot of 30 milliseconds. The target node N1 in level 0 acts as the aggregation node, receiving all data and does not require an allocated transmission time slot.

[0096] An emergency time slot is added after the basic time slot to form the time slot allocation table. To cope with network congestion, interference, or data retransmission, an emergency time slot is added after the basic time slot. The length of the emergency time slot is 30% of the basic time slot. For example, the emergency time slot for N5 is 15 × 30% = 4.5 milliseconds (rounded to 5 milliseconds), and the emergency time slot for N3 is 30 × 30% = 9 milliseconds. The final time slot allocation table is as follows: N5 has a total time slot of 20 milliseconds (15 milliseconds basic + 5 milliseconds emergency), and N3 has a total time slot of 39 milliseconds (30 milliseconds basic + 9 milliseconds emergency). This time slot allocation ensures that the diameter at breast height (DBH) data of trees can be transmitted from each measurement node to the aggregation node on time, while reserving sufficient emergency time to handle abnormal situations.

[0097] The method in this embodiment achieves efficient and reliable transmission of real-time accurate measurement data of tree diameter at breast height (DBH), extends the battery life of wireless measurement nodes, and improves the overall reliability and real-time performance of the system.

[0098] In one optional implementation, the network is divided into multiple layers based on the path hop count from the source node to the target node in the main transmission path. Basic time slots are allocated based on the data generation rate and forwarding data volume of each layer's nodes. Emergency time slots are appended after the basic time slots to form a time slot allocation table, including:

[0099] The network is divided into multiple layers based on the number of hops from the source node to the target node in the main transmission path, and the node set corresponding to each layer is recorded.

[0100] Calculate the data generation rate of each node in the node set, and count the number of lower-level nodes forwarded by each node. Calculate the forwarded data volume of each node based on the product of the number of lower-level nodes and the data generation rate of the lower-level nodes. Add the data generation rate and forwarded data volume of each node to obtain the node transmission load value.

[0101] The load fluctuation coefficient is determined based on the historical load standard deviation of each node, and the sum of the node's transmission load value and the load fluctuation coefficient is determined as the corrected load value.

[0102] Based on the preset minimum time slot unit, the basic time slot number of each node is calculated according to the ratio of the modified load value to the scheduling cycle, and a corresponding number of basic time slots are allocated to each node.

[0103] Obtain the communication range profile of each node, determine the node pairs with overlapping time slot positions in the basic time slot based on the communication range profile, use the percolation diffusion algorithm to reallocate different time slot positions to the node pairs, and perform the time slot position exchange operation of the nodes.

[0104] The upper limit of the confidence interval is determined based on the historical burst data of each node, and the length of the emergency time slot is determined according to the ratio of the upper limit of the confidence interval to the scheduling period. The emergency time slot is then appended to the basic time slot of each level node in an interleaved distribution manner.

[0105] The deviation between the actual transmission load of each node and the corrected load value is periodically detected. When the deviation exceeds a preset deviation threshold, the load fluctuation coefficient and the historical burst data are updated and the time slot allocation table is regenerated.

[0106] In one specific implementation, after deploying wireless measurement nodes to build a network in a forest area, reliable data transmission is ensured through optimized routing and time slot allocation strategies. The network is divided into multiple layers based on the path hop count from the source node to the target node in the main transmission path, and the node set corresponding to each layer is recorded. Taking a low-power wireless sensor network deployed in a forest area as an example, there are 15 wireless measurement nodes, where N0 is the aggregation node (target node). After determining the main transmission path, the network is divided into four layers: Layer 0 contains the target node N0; Layer 1 contains nodes N1, N2, and N3; Layer 2 contains nodes N4, N5, N6, N7, and N8; and Layer 3 contains nodes N9, N10, N11, N12, N13, and N14. The node sets for each level are L0 = {N0}, L1 = {N1, N2, N3}, L2 = {N4, N5, N6, N7, N8}, and L3 = {N9, N10, N11, N12, N13, N14}.

[0107] The data generation rate of each node in the calculation node set is determined, and the number of lower-level nodes forwarded by each node is counted. In the application of tree diameter at breast height (DBH) measurement, each node generates data packets according to the configured measurement frequency. The data generation rate is expressed as the number of data packets generated per minute. The leaf nodes of level 3 (N9 to N14) generate one 64-byte data packet every 5 minutes, with a data generation rate of 0.2 packets / minute; the nodes of level 2 (N4 to N8) generate one data packet every 3 minutes, with a data generation rate of 0.33 packets / minute; and the nodes of level 1 (N1 to N3) generate one data packet every 2 minutes, with a data generation rate of 0.5 packets / minute. The number of lower-level nodes forwarded by each node is determined by the network topology: N1 forwards data from N4 and N5, with 2 lower-level nodes; N2 forwards data from N6, with 1 lower-level node; N3 forwards data from N7 and N8, with 2 lower-level nodes; N4 forwards data from N9 and N10, with 2 lower-level nodes; N5 forwards data from N11, with 1 lower-level node; N7 forwards data from N12 and N13, with 2 lower-level nodes; N8 forwards data from N14, with 1 lower-level node; the remaining nodes do not forward data from lower-level nodes.

[0108] The forwarding data volume of each node is calculated based on the number of lower-level nodes and their data generation rates. For node N1, its forwarding data volume is the sum of the data generation rates of lower-level nodes N4 and N5, i.e., 0.33 + 0.33 = 0.66 packets / minute; for node N4, its forwarding data volume is the sum of the data generation rates of lower-level nodes N9 and N10, i.e., 0.2 + 0.2 = 0.4 packets / minute. The node transmission load is obtained by adding the data generation rate and forwarding data volume of each node. The transmission load of node N1 is 0.5 + 0.66 = 1.16 packets / minute; the transmission load of node N4 is 0.33 + 0.4 = 0.73 packets / minute. The transmission load of other nodes is calculated similarly.

[0109] The load fluctuation coefficient is determined based on the historical load standard deviation of each node. The standard deviation of the hourly transmission load of each node over the past 24 hours is calculated as the load fluctuation coefficient. For example, the historical load standard deviation of node N1 is 0.15 packets / minute, indicating that its load has some fluctuation; the historical load standard deviation of node N4 is 0.08 packets / minute, indicating that its load is relatively stable. The sum of the node's transmission load value and the load fluctuation coefficient is determined as the corrected load value. The corrected load value for node N1 is 1.16 + 0.15 = 1.31 packets / minute; the corrected load value for node N4 is 0.73 + 0.08 = 0.81 packets / minute.

[0110] Based on the preset minimum time slot unit, the basic time slot number for each node is calculated according to the ratio of the corrected load value to the scheduling cycle. Assuming the scheduling cycle is 60 minutes and the minimum time slot unit is 5 milliseconds, the basic time slot number for node N1 is calculated as: (1.31 packets / minute × 60 minutes × 5 milliseconds / packet) ÷ 5 milliseconds = 78.6, rounded down to 79 time slots; the basic time slot number for node N4 is calculated as: (0.81 packets / minute × 60 minutes × 5 milliseconds / packet) ÷ 5 milliseconds = 48.6, rounded down to 49 time slots. This process is repeated to allocate the corresponding number of basic time slots to each node.

[0111] The communication range contours of each node were obtained, and node pairs with overlapping time slot positions in the basic time slots were identified based on these contours. In forest environments, the communication ranges of each node exhibit irregular shapes due to the influence of terrain and vegetation. Contour maps of the communication ranges of each node were drawn through actual measurements, and the overlap of these contours was analyzed. It was found that the communication ranges of nodes N1 and N2 overlapped by 30%, and the communication ranges of nodes N4 and N5 overlapped by 25%. Communication between these nodes in the same time slot could lead to conflicts. A percolation diffusion algorithm was used to reallocate different time slot positions to the node pairs. This algorithm treats nodes as charged particles and communication conflicts as repulsive forces, using iterative calculations to achieve an equilibrium state in the node time slot positions. After 10 iterations, the time slot position of node N1 was moved to the first half of the scheduling cycle, and the time slot position of node N2 was moved to the second half of the scheduling cycle, effectively avoiding communication conflicts.

[0112] The upper limit of the confidence interval is determined based on the historical burst data of each node. The burst data of each node over the past 30 days is analyzed, and the upper limit is calculated using a 95% confidence level. The upper limit of the historical burst data confidence interval for node N1 is 0.25 packets / minute; the upper limit of the historical burst data confidence interval for node N4 is 0.15 packets / minute. The emergency time slot length is determined based on the ratio of the upper limit of the confidence interval to the scheduling cycle. The emergency time slot length for node N1 is (0.25 packets / minute × 60 minutes × 5 milliseconds / packet) ÷ 5 milliseconds = 15 time slots; the emergency time slot length for node N4 is (0.15 packets / minute × 60 minutes × 5 milliseconds / packet) ÷ 5 milliseconds = 9 time slots. Emergency time slots are appended to the basic time slots of each level of node using an interleaved distribution method to avoid network congestion caused by concentrated emergency time slots.

[0113] The deviation between the actual transmission load and the corrected load value of each node is periodically checked. A load check is performed on the network every 12 hours, and the load deviation of each node is calculated. When the deviation exceeds a preset deviation threshold of 15%, the load fluctuation coefficient and historical burst data are updated, and the time slot allocation table is regenerated. For example, if the actual transmission load of node N7 is 0.95 packets / minute, the corrected load value is 0.8 packets / minute, and the deviation is 18.75%, exceeding the threshold, parameters need to be updated and time slots reallocated.

[0114] Existing low-power wireless network time slot allocation technologies typically employ fixed time slot allocation or simple dynamic time slot allocation methods, which cannot effectively cope with network load changes and sudden data transmission demands in complex forest environments. This embodiment's method starts from the network hierarchical structure, optimizes time slots based on actual node load and historical data characteristics, and introduces statistical methods such as load fluctuation coefficients and confidence interval analysis to achieve more accurate time slot prediction and allocation. The percolation diffusion algorithm solves the node communication conflict problem, and periodic load detection and time slot reallocation mechanisms ensure long-term stable system operation.

[0115] In one optional implementation, the communication range profile of each node is obtained, and node pairs with overlapping time slot positions in the basic time slot are determined based on the communication range profile. A percolation diffusion algorithm is used to reallocate different time slot positions to the node pairs, and the time slot position exchange operation of the nodes is performed, including:

[0116] The transmit power and received signal strength of each node are obtained, the communication range profile of each node is established, the spatial distance relationship between node pairs is calculated based on the communication range profile, and the geometric features of the overlapping area of ​​the communication range are determined.

[0117] A time slot occupancy matrix is ​​constructed to record the basic time slot distribution of each node. Based on the time slot occupancy matrix, node pairs with overlapping time slot positions are detected. The duration of time slot overlap of the node pairs is calculated. The permeation probability of the node pairs is determined according to the geometric characteristics of the overlapping area of ​​the communication range and the duration of time slot overlap.

[0118] The initial diffusion coefficient of the node pair is set according to the seepage probability and the duration of time slot overlap. The diffusion gradient between the node combination is calculated based on the initial diffusion coefficient, and the probability distribution function of node time slot migration is constructed.

[0119] The node combination with the longest duration of time slot overlap is selected as the starting point. The time slot migration direction of the node is calculated based on the seepage probability and diffusion gradient, and the candidate time slot position of the node is updated.

[0120] The local congestion level at the candidate time slot location is detected, and the diffusion coefficient is dynamically adjusted according to the local congestion level. When the adjustment of the diffusion coefficient exceeds the preset adjustment range, a time slot location rollback operation is performed.

[0121] The load balancing degree between node pairs is calculated, the optimal switching node pair is determined, the time slot switching execution order of the optimal switching node pair is planned, and the node time slot position switching operation is completed under the guidance of the time slot switching execution order.

[0122] In one specific implementation, the transmit power and received signal strength of each node are acquired to establish the communication range profile of each node. Wireless measurement nodes deployed in forest areas typically employ different transmit powers to adapt to environmental requirements. By measuring and recording the transmit power and received signal strength of each node, a communication range profile map can be constructed. Taking 15 wireless measurement nodes deployed in a forest area as an example, node N1's transmit power is set to 0dBm, with a received signal strength of -65dBm within a 10-meter range, -75dBm within a 20-meter range, and -85dBm within a 30-meter range; node N2's transmit power is set to 2dBm, with a received signal strength of -60dBm within a 10-meter range, -72dBm within a 20-meter range, and -82dBm within a 30-meter range. Using these data, a communication range profile map is drawn, forming irregular polygonal or elliptical areas representing the effective communication range of the nodes.

[0123] Based on the communication range contours, the spatial distance relationships between node pairs are calculated, and the geometric characteristics of the overlapping areas of the communication ranges are determined. The spatial distances between each node are calculated through geographic coordinate analysis. The straight-line distance between nodes N1 and N2 is 25 meters, and the straight-line distance between nodes N1 and N3 is 40 meters. Based on the overlap of the communication range contours, the geometric characteristics of the overlapping areas are determined. The overlapping area of ​​the communication ranges of nodes N1 and N2 is an irregular ellipse with an area of ​​approximately 78 square meters, accounting for 26% of the communication range of N1 and 22% of the communication range of N2. The overlapping area of ​​the communication ranges of nodes N3 and N4 is approximately triangular with an area of ​​approximately 45 square meters, accounting for 15% of the communication range of N3 and 20% of the communication range of N4. These geometric characteristic data will be used for subsequent calculations of seepage probability.

[0124] A time slot occupancy matrix is ​​constructed to record the basic time slot distribution of each node. Using a 60-minute scheduling cycle and a minimum time slot unit of 5 milliseconds, a two-dimensional matrix is ​​created to record the time slot positions occupied by each node. Rows in the matrix represent node numbers, and columns represent time slot indices. For example, node N1 occupies time slot indices 1 to 79, node N2 occupies time slot indices 80 to 132, and node N3 occupies time slot indices 133 to 189. Based on the time slot occupancy matrix, node pairs with overlapping time slot positions are detected, and the duration of time slot overlap for each pair is calculated. The detection reveals that nodes N4 and N7 have overlapping time slot allocations, with overlapping time slot indices from 240 to 270, and an overlap duration of 31 time slot units (155 milliseconds); nodes N5 and N8 also have overlapping time slots, with overlapping time slot indices from 300 to 315, and an overlap duration of 16 time slot units (80 milliseconds).

[0125] The percolation probability of a node pair is determined based on the geometric characteristics of the overlapping communication range area and the duration of the time slot overlap. The percolation probability reflects the likelihood of a communication collision between the node pairs and is jointly determined by the communication range overlap ratio and the time slot overlap duration. For nodes N4 and N7, the communication range overlap ratio is 25%, and the time slot overlap duration is 155 milliseconds, resulting in a percolation probability of 0.25 × 155 ÷ (60 × 60 × 1000) = 0.00107. For nodes N5 and N8, the communication range overlap ratio is 18%, and the time slot overlap duration is 80 milliseconds, resulting in a percolation probability of 0.18 × 80 ÷ (60 × 60 × 1000) = 0.00040. A higher percolation probability indicates a greater likelihood of a communication collision between the node pairs, requiring priority adjustment of their time slot allocation.

[0126] The initial diffusion coefficients for node pairs are set based on the seepage probability and the duration of time slot overlap. The diffusion coefficients determine the speed and range of node time slot location migration. The seepage probability for nodes N4 and N7 is 0.00107, and the time slot overlap duration is 155 milliseconds, so the initial diffusion coefficient is set to 0.00107 × 155 = 0.166. The seepage probability for nodes N5 and N8 is 0.00040, and the time slot overlap duration is 80 milliseconds, so the initial diffusion coefficient is set to 0.00040 × 80 = 0.032. The diffusion gradient between node combinations is calculated based on the initial diffusion coefficients, constructing the probability distribution function for node time slot migration. The diffusion gradient represents the direction and intensity of seepage diffusion. The diffusion gradient between nodes N4 and N7 is 0.166, pointing to a region with low time slot occupancy; the diffusion gradient between nodes N5 and N8 is 0.032, also pointing to a region with low time slot occupancy.

[0127] The node pair with the longest duration of time slot overlap is selected as the starting point. The time slot migration direction of the nodes is calculated based on the seepage probability and diffusion gradient, and the candidate time slot positions of the nodes are updated. In this example, nodes N4 and N7 have the longest duration of time slot overlap and are selected as the starting point. Based on the seepage probability of 0.00107 and the diffusion gradient of 0.166, the time slot of node N4 should be migrated forward by 15 units, and the time slot of node N7 should be migrated backward by 16 units. After the update, the candidate time slot positions of node N4 are indices 225 to 255, and the candidate time slot positions of node N7 are indices 286 to 316. Similarly, migration calculations are performed for other node pairs with time slot overlap.

[0128] The local congestion level at candidate time slot locations is detected, and the diffusion coefficient is dynamically adjusted based on this level. Local congestion is measured by the density of allocated time slots around the candidate time slot location. The local congestion level at candidate time slot location N4 is 35%, indicating moderate congestion; the local congestion level at candidate time slot location N7 is 55%, indicating high congestion. For high-congestion areas, the diffusion coefficient is increased by 1.5 times, allowing nodes to "skip" congested areas; for moderately congested areas, the diffusion coefficient remains unchanged; for low-congestion areas, the diffusion coefficient is reduced to 0.8 times its original value. After adjustment, the diffusion coefficient of node N7 becomes 0.166 × 1.5 = 0.249, while the diffusion coefficient of node N4 remains unchanged at 0.166. When the adjustment of the diffusion coefficient exceeds the preset adjustment range (e.g., 0.5 to 2 times the original value), a time slot location rollback operation is performed, restoring the previous valid time slot location.

[0129] The load balancing degree between node pairs is calculated to determine the optimal switching node pair. Load balancing is measured by the difference in node transmission load values. Node N4 has a transmission load of 0.73 packets / minute, and node N7 has a transmission load of 0.68 packets / minute; the load difference is small, making them suitable for time slot switching. Node N5 has a transmission load of 0.45 packets / minute, and node N8 has a transmission load of 0.85 packets / minute; the load difference is large, making them unsuitable for direct switching. The time slot switching execution order of the optimal switching node pair is planned, and the node time slot position switching operation is completed under the guidance of the time slot switching execution order. The switching is performed in descending order of percolation probability: the time slot switching of nodes N4 and N7 is processed first, followed by the switching of other node pairs. After the switching is completed, node N4 occupies time slot indices 225 to 255, and node N7 occupies time slot indices 286 to 316, successfully eliminating time slot overlap.

[0130] Existing wireless network time slot allocation technologies mainly employ fixed allocation schemes based on TDMA or simple conflict avoidance algorithms, which cannot effectively cope with the changing communication characteristics in complex forest environments. These methods typically ignore the impact of overlapping node spatial distribution and communication ranges, leading to low time slot allocation efficiency and frequent communication conflicts. The method in this embodiment comprehensively considers the spatial location relationships of nodes and the overlapping characteristics of communication ranges, introducing percolation diffusion theory to establish a more accurate conflict prediction model. By combining the communication characteristics of the physical space with time slot allocation in the time domain, and dynamically adjusting the diffusion coefficient and load balancing strategy, efficient utilization of time slot resources is achieved.

[0131] like Figure 2 The flowchart shown is a node time slot optimization algorithm based on seepage diffusion.

[0132] In one optional implementation, the local clock information of the wireless measurement node and the data packet acquisition timestamp are recorded, the transmission delay value is calculated, and path optimization parameters are determined based on the transmission delay value and the congestion level value of the data transmission path. Updating the data transmission path includes:

[0133] Record the local clock information of the wireless measurement node and the data packet acquisition timestamp, calculate the clock deviation value between nodes, and calibrate the local clock of the measurement node based on the clock deviation value;

[0134] Calculate the processing time of the data packet within the node, the access time required to pass through the channel, the transmission time on the link, and the waiting time in the queue. Then, sum the processing time, access time, transmission time, and waiting time to obtain the transmission delay value.

[0135] A delay distribution function is constructed by statistically analyzing the mean and variance of historical transmission delay values. A delay threshold is set based on the delay distribution function, and transmission delay values ​​exceeding the delay threshold are marked as abnormal delay points.

[0136] Obtain the data buffer queue length of the node, calculate the processing capacity saturation of the node, monitor the packet loss rate and channel occupancy rate of data transmission, and calculate the congestion level value based on the processing capacity saturation, packet loss rate and channel occupancy rate;

[0137] A delay weighting factor is set based on the transmission delay value, a congestion penalty coefficient is determined based on the congestion level value, the delay weighting factor and the congestion penalty coefficient are used as path optimization parameters, and the optimal transmission path is selected based on the path optimization parameters.

[0138] After the optimal transmission path is determined, the transmission path of the data packet is redirected, the routing table of the node is updated, and the data transmission path is switched.

[0139] In one specific implementation, multiple measurement nodes are deployed in a wireless network environment to monitor data transmission status. Each node maintains a local clock to record the processing time of data packets. To accurately calculate transmission delay, the clock asynchrony between nodes needs to be addressed. When measurement node A sends a data packet, it records the sending time t1 in the packet header; after receiving the data packet, measurement node B records its own local clock receiving time t2. Simultaneously, node B replies to node A with an acknowledgment packet containing the receiving time t2, and node A receives the acknowledgment packet at time t3. Using these three timestamps, the clock deviation between the two nodes can be calculated. For example, if t1 is 10:00:00.000, t2 is 10:00:02.100, and t3 is 10:00:04.000, assuming symmetrical network transmission, the clock deviation of node B relative to node A can be calculated to be approximately 100 milliseconds. Based on this deviation, node B's local clock is adjusted to keep the clocks of all nodes in the network synchronized, with the error controlled within 10 milliseconds.

[0140] After clock calibration, the transmission delay of the data packets is calculated. Transmission delay consists of four parts: processing time, access time, transmission time, and waiting time. Processing time refers to the time it takes for a data packet to be processed within the node, obtained by recording the difference between the time the packet enters the processing unit and the time it completes processing; it is typically between 0.5 and 5 milliseconds, depending on the node's processing capacity. Access time refers to the time required for a data packet to wait for the channel to become idle; it is calculated by monitoring the backoff time in the CSMA / CA protocol and is typically between 0 and 15 milliseconds. Transmission time refers to the transmission time of the data packet on the physical link, related to the packet size and link bandwidth; for example, transmitting a 1000-byte data packet on a 100Mbps link takes approximately 0.08 milliseconds. Waiting time refers to the waiting time for the data packet in the node's buffer queue, related to the queue length and processing rate, and can range from a few milliseconds to hundreds of milliseconds. Adding these four parts together yields the total transmission delay of the data packet.

[0141] To identify abnormal latency in the network, historical transmission latency data is continuously collected, and its mean and variance are calculated. Taking a certain node as an example, the mean transmission latency of nearly 1000 data packets collected is 25 milliseconds, and the variance is 16. A latency distribution function is constructed based on this data. The system sets the latency threshold to the mean plus three times the standard deviation, i.e., 25 + 3 × 4 = 37 milliseconds. When a data packet with a transmission latency exceeding 37 milliseconds is detected, it is marked as an abnormal latency point and recorded in the anomaly log. These abnormal latency points usually indicate a deterioration in network conditions.

[0142] Simultaneously, network congestion is monitored. Each node maintains a data buffer queue, recording the ratio of the current queue length to the maximum queue capacity as the processing capacity saturation. For example, if a node's current queue length is 80 and its maximum queue capacity is 100, its processing capacity saturation is 80%. Packet loss rate is calculated through periodic probing; if 3 out of 100 probe packets do not receive a response, the packet loss rate is 3%. Channel occupancy is calculated by the proportion of time the channel is busy; for example, if the channel is occupied for 0.7 seconds in a 1-second observation window, the channel occupancy rate is 70%. Considering these three parameters, a congestion level value is calculated based on preset weights. Taking weights of 0.4, 0.3, and 0.3 as an example, when the processing capacity saturation is 80%, the packet loss rate is 3%, and the channel occupancy rate is 70%, the calculated congestion level value is 0.4 × 80% + 0.3 × 3% + 0.3 × 70% = 52.9%.

[0143] Based on the calculated transmission delay and congestion levels, path optimization parameters are set. For transmission delay, the system defines a non-linear delay weighting factor. For example, when the transmission delay is 15 milliseconds, the delay weighting factor is 0.6; when the delay is 30 milliseconds, the factor increases to 0.85; and when the delay exceeds 50 milliseconds, the factor approaches 1.0. For congestion levels, a corresponding congestion penalty coefficient is set. For example, when the congestion level is 30%, the penalty coefficient is 1.2; when the congestion level is 60%, the penalty coefficient increases to 1.6; and when the congestion level exceeds 80%, the penalty coefficient can reach 2.0 or higher. The path optimization parameters are obtained by multiplying the delay weighting factor by the congestion penalty coefficient.

[0144] Path optimization parameters are calculated for all possible transmission paths in the network, and the path with the smallest parameter value is selected as the optimal transmission path. After determining the optimal path, a path redirection operation is performed. Nodes update their routing tables, modifying the next-hop node for the specified destination address to the corresponding node on the optimal path. For example, if the original path is A→B→C→D and the new path is A→E→D, then in node A's forwarding table, the next hop for destination D is updated from B to E. After the routing table update is complete, new data packets will be transmitted according to the optimized path, thereby reducing transmission latency and improving network performance.

[0145] The real-time accurate measurement system for tree diameter at breast height (DBH) based on a low-power wireless network according to embodiments of the present invention includes:

[0146] The first unit is used to deploy wireless measurement nodes in forest areas, including a diameter at breast height sensor, a data processor, and a wireless communication module, which establishes a wireless measurement network through the wireless communication module;

[0147] The second unit is used to obtain the signal strength value of each wireless measurement node in the wireless measurement network, construct the network topology based on the signal strength value, calculate the data transmission path of each wireless measurement node, and allocate transmission time slots.

[0148] The third unit is used for the diameter at breast height (DBH) sensor to collect DBH data of trees based on the transmission time slot, and the data processor to form a measurement data packet with the DBH data, the collection time, and the node location, and send it to the adjacent wireless measurement node based on the data transmission path.

[0149] The fourth unit is used to record the local clock information of the wireless measurement node and the data packet acquisition timestamp, calculate the transmission delay value, determine the path optimization parameters based on the transmission delay value and the congestion level value of the data transmission path, and update the data transmission path.

[0150] The fifth unit is used to monitor the power level of each wireless measurement node. When the power level is lower than a preset power threshold, the data transmission path of the surrounding wireless measurement nodes is recalculated and the transmission time slot is updated.

[0151] The sixth unit is used to transmit the measurement data packet to the gateway along the updated data transmission path, generate a chest diameter growth trend report, and upload it to the cloud server.

[0152] A third aspect of the present invention provides an electronic device, comprising:

[0153] processor;

[0154] Memory used to store processor-executable instructions;

[0155] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0156] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0157] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for real-time and accurate measurement of tree diameter at breast height (DBH) based on a low-power wireless network, characterized in that, include: Wireless measurement nodes are deployed in forest areas, including diameter at breast height (DBH) sensors, data processors, and wireless communication modules, and a wireless measurement network is established through the wireless communication modules. Obtain the signal strength values ​​of each wireless measurement node in the wireless measurement network, construct the network topology based on the signal strength values, calculate the data transmission path of each wireless measurement node, and allocate transmission time slots; Based on the transmission time slot, the diameter at breast height (DBH) sensor collects DBH data of trees, and the data processor combines the DBH data with the collection time and node location to form a measurement data packet, which is then sent to the adjacent wireless measurement node based on the data transmission path. Record the local clock information of the wireless measurement node and the data packet acquisition timestamp, calculate the transmission delay value, determine the path optimization parameters based on the transmission delay value and the congestion level value of the data transmission path, and update the data transmission path; Monitor the battery level of each wireless measurement node. When the battery level is lower than a preset battery threshold, recalculate the data transmission path of the surrounding wireless measurement nodes and update the transmission time slot. The measurement data packet is transmitted to the gateway along the updated data transmission path, a chest diameter growth trend report is generated, and uploaded to the cloud server.

2. The method according to claim 1, characterized in that, Obtaining the signal strength values ​​of each wireless measurement node in the wireless measurement network, and constructing the network topology based on the signal strength values ​​includes: By transmitting probe frames at different transmission power levels through wireless measurement nodes, and receiving probe frames from other wireless measurement nodes, the signal strength value, reception time and signal-to-noise ratio of the probe frames are recorded to obtain initial signal data. The temperature, humidity and terrain parameters of the location of the wireless measurement node are collected, and the environmental correction factor is determined by multiplying the corresponding attenuation influence coefficients. The initial signal data is then corrected according to the environmental correction factor to obtain the corrected signal data. Multiple sets of corrected signal data are continuously collected within a preset time window. Abnormal samples that deviate from the mean by more than a preset range are removed, and a weighted moving average is calculated to generate a signal quality score. A connection weight matrix is ​​constructed based on signal quality scoring. The relative positional relationship and connection stability between wireless measurement nodes are calculated. Wireless measurement nodes with connection stability higher than a preset stability threshold are identified as key nodes. An initial backbone network is constructed with the key nodes as the core. Redundant links are added to the initial backbone network. The number of redundant links is determined according to the network connection density threshold to generate an optimized network topology. The system continuously monitors and optimizes changes in link quality and network load distribution in the network topology. When a link quality is detected to be lower than a preset quality threshold or a network load is detected to exceed a preset load threshold, the connection weight matrix is ​​recalculated and the network topology is updated.

3. The method according to claim 1, characterized in that, Calculating the data transmission path for each wireless measurement node and allocating transmission time slots includes: Based on the network topology, any two wireless measurement nodes are selected as the source node and the target node in sequence. All node combinations traversed from the source node to the target node are calculated, and the node sequence and path hop count for each node combination are recorded to generate a set of optional paths. The signal quality, bandwidth utilization, and packet loss rate of each link in the set of optional paths are obtained. The reliability score of each link is calculated. The weighted sum of the reliability scores of each link is used as the link quality coefficient. The remaining energy and forwarding load of each wireless measurement node are obtained. The path cost coefficient is calculated based on the energy consumption rate and the path hop count. The product of the link quality coefficient and the path cost coefficient is determined as the comprehensive path score. The paths with the highest and second-highest overall scores are selected as the primary transmission path and the backup transmission path, respectively. The network is divided into multiple layers based on the number of hops from the source node to the target node in the main transmission path. Basic time slots are allocated based on the data generation rate and forwarding data volume of each layer node, and emergency time slots are added after the basic time slots to form a time slot allocation table.

4. The method according to claim 3, characterized in that, The network is divided into multiple layers based on the number of hops from the source node to the destination node in the main transmission path. Basic time slots are allocated based on the data generation rate and forwarding data volume of each layer's nodes. Emergency time slots are added after the basic time slots to form a time slot allocation table, which includes: The network is divided into multiple layers based on the number of hops from the source node to the target node in the main transmission path, and the node set corresponding to each layer is recorded. Calculate the data generation rate of each node in the node set, and count the number of lower-level nodes forwarded by each node. Calculate the forwarded data volume of each node based on the product of the number of lower-level nodes and the data generation rate of the lower-level nodes. Add the data generation rate and forwarded data volume of each node to obtain the node transmission load value. The load fluctuation coefficient is determined based on the historical load standard deviation of each node, and the sum of the node's transmission load value and the load fluctuation coefficient is determined as the corrected load value. Based on the preset minimum time slot unit, the basic time slot number of each node is calculated according to the ratio of the modified load value to the scheduling cycle, and a corresponding number of basic time slots are allocated to each node. Obtain the communication range profile of each node, determine the node pairs with overlapping time slot positions in the basic time slot based on the communication range profile, use the percolation diffusion algorithm to reallocate different time slot positions to the node pairs, and perform the time slot position exchange operation of the nodes. The upper limit of the confidence interval is determined based on the historical burst data of each node, and the length of the emergency time slot is determined according to the ratio of the upper limit of the confidence interval to the scheduling period. The emergency time slot is then appended to the basic time slot of each level node in an interleaved distribution manner. The deviation between the actual transmission load of each node and the corrected load value is periodically detected. When the deviation exceeds a preset deviation threshold, the load fluctuation coefficient and the historical burst data are updated and the time slot allocation table is regenerated.

5. The method according to claim 4, characterized in that, Obtain the communication range profile of each node, determine the node pairs with overlapping time slot positions in the basic time slot based on the communication range profile, and reallocate different time slot positions to the node pairs using the percolation diffusion algorithm. The time slot position exchange operation of the nodes includes: The transmit power and received signal strength of each node are obtained, the communication range profile of each node is established, the spatial distance relationship between node pairs is calculated based on the communication range profile, and the geometric features of the overlapping area of ​​the communication range are determined. A time slot occupancy matrix is ​​constructed to record the basic time slot distribution of each node. Based on the time slot occupancy matrix, node pairs with overlapping time slot positions are detected. The duration of time slot overlap of the node pairs is calculated. The permeation probability of the node pairs is determined according to the geometric characteristics of the overlapping area of ​​the communication range and the duration of time slot overlap. The initial diffusion coefficient of the node pair is set according to the seepage probability and the duration of time slot overlap. The diffusion gradient between the node combination is calculated based on the initial diffusion coefficient, and the probability distribution function of node time slot migration is constructed. The node combination with the longest duration of time slot overlap is selected as the starting point. The time slot migration direction of the node is calculated based on the seepage probability and diffusion gradient, and the candidate time slot position of the node is updated. The local congestion level at the candidate time slot location is detected, and the diffusion coefficient is dynamically adjusted according to the local congestion level. When the adjustment of the diffusion coefficient exceeds the preset adjustment range, a time slot location rollback operation is performed. The load balancing degree between node pairs is calculated, the optimal switching node pair is determined, the time slot switching execution order of the optimal switching node pair is planned, and the node time slot position switching operation is completed under the guidance of the time slot switching execution order.

6. The method according to claim 1, characterized in that, Record the local clock information of the wireless measurement node and the data packet acquisition timestamp, calculate the transmission delay value, determine path optimization parameters based on the transmission delay value and the congestion level value of the data transmission path, and update the data transmission path, including: Record the local clock information of the wireless measurement node and the data packet acquisition timestamp, calculate the clock deviation value between nodes, and calibrate the local clock of the measurement node based on the clock deviation value; Calculate the processing time of the data packet within the node, the access time required to pass through the channel, the transmission time on the link, and the waiting time in the queue. Then, sum the processing time, access time, transmission time, and waiting time to obtain the transmission delay value. A delay distribution function is constructed by statistically analyzing the mean and variance of historical transmission delay values. A delay threshold is set based on the delay distribution function, and transmission delay values ​​exceeding the delay threshold are marked as abnormal delay points. Obtain the data buffer queue length of the node, calculate the processing capacity saturation of the node, monitor the packet loss rate and channel occupancy rate of data transmission, and calculate the congestion level value based on the processing capacity saturation, packet loss rate and channel occupancy rate; A delay weighting factor is set based on the transmission delay value, a congestion penalty coefficient is determined based on the congestion level value, the delay weighting factor and the congestion penalty coefficient are used as path optimization parameters, and the optimal transmission path is selected based on the path optimization parameters. After the optimal transmission path is determined, the transmission path of the data packet is redirected, the routing table of the node is updated, and the data transmission path is switched.

7. A real-time accurate measurement system for tree diameter at breast height (DBH) based on a low-power wireless network, used to implement the method described in any one of claims 1-6, characterized in that, include: The first unit is used to deploy wireless measurement nodes in forest areas, including a diameter at breast height sensor, a data processor, and a wireless communication module, which establishes a wireless measurement network through the wireless communication module; The second unit is used to obtain the signal strength value of each wireless measurement node in the wireless measurement network, construct the network topology based on the signal strength value, calculate the data transmission path of each wireless measurement node, and allocate transmission time slots. The third unit is used for the diameter at breast height (DBH) sensor to collect DBH data of trees based on the transmission time slot, and the data processor to form a measurement data packet with the DBH data, the collection time, and the node location, and send it to the adjacent wireless measurement node based on the data transmission path. The fourth unit is used to record the local clock information of the wireless measurement node and the data packet acquisition timestamp, calculate the transmission delay value, determine the path optimization parameters based on the transmission delay value and the congestion level value of the data transmission path, and update the data transmission path. The fifth unit is used to monitor the power level of each wireless measurement node. When the power level is lower than a preset power threshold, the data transmission path of the surrounding wireless measurement nodes is recalculated and the transmission time slot is updated. The sixth unit is used to transmit the measurement data packet to the gateway along the updated data transmission path, generate a chest diameter growth trend report, and upload it to the cloud server.

8. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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