Method and system for wireless ad hoc network transmission of monitoring data of oil tank vocs

Abstract

This application provides a wireless ad hoc network transmission method and system for VOCs monitoring data from oil storage tanks, relating to the field of wireless transmission technology. The method includes: deploying monitoring nodes in the tank area to collect VOCs monitoring data and meteorological data, and deploying relay nodes to collect wind speed and direction data; correcting the VOCs monitoring data based on the meteorological data to generate a data packet to be transmitted, and simultaneously generating link state parameters based on the wind speed and direction data; then determining the stability parameters of the wireless link based on the link state parameters and constructing a dynamic network topology; selecting a first transmission path for the data packet to be transmitted; and finally updating the stability parameters when the number of consecutive wind direction data changes exceeds a preset threshold, selecting a second transmission path, and switching the data packet to be transmitted to the second transmission path, thus completing the wireless ad hoc network transmission of VOCs monitoring data. This application improves the continuity and reliability of data transmission under complex meteorological conditions.

Description

Technical Field

[0001] This application relates to the field of wireless transmission technology, and in particular to a wireless self-organizing network transmission method and system for monitoring volatile organic compounds (VOCs) in oil storage tanks. Background Technology

[0002] As a significant source of volatile organic compound emissions, the real-time and accurate monitoring data of oil storage tank areas are crucial for safe production and environmental protection. Wireless ad hoc networking technology, with its advantages of flexible deployment and controllable cost, has shown broad application prospects in data acquisition and transmission scenarios in large tank areas.

[0003] In existing wireless monitoring solutions for oil tank areas, monitoring nodes are typically deployed on the surface of the tank or in the surrounding area. They are responsible for collecting gas concentration data and transmitting the data back to the central server via multi-hop relay. Some solutions also deploy fixed relay nodes at high points to extend network coverage, thereby forming a basic wireless self-organizing network transmission link.

[0004] However, the environment in tank farms is complex and variable. Large metal tanks significantly block wireless signals, and drastic weather conditions further exacerbate the problem, leading to significant fluctuations in the communication quality of wireless links. This results in packet loss and increased latency during data transmission. Therefore, existing technologies suffer from insufficient reliability of wireless ad hoc networks in complex environments. Summary of the Invention

[0005] This application provides a wireless self-organizing network transmission method and system for VOCs monitoring data from oil storage tanks, which solves the problems of poor continuity and low reliability of data transmission under complex meteorological conditions in the prior art.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a wireless ad hoc network transmission method for VOCs monitoring data from oil storage tanks, comprising:

[0007] Monitoring nodes are deployed in the tank area to collect VOCs monitoring data and meteorological data from the oil tanks. At least one relay node is deployed at a predetermined location in the tank area, in addition to the monitoring nodes, to collect wind speed and wind direction data.

[0008] Based on the meteorological data, the VOCs monitoring data is corrected, a time stamp is added to the corrected VOCs monitoring data, a data packet to be transmitted is generated, and link status parameters are generated based on the wind speed data and the wind direction data.

[0009] Based on the link state parameters, the stability parameters of the wireless link are determined by a dynamic topology discovery algorithm, and a dynamic network topology is constructed based on the stability parameters.

[0010] Based on the dynamic network topology, an adaptive routing algorithm is used to select a first transmission path for the data packet to be transmitted.

[0011] When the number of times the change in wind direction data exceeds a preset change threshold reaches a preset number, the stability parameter is updated. Using the updated stability parameter, a second transmission path is selected for the data packet to be transmitted, and the transmission path is switched from the first transmission path to the second transmission path to complete the wireless self-organizing network transmission of the VOCs monitoring data.

[0012] Secondly, this application provides a wireless self-organizing network transmission system for VOCs monitoring data of oil storage tanks, comprising:

[0013] The data acquisition module is used to deploy monitoring nodes in the tank area to collect VOCs monitoring data and meteorological data of the oil storage tanks, and to deploy at least one relay node at a predetermined location in the tank area other than the monitoring nodes to collect wind speed data and wind direction data.

[0014] The correction module is used to correct the VOCs monitoring data based on the meteorological data, add a time stamp to the corrected VOCs monitoring data, generate a data packet to be transmitted, and generate link status parameters based on the wind speed data and the wind direction data.

[0015] The determination module is used to determine the stability parameters of the wireless link based on the link state parameters using a dynamic topology discovery algorithm, and to construct a dynamic network topology based on the stability parameters.

[0016] The selection module is used to select a first transmission path for the data packet to be transmitted based on the dynamic network topology using an adaptive routing algorithm.

[0017] The update module is used to update the stability parameter when the number of times the change in the wind direction data exceeds the preset change threshold reaches a preset number. Using the updated stability parameter, a second transmission path is selected for the data packet to be transmitted, and the transmission path is switched from the first transmission path to the second transmission path to complete the wireless self-organizing network transmission of the VOCs monitoring data.

[0018] Thirdly, this application provides an electronic device, comprising:

[0019] Memory, used to store computer programs;

[0020] A processor is configured to execute the computer program to implement the steps of the wireless ad hoc network transmission method for VOCs monitoring data of oil storage tanks as described in the first aspect above.

[0021] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the wireless self-organizing network transmission method for VOCs monitoring data of oil storage tanks as described in the first aspect above.

[0022] The technical solution provided in this application has the following beneficial effects:

[0023] This application deploys two types of nodes in the tank area to collect environmental and gas data respectively. Meteorological data is used to correct the monitoring results to ensure data accuracy. At the same time, time stamps are added to the data packets for subsequent traceability, and wind speed and direction information is converted into link status parameters to evaluate communication quality. Then, based on the link status parameters, the stability parameters of each link are determined and a dynamic network topology is constructed so that the network structure can reflect the current environmental conditions. Then, based on the dynamic network topology, the initial transmission path is selected for the data packets to ensure that the data is transmitted on a stable link. Finally, when the wind direction changes continuously to the set conditions, the stability parameters are updated in a timely manner and the transmission path is switched to a new one, so that the data transmission path can be dynamically adjusted with environmental changes, ultimately achieving reliable backhaul of VOCs monitoring data in the complex tank area environment.

[0024] Furthermore, in the constructed dynamic network topology, this application also determines all candidate transmission paths from the source node to the target node and counts the number of intermediate nodes in each path as the path hop count. At the same time, it accumulates the stability parameters of the links traversed by each path to obtain the total path stability value. Then, based on the path hop count and the total stability value, it calculates the basic score of each candidate path. Then, it introduces fuzzy logic rules to generate a dynamic adjustment factor based on the current network load rate to correct the basic score. Finally, it obtains the comprehensive cost score of each path and selects the path with the lowest score as the first transmission path for data transmission.

[0025] Furthermore, by comprehensively considering multiple factors such as path hop count, link stability, and network load, and using fuzzy logic for dynamic weighting, the transmission path with the best overall quality can be selected from numerous candidate paths. This ensures that data arrives quickly while effectively avoiding congested nodes and unstable links, thereby improving the transmission efficiency and stability of wireless ad hoc networks under complex operating conditions.

[0026] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A flowchart illustrating a wireless ad hoc network transmission method for VOCs monitoring data from an oil storage tank, provided in this application embodiment;

[0029] Figure 2 A schematic diagram illustrating a specific implementation of a wireless ad hoc network transmission method for VOCs monitoring data from an oil storage tank, provided in this application embodiment;

[0030] Figure 3 This is a schematic diagram of a wireless self-organizing network transmission system for VOCs monitoring data of an oil storage tank, provided in an embodiment of this application. Detailed Implementation

[0031] In the wireless transmission scenario of volatile organic compound (VOC) monitoring data in oil tank areas, existing ad hoc network solutions typically rely on statically deployed relay nodes and fixed routing strategies to complete multi-hop data backhaul through preset paths. However, tank areas have numerous metal tanks that complexly obstruct wireless signals, and the real-time changes in wind direction and speed in open-air environments cause significant spatiotemporal fluctuations in the communication quality of wireless links. This can easily lead to increased packet loss rates or exacerbated latency fluctuations during data transmission, making it difficult to guarantee stable and reliable data backhaul under complex weather conditions.

[0032] To address the aforementioned issues, this application proposes a wireless ad hoc network transmission method for VOCs monitoring data from oil storage tanks. Its core lies in integrating environmental perception with network topology construction to achieve dynamic adaptation of the transmission path to meteorological changes. Specifically, by deploying monitoring nodes in the tank area to collect gas concentration and meteorological data, and simultaneously deploying relay nodes to acquire real-time wind speed and direction information, the monitoring results are first corrected using meteorological data to ensure data accuracy. Wind speed and direction are then converted into link state parameters for evaluating communication quality. Based on these parameters, a network topology reflecting the current environmental state is dynamically constructed, and an initial transmission path is selected for data packets accordingly. When the wind direction continuously changes to meet set conditions, the link state is updated promptly, and a new transmission path is switched.

[0033] Therefore, this method transforms the original static wireless network into a dynamic system that can sense and respond to environmental changes, so that the data transmission path is always adjusted according to the wind direction. This fundamentally solves the problem of insufficient transmission reliability caused by the fluctuation of meteorological conditions in the existing technology, and improves the stability of VOCs monitoring data transmission in tank farms under complex environments.

[0034] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0035] The core of this application is to provide a wireless self-organizing network transmission method for VOCs monitoring data from oil storage tanks, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0036] Step 101: Deploy monitoring nodes in the tank area to collect VOCs monitoring data and meteorological data from the oil storage tanks. Deploy at least one relay node at a predetermined location in the tank area, excluding the monitoring nodes, to collect wind speed and wind direction data.

[0037] In step 101, the monitoring nodes are data acquisition devices deployed near each oil storage tank in the tank area to acquire volatile organic compound monitoring data and meteorological data; the relay nodes are relay devices deployed at high points or open locations within the tank area, with at least one relay node, to acquire wind speed data and wind direction data.

[0038] Volatile organic compound (VOC) monitoring data refers to concentration information reflecting the gas leakage status of oil storage tanks. Meteorological data refers to environmental information related to gas sensor readings, including temperature and humidity data. Wind speed and wind direction data refer to physical quantities reflecting the real-time airflow status of the tank area.

[0039] In this embodiment, multiple monitoring nodes are first deployed around each oil storage tank in the tank area. These monitoring nodes collect volatile organic compound monitoring data, as well as synchronous temperature and humidity data from the oil storage tanks. At the same time, at least one relay node is deployed at a key location in the tank area other than the monitoring nodes. This relay node is responsible for collecting real-time wind speed and wind direction data, thereby providing basic input for subsequent data correction and network evaluation.

[0040] Step 102: Based on the meteorological data, correct the VOCs monitoring data, add a time stamp to the corrected VOCs monitoring data, generate a data packet to be transmitted, and generate link status parameters based on the wind speed data and the wind direction data.

[0041] Among them, the time stamp refers to the timestamp information that records the time when the data packet is generated, the data packet to be transmitted refers to the data structure unit that encapsulates the corrected monitoring data and the time stamp and is ready to be sent to the network, and the link status parameter is a quantitative indicator used to characterize the quality of the wireless communication link under the current meteorological conditions.

[0042] In this embodiment, step 102 includes the following process:

[0043] Step 1021: Extract temperature and humidity data from the meteorological data.

[0044] In step 1021, the temperature data is the ambient temperature value synchronously collected by the monitoring node, and the humidity data is the ambient humidity value synchronously collected by the monitoring node.

[0045] In this embodiment of the application, temperature data and humidity data are first extracted from the meteorological data collected by the monitoring node. These two types of data will serve as the basis for subsequent correction of the gas concentration measurement results.

[0046] Step 1022: Adjust the concentration values ​​in the VOCs monitoring data according to the preset compensation relationship, the temperature data and the humidity data to obtain the corrected VOCs monitoring data, and add a time stamp to the corrected VOCs monitoring data to form a data packet to be transmitted.

[0047] In step 1022, the preset compensation relationship refers to the temperature compensation coefficient table and humidity compensation coefficient table stored in the monitoring node memory.

[0048] The temperature compensation coefficient table contains the concentration compensation value corresponding to every 5 degrees Celsius in the temperature range from -20 degrees Celsius to 50 degrees Celsius, and the humidity compensation coefficient table contains the concentration compensation value corresponding to every 10 percent in the relative humidity range from 10 percent to 90 percent.

[0049] In actual correction, firstly, the temperature compensation coefficient table is consulted based on the currently collected temperature data. The compensation values ​​corresponding to the two temperature nodes closest to the current temperature value are found and linear interpolation is performed to obtain the temperature compensation value at the current temperature. Then, the humidity compensation coefficient table is consulted based on the currently collected humidity data. The compensation values ​​corresponding to the two humidity nodes closest to the current humidity value are found and linear interpolation is performed to obtain the humidity compensation value at the current humidity. Finally, the original concentration reading is subtracted from the temperature compensation value and then from the humidity compensation value to obtain the corrected volatile organic compound monitoring data.

[0050] In this embodiment of the application, the original concentration values ​​in the volatile organic compound monitoring data are calculated and adjusted using the extracted temperature and humidity data according to a preset compensation relationship. After eliminating the interference of temperature and humidity, the corrected volatile organic compound monitoring data is obtained. Then, a time stamp for recording the generation time is added to the corrected data and it is encapsulated to finally form a data packet to be transmitted over the network.

[0051] Step 1023: Based on the wind speed data and the wind direction data, query the preset wind speed and signal attenuation relationship mapping table to obtain the basic evaluation value.

[0052] In step 1023, the preset wind speed and signal attenuation mapping table is a data table that records the correspondence between different wind speed values ​​and the degree of wireless signal propagation loss. The preset wind speed and signal attenuation mapping table is pre-established based on the theoretical model disclosed in the prior art and combined with the on-site measured data of this tank area. It can be set according to the actual situation, and this application does not limit it. The basic evaluation value refers to the preliminary score of the degree of wireless signal attenuation determined only by the wind speed.

[0053] It should be noted that the specific form of the preset wind speed and signal attenuation relationship mapping table is not limited in the embodiments of this application, and can be set accordingly according to the actual situation.

[0054] In this embodiment of the application, wind speed data and wind direction data collected by the relay node are used as input. First, a preset wind speed and signal attenuation relationship mapping table is queried according to the wind speed value in the wind speed data. The signal attenuation degree corresponding to the wind speed value is matched from the table as a preliminary score as the basic evaluation value.

[0055] Step 1024: Based on the wind direction angle in the wind direction data and the geographical coordinates of the current node, determine the direction of signal propagation, and adjust the basic evaluation value based on the direction of propagation to obtain the link state parameters.

[0056] In step 1024, the geographic location coordinates refer to the specific location information of the node within the tank area, and the direction status refers to the qualitative result of whether the wireless signal propagation is favorable or unfavorable based on the current wind direction.

[0057] In this embodiment, the wind direction angle in the wind direction data is analyzed in combination with the geographical coordinates of the current node. The direction of signal propagation at the current moment is determined by judging the relationship between the wind direction angle and the direction of the connection between nodes. Then, the previously obtained basic evaluation value is adjusted accordingly based on the direction. That is, the score is increased when the wind is downwind and decreased when the wind is upwind. Finally, the adjusted score result is determined as the link state parameter.

[0058] Specific example: The adjustment of the basic evaluation value based on the directional status is achieved through a preset directional adjustment coefficient. First, the directional status is divided into three intervals based on the angle between the wind direction and the connection direction: an angle from 0 to 30 degrees is defined as a tailwind state, an angle from 30 to 150 degrees as a crosswind state, and an angle from 150 to 180 degrees as a headwind state. The adjustment coefficient for the tailwind state is 1.2, for the crosswind state it is 1.0, and for the headwind state it is 0.8. During the adjustment calculation, the basic evaluation value obtained in step 1023 is directly multiplied by the adjustment coefficient corresponding to the current directional status; the product is the final link status parameter.

[0059] This application compensates and corrects gas concentration measurement results by using temperature and humidity data, and quantifies wind speed and direction information into link state parameters that can participate in network evaluation, thereby improving the environmental adaptability of the original monitoring data and providing a quantitative basis for the dynamic construction of subsequent network topology.

[0060] Step 103: Based on the link state parameters, determine the stability parameters of the wireless link through a dynamic topology discovery algorithm, and construct a dynamic network topology based on the stability parameters.

[0061] Among them, a wireless link refers to the connection channel between any two nodes in the network that can communicate directly. The stability parameter is a score used to quantitatively evaluate the reliability of the wireless link under the current environmental conditions. The dynamic network topology refers to the network structure diagram that reflects the current connection relationship and connection quality between nodes, constructed based on the real-time updated stability parameters.

[0062] In this embodiment, step 103 includes the following process:

[0063] Step 1031: At a preset time interval, the monitoring node and the relay node send signaling data. After receiving the signaling data, any other node determines the connection direction based on the location data of the corresponding node and the location data in the signaling data.

[0064] In step 1031, the preset time interval refers to the fixed duration between two adjacent signaling transmissions, the signaling data refers to the data that the node broadcasts periodically and contains its own status information, the location data refers to the coordinate information of the node in the tank area, and the connection direction refers to the spatial orientation line from the receiving node to the sending node.

[0065] In this embodiment, each monitoring node and each relay node first broadcast signaling data containing their own location data to the outside world at preset time intervals. When any other node in the network receives the signaling data, the receiving node calculates the connection direction from the receiving node to the sending node by connecting the coordinates of the two points based on the location data of the corresponding node stored in its own memory and the location data of the sending node carried in the signaling data. Each of the monitoring node and relay node needs to perform the operation of sending signaling data. Other nodes refer to nodes other than themselves.

[0066] In practical applications, assuming the position coordinates of node A are (xa, ya) and the position coordinates of node B are (xb, yb), after receiving the signaling data sent by node A, node B calculates the connection direction vector from node B to node A as [xa-xb, ya-yb] based on its own coordinates and the coordinates of node A.

[0067] Step 1032: Compare the connection direction with the wind direction data in the signaling data to obtain the direction angle.

[0068] In step 1032, the directional angle refers to the angle between the connecting direction and the wind direction.

[0069] In this embodiment of the application, the calculated connection direction is compared with the wind direction data of the sending node carried in the signaling data. The direction angle is obtained by calculating the angle between the connection direction line and the wind direction line. This direction angle is used to reflect the angular relationship between the current wind direction and the communication link between the two nodes.

[0070] In practical applications, continuing the previous example, node B compares the calculated connection direction vector with the wind direction angle carried in the signaling of node A, and calculates the angle θ between the two directions using the vector dot product formula. The closer the connection direction is to the wind direction, the smaller the angle.

[0071] It should be noted that this embodiment does not specifically limit the specific expression used in the vector dot product formula; it can be set according to the actual situation.

[0072] Step 1033: Based on the angle of the direction, query the preset angle-stability relationship table to obtain the stability parameters.

[0073] In step 1033, the preset angle and stability relationship table is a data table that records the correspondence between different angle intervals and the corresponding link stability scores.

[0074] It should be noted that the specific form of the preset angle-stability relationship table is not limited in the embodiments of this application, and can be set accordingly according to the actual situation.

[0075] For example, the angle-stability relationship table is pre-established based on a combination of wireless signal propagation theory and field measurement data. First, representative node pairs are selected within the tank area, and signal strength testing equipment is deployed on each node to record the angle between the connection direction between different node pairs and the current wind direction. Simultaneously, the actual received signal strength and packet loss rate at the corresponding angle are measured. Then, based on the measured data, the distribution pattern of signal quality within different angle intervals is statistically analyzed. The angle from 0 degrees to 180 degrees is divided into several continuous intervals, each corresponding to a stability score. Intervals with smaller angles indicate that the communication direction is more consistent with the wind direction, the signal is less affected by atmospheric turbulence, and multipath fading is weaker, thus receiving a higher stability score. Intervals with larger angles indicate that the communication direction is opposite to or perpendicular to the wind direction, and the signal is more significantly affected by environmental disturbances, thus receiving a lower stability score. Finally, the correspondence between angle intervals and stability scores is compiled into a table and stored in the node's memory for subsequent real-time queries.

[0076] An exemplary table showing the relationship between included angle and stability is shown in Table 1:

[0077] Table 1 Relationship between included angle and stability

[0078] Angle range (degrees) Stability score (points) Greater than or equal to 0 degrees and less than 30 degrees 90 30 degrees or greater and less than 60 degrees 70 60 degrees or greater and less than 90 degrees 50 Greater than or equal to 90 degrees and less than or equal to 180 degrees 30

[0079] In this embodiment of the application, a preset angle-stability relationship table is queried based on the calculated directional angle. The stability score corresponding to the interval of the angle value is found in the table, and the score is determined as the stability parameter of the wireless link between the current receiving node and the signaling sending node. The smaller the angle, the higher the corresponding stability score.

[0080] In practical applications, assuming that the preset angle and stability relationship table specifies that the stability score is 90 points when the angle is between 0 and 30 degrees, 70 points when the angle is between 30 and 60 degrees, 50 points when the angle is between 60 and 90 degrees, and 30 points when the angle is greater than 90 degrees, if the angle calculated in step 1032 is 25 degrees, then the stability parameter of the link obtained by querying is 90 points.

[0081] Step 1034: Based on the dynamic topology discovery algorithm, repeat the above process to obtain and update the stability parameters of all node pairs in the network. One node pair corresponds to one wireless link.

[0082] In step 1034, a node pair refers to a combination of two nodes in the network that can communicate with each other.

[0083] In this embodiment of the application, according to the control logic of the dynamic topology discovery algorithm, the above-mentioned signaling transmission, direction calculation, angle comparison and stability score acquisition process are repeatedly executed throughout the entire network, so that each monitoring node and relay node establishes a corresponding node pair with all other nodes within its communication range, and calculates a real-time stability parameter for the wireless link corresponding to each node pair, while continuously acquiring the latest wind direction data and updating the stability parameters of all node pairs accordingly.

[0084] In practical applications, assuming there are 5 nodes in the network, the stability parameters of 10 wireless links between nodes 1 and 2, 1 and 3, and so on, are calculated by repeating the above process. These parameter values ​​are then updated in real time as the wind direction changes.

[0085] Step 1035: Based on the updated stability parameters of all node pairs, apply the minimum spanning tree algorithm to generate the dynamic network topology.

[0086] In this embodiment, all monitoring nodes and relay nodes in the network are taken as vertices of the graph, the wireless links between all node pairs are taken as edges of the graph, and the updated stability parameter corresponding to each link is taken as the weight of the edge. The minimum spanning tree algorithm is applied to calculate the weighted graph, and the set of edges that can connect all vertices and have the smallest sum of weights of all edges is selected. The current dynamic network topology is generated based on the set of edges, which is the basic network structure for subsequent data transmission.

[0087] In practical applications, five nodes are used as vertices, and the stability parameters of ten wireless links are used as the weights of the corresponding edges. After running the minimum spanning tree algorithm, a tree structure consisting of four edges is obtained. The wireless links corresponding to these four edges are the effective connection channels in the current dynamic network topology.

[0088] This application uses periodic signaling information exchange between nodes and dynamically calculates link stability scores based on wind direction data. Then, it applies the minimum spanning tree algorithm to construct the network topology, enabling the network structure to reflect the impact of environmental changes on communication quality in real time, thus providing a reliable basic network model for subsequent path selection.

[0089] Step 104: Based on the dynamic network topology, select a first transmission path for the data packet to be transmitted using an adaptive routing algorithm.

[0090] The first transmission path refers to the communication channel initially selected between the data source node and the target node for transmitting the data packets to be transmitted.

[0091] In this embodiment, such as Figure 2 As shown, step 104 includes the following process:

[0092] Step 1041: In the dynamic network topology, determine all candidate transmission paths from the source node to the target node, and count the number of intermediate nodes traversed by each candidate transmission path as the path hop value.

[0093] In step 1041, the source node refers to the node that initiates the transmission of the data packet to be transmitted, the target node refers to the final receiving node that the data packet to be transmitted needs to reach, the candidate transmission path refers to all possible node sequences that can reach the target node from the source node in the dynamic network topology, the intermediate node refers to the node that performs the data forwarding function between the source node and the target node, and the path hop value refers to the number of intermediate nodes traversed by a candidate transmission path plus 1, representing the number of transmission segments that the path needs to traverse.

[0094] In this embodiment of the application, firstly, in the dynamic network topology constructed in step 103, the monitoring node that generates the data packet to be transmitted is taken as the source node, and the central node to which the data needs to be finally delivered is taken as the target node. By traversing the dynamic network topology, all node sequences that can reach the target node from the source node are found as candidate transmission paths. Then, the number of intermediate nodes contained in each candidate transmission path is counted, and the number of intermediate nodes is incremented by 1 to obtain the path hop value corresponding to each candidate transmission path.

[0095] In practical applications, assume that the dynamic network topology contains 5 nodes: S, A, B, C, and D. Node S is the source node and node D is the target node. By traversing the topology, all possible paths from S to D are found, including 4 candidate transmission paths: SAD, SBD, SCD, and SABD. After counting the number of intermediate nodes for each path, the path hop values ​​are 2, 2, 2, and 3, respectively.

[0096] Step 1042: Accumulate the stability parameters of all communication links included in each candidate transmission path to obtain the total path stability value.

[0097] In step 1042, a communication link refers to the wireless connection channel between two adjacent nodes in a candidate transmission path, and the total path stability value refers to the sum of the stability parameters of all communication links contained in a candidate transmission path.

[0098] In this embodiment of the application, for each candidate transmission path, the stability parameters corresponding to the communication links formed between every two adjacent nodes traversed by the path are extracted from the dynamic network topology, and these stability parameters are summed up, with the summation result used as the total path stability value of the candidate transmission path.

[0099] In practical applications, for a candidate transmission path SAD, assuming that the stability parameter of link SA is 90 points and the stability parameter of link AD is 80 points obtained from the dynamic network topology, the total path stability value of this path is 90 + 80 = 170 points; similarly, the total path stability value of other paths is calculated.

[0100] Step 1043: Calculate the basic score for each candidate transmission path based on the path hop value and the total path stability value.

[0101] In step 1043, the base score refers to the preliminary assessment value of path quality obtained by comprehensively considering the number of path hops and path stability.

[0102] In this embodiment of the application, the average hop stability score of the path is obtained by comprehensively calculating the path hop value and the total path stability value of each candidate transmission path, and the average hop stability score is used as the basic score of the candidate transmission path.

[0103] In practical applications, for a candidate transmission path SAD, its path hop value is 2 and the total path stability value is 170 points, so its basic score is 170 / 2=85 points.

[0104] Step 1044: Introduce a preset fuzzy logic rule and apply a dynamic adjustment factor to the basic score to obtain a comprehensive cost score for each candidate transmission path, wherein the dynamic adjustment factor is calculated based on the current network load rate.

[0105] Among them, fuzzy logic rules refer to a set of reasoning rules based on fuzzy set theory for processing uncertain information, dynamic adjustment factors are multiplier coefficients calculated based on the current network load to correct the basic score, and comprehensive cost score refers to the comprehensive evaluation value obtained after fuzzy logic adjustment for final path comparison.

[0106] Step 1044 may specifically include the following steps:

[0107] A1: Calculate the current network load rate by statistically analyzing the length of the data packet sending queue and the data packet sending frequency of the source node within a preset time period.

[0108] In step A1, the data packet sending queue length refers to the number of data packets that the source node is currently waiting to send, the data packet sending frequency refers to the number of times the source node sends data packets per unit time, and the current network load rate refers to a quantitative indicator reflecting the current communication busyness of the source node.

[0109] In this embodiment, the average length of the data packet sending queue and the average frequency of data packet sending of the source node within the most recent preset time window are first counted. Then, the queue length and sending frequency are normalized and weighted and summed. The calculation result is used as the current network load rate at the current moment.

[0110] In practical applications, assuming a preset time window of 10 seconds, the average sending queue length of source node S is statistically determined to be 5 data packets, and the average sending frequency is 2 times per second. This can be achieved using the formula... Calculation, where This indicates the current network load rate. This represents the average send queue length of the source node. This represents the average transmission frequency, and the current network load rate is obtained. .

[0111] A2: Map the current network load rate to fuzzy logic input variables, the input variables including a first fuzzy set representing the load level.

[0112] In step A2, the first fuzzy set refers to the set of membership functions that divide the network load rate into different load levels, such as fuzzy subsets like low load, medium load, and high load.

[0113] In this embodiment of the application, the calculated current network load rate is input into the fuzzy logic system, and the specific value is mapped to the membership value of the fuzzy subset of each load level through a preset membership function, forming a fuzzy input variable for subsequent reasoning.

[0114] In practical applications, assuming that the preset low load membership function stipulates that when the load rate is less than 2, the membership degree of low load is 1, and when the load rate is between 2 and 3, the membership degree decreases linearly from 1 to 0, then for a load rate of 2.6, the calculated membership degree of low load is 0.4, the membership degree of medium load is 0.6, and the membership degree of high load is 0.

[0115] It should be noted that this embodiment does not specifically limit the specific expression used for the preset low-load membership function, and it can be set according to the actual situation.

[0116] A3: Reasoning is performed based on a preset fuzzy rule base, which contains multiple fuzzy logic rules that take the first fuzzy set as a condition and a dynamic adjustment factor as a conclusion.

[0117] In step A3, the fuzzy rule base refers to a pre-defined set of rules consisting of several fuzzy implication statements in the form of "if the condition is true, then the conclusion is true".

[0118] In this embodiment of the application, the membership values ​​of the input variables obtained in step A2 on each fuzzy subset are substituted into the preset fuzzy rule base, and fuzzy inference is performed based on the activation intensity of each rule to infer the membership distribution of the conclusion variable, i.e., the dynamic adjustment factor, on each output fuzzy subset.

[0119] In practical applications, assuming the fuzzy rule base contains the rules "if the load is low, the adjustment factor is high", "if the load is medium, the adjustment factor is medium", and "if the load is high, the adjustment factor is low", based on the membership values ​​in step A2, the activation strength of the low-load rule is 0.4, the activation strength of the medium-load rule is 0.6, and the activation strength of the high-load rule is 0. Therefore, the membership values ​​of the adjustment factor belonging to high, medium, and low are 0.4, 0.6, and 0, respectively.

[0120] A4: Defuzzify the reasoning results to obtain the value of the dynamic adjustment factor.

[0121] In step A4, defuzzification refers to the computational process of converting the fuzzy conclusions obtained from fuzzy reasoning into precise numerical values.

[0122] In this embodiment of the application, the fuzzy inference result of the dynamic adjustment factor obtained in step A3 is defuzzified. Typically, the centroid method or the maximum membership method is used to convert the output fuzzy set into a specific numerical value, which is the dynamic adjustment factor used for subsequent calculations.

[0123] In practical applications, assuming the output universe of discourse is between 0 and 2, and the center values ​​of the three output fuzzy subsets (high, medium, and low) are 1.8, 1.0, and 0.2 respectively, the dynamic adjustment factor calculated using the centroid method is 0.4×1.8+0.6×1.0+0×0.2 / 0.4+0.6+0=1.32.

[0124] A5: Multiply the value of the dynamic adjustment factor with the base score to obtain the product result, and determine the product result as the comprehensive cost score of each candidate transmission path.

[0125] In this embodiment of the application, the dynamic adjustment factor value calculated in step A4 is multiplied by the basic score obtained in step 1043 to obtain the comprehensive cost score after network load status correction. The lower the comprehensive cost score, the higher the overall quality of the path under the current network and environmental conditions.

[0126] In practical applications, for a candidate transmission path SAD, its base score is 85 points and its dynamic adjustment factor is 1.32, so its comprehensive cost score is 85 × 1.32 = 112.2 points.

[0127] Step 1045: Using an adaptive routing algorithm, select the path with the lowest overall cost score from all candidate transmission paths, determine it as the first transmission path, and send the data packet to be transmitted through the first transmission path.

[0128] In this embodiment of the application, the comprehensive cost scores of all candidate transmission paths calculated in step 1044 are compared by an adaptive routing algorithm, and the candidate transmission path with the smallest comprehensive cost score is selected as the first transmission path. Then, the data packet to be transmitted generated in step 102 is forwarded sequentially through the node sequence contained in the first transmission path, and finally the data packet is sent to the target node.

[0129] In practical applications, assuming that the comprehensive cost scores of the four candidate transmission paths SAD, SBD, SCD, and SABD are 112.2, 105.6, 118.3, and 135.7 respectively, the SBD path with the lowest score is selected as the first transmission path, and the data packets to be transmitted are sent according to the path from node S to node B and then to node D.

[0130] This application comprehensively considers multiple factors such as path hop count, link stability, and network load, and introduces fuzzy logic rules for dynamic weighting. This enables the selection of the transmission path with the best overall quality under complex operating conditions, effectively improving the data transmission efficiency and stability of wireless ad hoc networks.

[0131] Step 105: When the number of times the change in wind direction data exceeds the preset change threshold reaches a preset number, update the stability parameter, use the updated stability parameter to select a second transmission path for the data packet to be transmitted, and switch from the first transmission path to the second transmission path to complete the wireless self-organizing network transmission of the VOCs monitoring data.

[0132] Among them, the preset change threshold refers to the pre-set critical value of angle change used to determine whether the wind direction has changed significantly, the preset number of times refers to the pre-set requirement for the number of consecutive occurrences to confirm the continuous change of wind direction, and the second transmission path refers to the data transmission channel that is reselected after the wind direction has changed significantly.

[0133] The embodiments of this application do not specifically limit the numerical values ​​of the preset change threshold and the preset number of times; these can be set according to the actual situation.

[0134] In this embodiment, step 105 includes the following process:

[0135] Step 1051: Set a time interval, continuously acquire the wind direction angles in the wind direction data within the time interval to form a wind direction angle sequence, and calculate the average value of the wind direction angle sequence as the current reference wind direction.

[0136] In step 1051, the time interval refers to the pre-set time window length for collecting wind direction data, the wind direction angle sequence refers to the set of wind direction angle values ​​arranged in the sampling order within the time interval, and the current reference wind direction refers to the wind direction reference value used for subsequent deviation calculation.

[0137] The embodiments of this application do not specifically limit the size of the time interval, which can be set according to the actual situation.

[0138] In this embodiment of the application, a fixed time interval length is first set, and the wind direction angle at each sampling moment is continuously extracted from the wind direction data collected from the relay node within the time interval. These wind direction angles arranged in chronological order form a wind direction angle sequence. Then, the arithmetic mean of all wind direction angles in the sequence is calculated, and the calculated average value is used as the current reference wind direction at the current moment.

[0139] Step 1052: Input the wind direction angle sequence into the LSTM network to predict the wind direction angle for the next sampling period.

[0140] In step 1052, the LSTM network structure includes an input layer, an LSTM layer, and an output layer. The LSTM layer consists of multiple memory units, each containing three gate structures: a forget gate, an input gate, and an output gate. The forget gate determines which information to discard from the unit state, the input gate determines which new information needs to be stored in the unit state, and the output gate controls which information to output from the unit state to the current hidden state. In practical applications, taking the prediction of the wind direction angle in the next sampling period as an example, the number of nodes in the input layer is set to 10, indicating that the wind direction angles of the past 10 time steps are used as the input sequence. The LSTM layer has 64 hidden units to extract time series features, and the number of nodes in the output layer is 1 to output the predicted wind direction angle for the next time step. The network uses mean squared error as the loss function.

[0141] The training process of this model first collects historical wind direction data to form a training sample set. Each sample contains wind direction angles at 11 consecutive moments. The first 10 moments are used as input and the 11th moment is used as the label. Then, the data is normalized so that it is distributed between 0 and 1. Next, the training data is input into the network for forward propagation to calculate the predicted value. The error between the predicted value and the true label is calculated through the loss function. Then, the gradient is calculated using the time backpropagation algorithm and the network weights are updated using the Adam optimizer. This process is repeated for multiple rounds until the loss function converges, and finally, a trained LSTM network that can be used for real-time prediction is obtained.

[0142] It should be noted that the above structure is exemplary, and this application does not impose specific limitations on the internal structure design of the LSTM network. The design can be adjusted according to the actual situation.

[0143] The wind direction angle for the next sampling period refers to the wind direction angle value that may occur at a future sampling moment, as predicted by a neural network.

[0144] In this embodiment of the application, the wind direction angle sequence formed in step 1051 is fed into a pre-trained LSTM network as input data. The LSTM network has mastered the regular pattern of wind direction changes by learning historical wind direction data. After the network analyzes and processes the input sequence, it outputs a predicted value, which is the wind direction angle of the next sampling period.

[0145] The specific implementation process is as follows: First, the wind direction angle sequence formed in step 1051 is preprocessed. The sequence is reconstructed into a three-dimensional tensor form according to the input format required by the LSTM network, namely, the number of samples, the time step, and the feature dimension. The time step is set to 10, which means that the wind direction angle of the past 10 time steps is used as the input feature. The feature dimension is 1, which means that each time step only contains the wind direction angle as a feature. Then, the processed tensor data is input into the pre-trained LSTM network. The network contains an input layer for receiving the wind direction angle data of 10 time steps, an LSTM layer with 64 hidden units for extracting the time-dependent features of wind direction changes, and a fully connected output layer for mapping the output of the LSTM layer to a single predicted value.

[0146] In the LSTM layer, the data passes through the forget gate, input gate and output gate in sequence. The forget gate calculates the proportion of historical information to be discarded based on the current input and the hidden state at the previous time step. The input gate determines the new information to be updated and generates candidate cell states. The output gate controls how much information is output from the updated cell state to the current hidden state.

[0147] After processing 10 input data points step by step using this gating mechanism, the hidden state of the last time step is taken as the extracted temporal feature. Finally, the hidden state is passed to the fully connected output layer for linear transformation to obtain a specific value as the wind direction angle prediction value for the next sampling period.

[0148] Step 1053: Continuously acquire new wind direction angles and calculate the angle deviation value between the new wind direction angle and the current reference wind direction. Combine the prediction results of the LSTM network and use a sliding window averaging mechanism to smooth and filter the angle deviation value to obtain the filtered angle deviation.

[0149] In step 1053, the angle deviation value refers to the absolute value of the difference between the newly acquired wind direction angle and the current reference wind direction. The sliding window averaging mechanism refers to a filtering method that averages multiple consecutive angle deviation values ​​using a sliding window of fixed length. The filtered angle deviation refers to the deviation value used for abrupt change judgment after smoothing.

[0150] Furthermore, the embodiments of this application do not specifically limit the specific implementation process of the sliding window averaging mechanism, and can be set accordingly according to the actual situation.

[0151] In this embodiment, new wind direction angles are continuously collected from relay nodes in real time. Each time a new wind direction angle is acquired, the difference between the angle and the current reference wind direction determined in step 1051 is immediately calculated as the angle deviation value. At the same time, the wind direction angle predicted in step 1052 for the next sampling period is compared with the actual collected value. The predicted information is combined with the actual deviation value, and a sliding window averaging mechanism is used to calculate the weighted average of multiple angle deviation values ​​in the most recent window. The filtered angle deviation after filtering out instantaneous fluctuations is obtained through this smoothing filtering method.

[0152] Step 1054: When the absolute value of the filtered angle deviation exceeds the preset change threshold a preset number of times, a valid wind direction change event is determined to have occurred.

[0153] In step 1054, a valid wind direction change event refers to a significant change in wind direction that actually occurs after smoothing confirmation and meets the preset judgment criteria.

[0154] In this embodiment of the application, each filtered angle deviation obtained in step 1053 is monitored to determine whether the absolute value of the filtered angle deviation is greater than a preset change threshold. When the number of times the absolute value of the filtered angle deviation is continuously monitored to be greater than the preset change threshold reaches the preset number requirement, it is considered that a stable wind direction change has indeed occurred rather than an accidental fluctuation, and thus it is determined that a valid wind direction change event has occurred.

[0155] Step 1055: In response to the effective wind direction change event, update the stability parameters of all links in the dynamic network topology according to the new wind direction angle.

[0156] In this embodiment of the application, in response to the valid wind direction change event determined in step 1054, the latest wind direction angle collected at the current moment is used as input to re-execute the dynamic topology discovery algorithm in step 103. Based on the new wind direction angle, the directional angle between each pair of nodes in the network is recalculated, and the preset angle and stability relationship table is re-queried based on the new directional angle, thereby updating the stability parameters of all wireless links in the dynamic network topology.

[0157] Step 1056: Based on the updated stability parameters, recalculate the comprehensive cost score of all candidate transmission paths, and select the path with the lowest comprehensive cost score as the second transmission path.

[0158] In this embodiment, based on the stability parameters of all links in the dynamic network topology updated in step 1055, and according to the adaptive routing algorithm in step 104, all candidate transmission paths from the source node to the target node are re-determined, the path hop value and total path stability value of each path are recalculated, the basic score of each candidate transmission path is recalculated, the same fuzzy logic rule is introduced to calculate the dynamic adjustment factor based on the current network load rate, and finally the comprehensive cost score of each candidate transmission path is obtained. The path with the lowest comprehensive cost score among all candidate transmission paths is selected as the second transmission path.

[0159] Step 1057: Switch the routing path of the data packet to be transmitted from the first transmission path to the second transmission path to complete the wireless self-organizing network transmission of the VOCs monitoring data.

[0160] In this embodiment of the application, the transmission channel of the data packet to be transmitted generated in step 102 is switched from the first transmission path selected in step 104 to the second transmission path selected in step 1056. Subsequent data packets will be forwarded along this new second transmission path. This dynamic switching mechanism enables reliable wireless self-organizing network transmission of volatile organic compound monitoring data in the tank area under wind direction change conditions.

[0161] In this embodiment, after step 105, the method further includes the following steps:

[0162] B1: Collect transmission delay data and packet loss data of each node in the second transmission path.

[0163] In step B1, transmission delay data refers to the time it takes for a data packet to travel from the source node to the destination node, and packet loss data refers to the number or proportion of data packets lost during transmission over a period of time.

[0164] In this embodiment of the application, during the process of switching the data packet to the second transmission path for transmission, the transmission delay data and packet loss data of each intermediate node passed through on the second transmission path are continuously collected, and these data are used as the original basis for evaluating the actual operating quality of the current path.

[0165] B2: Input the transmission delay data, the packet loss data, and the current wind direction data into the BP neural network to obtain the performance prediction result of the second transmission path.

[0166] In step B2, the performance prediction result refers to the evaluation value of the transmission quality of the second transmission path over a future period of time output by the neural network.

[0167] In this embodiment of the application, the transmission delay data and packet loss data collected in step B1, along with the wind direction data at the current moment, are combined together as an input vector and fed into a pre-trained BP neural network. This BP neural network has learned the mapping relationship between transmission performance and delay, packet loss, and wind direction through a large number of historical samples. After processing the input, the network outputs a value as the performance prediction result of the second transmission path, which reflects the transmission quality level that the path may reach in the future.

[0168] The specific process is as follows: First, the transmission delay data, packet loss data, and current wind direction data collected from the second transmission path are normalized. The transmission delay data is converted into a normalized delay value between 0 and 1, the packet loss data is converted into a normalized packet loss rate value between 0 and 1, and the wind direction angle in the current wind direction data is converted into a normalized angle value between 0 and 1. Then, these three normalized values ​​are combined into a three-dimensional input vector. The first component of this input vector represents the transmission delay status, the second component represents the packet loss status, and the third component represents the environmental wind direction status.

[0169] The three-dimensional input vector is input into a pre-trained BP neural network, which includes an input layer for receiving the three-dimensional input vector, a hidden layer with 16 neurons for extracting the nonlinear relationship between input features, and an output layer for outputting the performance prediction result. In the hidden layer, the input vector is first linearly weighted and summed, and then a bias term is added to obtain the net input value. Then, a nonlinear transformation is performed through the Sigmoid activation function to obtain the hidden layer output value.

[0170] In the output layer, the hidden layer output values ​​are linearly weighted and summed again, and a bias term is added. Then, a value in the range of 0 to 1 is directly output through a linear activation function as the performance prediction result of the second transmission path. The closer the result is to 1, the better the predicted path transmission performance is, and the closer it is to 0, the worse the predicted path transmission performance is.

[0171] It should be noted that the above structure is exemplary, and this application does not impose specific limitations on the internal structure design of the BP neural network. The design can be adjusted according to the actual situation.

[0172] B3: When the performance prediction result is lower than the preset performance threshold, the path verification mechanism is activated, and the ant colony algorithm is used to determine multiple alternative paths.

[0173] In step B3, the preset performance threshold refers to the lower limit of the performance score set in advance to determine whether a path needs to be switched, and the path verification mechanism refers to the process of finding an alternative path when the path performance prediction is poor.

[0174] This application embodiment does not specifically limit the value of the preset performance threshold, which can be set according to the actual situation. Furthermore, this application embodiment does not specifically limit the specific implementation process of the path verification mechanism, which can be set accordingly according to the actual situation.

[0175] In this embodiment of the application, the performance prediction result obtained in step B2 is compared with a preset performance threshold. If the value of the performance prediction result is lower than the preset performance threshold, it indicates that the current second transmission path may experience performance degradation in the future. At this time, the path verification mechanism is activated. Based on the latest dynamic network topology, the ant colony algorithm is used to perform multi-path parallel search. By simulating the positive feedback mechanism of ants releasing pheromones, multiple alternative paths that may reach the target node are explored in the topology.

[0176] B4: Evaluate the stability parameters and predicted performance of each alternative path, select the alternative path with the best performance as the new second transmission path, and switch the data packet to be transmitted from the current second transmission path to the new second transmission path for transmission.

[0177] In step B4, the new second transmission path refers to the data transmission channel that is reselected under the path verification mechanism to replace the current path.

[0178] In this embodiment of the application, each candidate path searched in step B3 is comprehensively evaluated. On the one hand, the stability parameters of the links contained in each candidate path are extracted from the dynamic network topology. On the other hand, the transmission performance of each candidate path is predicted in combination with the current environmental conditions. The candidate path with the best overall performance is selected as the new second transmission path by combining the factors of stability and predicted performance. Then, the transmission channel of the data packet to be transmitted is switched from the current second transmission path to this new second transmission path to continue transmission. This continuous optimization mechanism further ensures the reliability of data transmission.

[0179] Specific example: The evaluation of both stability and predicted performance is performed using a weighted summation method. First, the overall stability value of each candidate path is calculated, which is the sum of the stability parameters of all links traversed by the path divided by the path hop count to obtain the average stability parameter. Then, the predicted performance score of the candidate path is obtained from the performance prediction results output by the BP neural network. Next, the average stability parameter is multiplied by 0.6 to obtain the stability score, and the predicted performance score is multiplied by 0.4 to obtain the performance score. The two scores are added together to obtain the overall score of the candidate path. Finally, the overall scores of all candidate paths are compared, and the candidate path with the highest overall score is selected as the new second transmission path.

[0180] This application triggers network topology updates and path switching by detecting sudden changes in wind direction. It also introduces path performance prediction and ant colony optimization mechanisms, enabling data transmission paths to be dynamically adjusted in response to environmental changes and path quality degradation. This improves the continuity and reliability of volatile organic compound monitoring data transmission in tank farms under complex weather conditions.

[0181] Figure 3 This is a schematic diagram of the structure of a wireless self-organizing network transmission system for VOCs monitoring data of an oil storage tank, provided in an embodiment of this application. Figure 3 As shown, the system includes:

[0182] The data acquisition module 31 is used to deploy monitoring nodes in the tank area to collect VOCs monitoring data and meteorological data of the oil storage tanks, and to deploy at least one relay node at a predetermined location in the tank area other than the monitoring nodes to collect wind speed data and wind direction data.

[0183] The correction module 32 is used to correct the VOCs monitoring data based on the meteorological data, add a time stamp to the corrected VOCs monitoring data, generate a data packet to be transmitted, and generate link status parameters based on the wind speed data and the wind direction data.

[0184] The determination module 33 is used to determine the stability parameters of the wireless link based on the link state parameters through a dynamic topology discovery algorithm, and to construct a dynamic network topology based on the stability parameters.

[0185] Selection module 34 is used to select a first transmission path for the data packet to be transmitted based on the dynamic network topology using an adaptive routing algorithm.

[0186] The update module 35 is used to update the stability parameter when the number of times the change in the wind direction data exceeds the preset change threshold reaches a preset number. Using the updated stability parameter, a second transmission path is selected for the data packet to be transmitted, and the transmission path is switched from the first transmission path to the second transmission path to complete the wireless self-organizing network transmission of the VOCs monitoring data.

[0187] The wireless ad hoc network transmission system for VOCs monitoring data of oil storage tanks in this application embodiment is used to implement the aforementioned wireless ad hoc network transmission method for VOCs monitoring data of oil storage tanks. Therefore, the specific implementation of the wireless ad hoc network transmission system for VOCs monitoring data of oil storage tanks can be found in the embodiment section of the wireless ad hoc network transmission method for VOCs monitoring data of oil storage tanks above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0188] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the wireless self-organizing network transmission method for VOCs monitoring data of oil storage tanks as described above.

[0189] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the wireless self-organizing network transmission method for VOCs monitoring data of oil storage tanks described above.

[0190] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0191] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the wireless ad hoc network transmission method for VOCs monitoring data of oil storage tanks.

[0192] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0193] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0194] The above provides a detailed description of a wireless ad hoc network transmission method and system for VOCs monitoring data from oil storage tanks, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A wireless self-organizing network transmission method for VOCs monitoring data from oil storage tanks, characterized in that, include: Monitoring nodes are deployed in the tank area to collect VOCs monitoring data and meteorological data from the oil tanks. At least one relay node is deployed at a predetermined location in the tank area, in addition to the monitoring nodes, to collect wind speed and wind direction data. Based on the meteorological data, the VOCs monitoring data is corrected, a time stamp is added to the corrected VOCs monitoring data, a data packet to be transmitted is generated, and link status parameters are generated based on the wind speed data and the wind direction data. Based on the link state parameters, a dynamic topology discovery algorithm is used to determine the stability parameters of the wireless link, and a dynamic network topology is constructed based on the stability parameters, including: Step 1031: At a preset time interval, the monitoring node and the relay node send signaling data. After receiving the signaling data, any other node determines the connection direction based on the location data of the corresponding node and the location data in the signaling data. Step 1032: Compare the connection direction with the wind direction data in the signaling data to obtain the directional angle; Step 1033: Based on the angle of the direction, query the preset angle-stability relationship table to obtain the stability parameters; Based on the dynamic topology discovery algorithm, repeat the above steps 1031, 1032 and 1033 to obtain and update the stability parameters of all node pairs in the network. One node pair corresponds to one wireless link. Based on the updated stability parameters of all nodes, the minimum spanning tree algorithm is applied to generate a dynamic network topology. Based on the dynamic network topology, an adaptive routing algorithm is used to select a first transmission path for the data packet to be transmitted. When the number of times the change in wind direction data continuously exceeds a preset change threshold reaches a preset number, the stability parameter is updated. Using the updated stability parameter, a second transmission path is selected for the data packet to be transmitted, and the transmission path is switched from the first transmission path to the second transmission path to complete the wireless self-organizing network transmission of the VOCs monitoring data, including: A time interval is set, and the wind direction angle in the wind direction data is continuously acquired within the time interval to form a wind direction angle sequence. The average value of the wind direction angle sequence is calculated as the current reference wind direction. The wind direction angle sequence is input into an LSTM network to predict the wind direction angle for the next sampling period; New wind direction angles are continuously acquired, and the angle deviation between the new wind direction angle and the current reference wind direction is calculated. Combined with the prediction results of the LSTM network, the angle deviation value is smoothed and filtered using a sliding window averaging mechanism to obtain the filtered angle deviation. When the absolute value of the filtered angle deviation exceeds the preset change threshold a preset number of times, a valid wind direction change event is determined to have occurred. In response to the effective wind direction change event, the stability parameters of all links in the dynamic network topology are updated according to the new wind direction angle. Based on the updated stability parameters, the comprehensive cost score of all candidate transmission paths is recalculated, and the path with the lowest comprehensive cost score is selected as the second transmission path. The routing path of the data packet to be transmitted is switched from the first transmission path to the second transmission path to complete the wireless self-organizing network transmission of the VOCs monitoring data.

2. The method according to claim 1, characterized in that, The step of selecting a first transmission path for the data packet to be transmitted based on the dynamic network topology using an adaptive routing algorithm includes: In the dynamic network topology, all candidate transmission paths from the source node to the target node are determined, and the number of intermediate nodes traversed by each candidate transmission path is counted as the path hop value. The stability parameters of all communication links included in each candidate transmission path are summed to obtain the total path stability value; Based on the path hop count and the total path stability value, a basic score is calculated for each candidate transmission path; A preset fuzzy logic rule is introduced to apply a dynamic adjustment factor to the basic score, thereby obtaining a comprehensive cost score for each candidate transmission path. The dynamic adjustment factor is calculated based on the current network load rate. An adaptive routing algorithm is used to select the path with the lowest overall cost score from all candidate transmission paths, which is then determined as the first transmission path, and the data packet to be transmitted is sent through the first transmission path.

3. The method according to claim 2, characterized in that, The introduction of preset fuzzy logic rules applies a dynamic adjustment factor to the basic score to obtain a comprehensive cost score for each candidate transmission path, including: The current network load rate is calculated by statistically analyzing the length of the data packet sending queue and the data packet sending frequency of the source node within a preset time period. The current network load rate is mapped to fuzzy logic input variables, which include a first fuzzy set representing the load level; Reasoning is performed based on a preset fuzzy rule base, which contains multiple fuzzy logic rules that take the first fuzzy set as a condition and a dynamic adjustment factor as a conclusion. The inference results are defuzzified to obtain the value of the dynamic adjustment factor. The dynamic adjustment factor value is multiplied by the base score to obtain the product result, and the product result is determined as the comprehensive cost score of each candidate transmission path.

4. The method according to claim 1, characterized in that, The process involves correcting the VOCs monitoring data based on the meteorological data, adding a time stamp to the corrected VOCs monitoring data, generating a data packet to be transmitted, and generating link status parameters based on the wind speed and wind direction data, including: Temperature and humidity data are extracted from the meteorological data. Based on the preset compensation relationship, the temperature data, and the humidity data, the concentration values ​​in the VOCs monitoring data are adjusted to obtain corrected VOCs monitoring data, and a time stamp is added to the corrected VOCs monitoring data to form a data packet to be transmitted. Based on the wind speed data and the wind direction data, a preset wind speed and signal attenuation mapping table is queried to obtain the basic evaluation value; Based on the wind direction angle in the wind direction data and the geographical coordinates of the current node, the direction of signal propagation is determined, and the basic evaluation value is adjusted based on the direction of propagation to obtain the link state parameters.

5. The method according to claim 1, characterized in that, After switching from the first transmission path to the second transmission path, the process further includes: Collect transmission delay data and packet loss data of each node in the second transmission path; The transmission delay data, the packet loss data, and the current wind direction data are input into the BP neural network to obtain the performance prediction result of the second transmission path. When the performance prediction result is lower than the preset performance threshold, the path verification mechanism is activated, and the ant colony algorithm is used to determine multiple alternative paths; The stability parameters and predicted performance of each alternative path are evaluated, the alternative path with the best performance is selected as the new second transmission path, and the data packet to be transmitted is switched from the current second transmission path to the new second transmission path for transmission.

6. A wireless self-organizing network transmission system for VOCs monitoring data from oil storage tanks, characterized in that, include: The data acquisition module is used to deploy monitoring nodes in the tank area to collect VOCs monitoring data and meteorological data of the oil storage tanks, and to deploy at least one relay node at a predetermined location in the tank area other than the monitoring nodes to collect wind speed data and wind direction data. The correction module is used to correct the VOCs monitoring data based on the meteorological data, add a time stamp to the corrected VOCs monitoring data, generate a data packet to be transmitted, and generate link status parameters based on the wind speed data and the wind direction data. The determination module is used to determine the stability parameters of the wireless link based on the link state parameters using a dynamic topology discovery algorithm, and to construct a dynamic network topology based on the stability parameters, including: Step 1031: At a preset time interval, the monitoring node and the relay node send signaling data. After receiving the signaling data, any other node determines the connection direction based on the location data of the corresponding node and the location data in the signaling data. Step 1032: Compare the connection direction with the wind direction data in the signaling data to obtain the directional angle; Step 1033: Based on the angle of the direction, query the preset angle-stability relationship table to obtain the stability parameters; Based on the dynamic topology discovery algorithm, repeat the above steps 1031, 1032 and 1033 to obtain and update the stability parameters of all node pairs in the network. One node pair corresponds to one wireless link. Based on the updated stability parameters of all nodes, the minimum spanning tree algorithm is applied to generate a dynamic network topology. The selection module is used to select a first transmission path for the data packet to be transmitted based on the dynamic network topology using an adaptive routing algorithm. The update module is used to update the stability parameter when the number of times the change in wind direction data continuously exceeds a preset change threshold reaches a preset number. Using the updated stability parameter, a second transmission path is selected for the data packet to be transmitted, and the transmission path is switched from the first transmission path to the second transmission path to complete the wireless self-organizing network transmission of the VOCs monitoring data. This includes: A time interval is set, and the wind direction angle in the wind direction data is continuously acquired within the time interval to form a wind direction angle sequence. The average value of the wind direction angle sequence is calculated as the current reference wind direction. The wind direction angle sequence is input into an LSTM network to predict the wind direction angle for the next sampling period; New wind direction angles are continuously acquired, and the angle deviation between the new wind direction angle and the current reference wind direction is calculated. Combined with the prediction results of the LSTM network, the angle deviation value is smoothed and filtered using a sliding window averaging mechanism to obtain the filtered angle deviation. When the absolute value of the filtered angle deviation exceeds the preset change threshold a preset number of times, a valid wind direction change event is determined to have occurred. In response to the effective wind direction change event, the stability parameters of all links in the dynamic network topology are updated according to the new wind direction angle. Based on the updated stability parameters, the comprehensive cost score of all candidate transmission paths is recalculated, and the path with the lowest comprehensive cost score is selected as the second transmission path. The routing path of the data packet to be transmitted is switched from the first transmission path to the second transmission path to complete the wireless self-organizing network transmission of the VOCs monitoring data.

7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the wireless ad hoc network transmission method for VOCs monitoring data of oil storage tanks as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the wireless self-organizing network transmission method for VOCs monitoring data of oil storage tanks as described in any one of claims 1 to 5.

Images