Intelligent gas data transmission management and control method and system based on Internet of Things

Abstract

The invention discloses an intelligent gas data transmission management and control method and system based on the Internet of Things, and relates to the technical field of intelligent data transmission of the Internet of Things, and the method comprises the steps: defining a path planning objective function, calculating an optimal transmission path of a transmission node and a target node in a gas network model through a path planning algorithm, and executing a transmission task of the gas state data of the Internet of Things according to the bandwidth allocation plan, and monitoring the state of a communication link in real time. According to the method, the intelligent level of path planning is remarkably improved, the transmission efficiency and the energy consumption are optimized, and meanwhile, the real-time performance and the reliability of high-priority data are ensured. A path planning algorithm selects an ant colony algorithm with high adaptability, so that the flexibility and robustness of path selection are greatly improved, an optimal path meeting performance requirements can be quickly found in a complex network environment, the fault risk is reduced, and the real-time performance of high-priority data and the flexibility of low-priority data are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things intelligent data transmission, and particularly to a method and system for controlling and managing intelligent gas data transmission based on the Internet of Things. Background Art

[0002] With the development of Internet of Things technology, the data transmission and control of intelligent gas have become an important research direction in the field of energy management. As the scale of the intelligent gas system continues to expand and the amount of data grows exponentially, higher requirements are imposed on the performance of communication links, which further promotes the research and development of intelligent and adaptive data transmission control methods.

[0003] Existing gas data transmission control methods have many deficiencies. First, ordinary path planning algorithms often rely on static network models and cannot fully consider the actual characteristics of communication links and the differences in node computing capabilities, resulting in inaccurate path selection and difficulty in meeting the real-time requirements of high-priority data. Second, in terms of bandwidth allocation, existing static allocation strategies lack flexibility and cannot be adjusted in a timely manner according to the dynamic changes in link status. When the network load fluctuates greatly, resource waste or bottleneck phenomena are likely to occur. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for controlling and managing intelligent gas data transmission based on the Internet of Things, which solves the problems of inaccurate path planning and lack of dynamic adaptability in bandwidth allocation during intelligent gas data transmission.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, the present invention provides a method for controlling and managing intelligent gas data transmission based on the Internet of Things, which includes collecting Internet of Things gas status data, abstracting transmission nodes as graph vertices using a graph theory model, abstracting communication links between transmission nodes as edges, and setting the computing capabilities and bandwidth resources of each transmission node to generate a gas network model; Defining a path planning objective function and using a path planning algorithm to calculate the optimal transmission path between a transmission node and a target node in the gas network model; Combining the characteristics of communication links between transmission nodes on the optimal transmission path and using a bandwidth allocation algorithm to allocate bandwidth resources to the optimal transmission path to obtain a bandwidth allocation plan; Executing the transmission task of Internet of Things gas status data according to the bandwidth allocation plan, and during the transmission process, monitoring the communication link status in real time to obtain a transmission log.

[0007] As a preferred solution of the intelligent gas data transmission control method based on the Internet of Things according to the present invention, wherein: the Internet of Things gas state data includes the Internet of Things network interface state, resource utilization rate, data priority of gas, temperature and humidity of gas state, gas pressure, and gas flow rate.

[0008] As a preferred solution of the intelligent gas data transmission control method based on the Internet of Things according to the present invention, wherein: using a graph theory model to abstract transmission nodes as graph vertices, abstract communication links between transmission nodes as edges, and setting the computing power and bandwidth resources of each transmission node to generate a gas network model, including the following steps, The transmission nodes include intelligent gas meters, gateways, and servers; Using the graph theory model to traverse all transmission nodes and treating each transmission node as a graph vertex; Using SNMP to send detection signals to all graph vertices. When a graph vertex responds to a detection signal with another graph vertex, determine the communication link between each pair of graph vertices, and at the same time create an edge and connect the corresponding graph vertices to form an initial graph structure; Statistical attribute indicators of communication links in the initial graph structure, setting weights for the communication link attribute indicators according to actual needs to obtain a graph structure after weight assignment; According to the specific technical specifications of the hardware device, set the computing power and bandwidth resources of each graph vertex; Using Graphviz to visualize the graph structure after weight assignment and the computing power and bandwidth resources of each graph vertex as a gas network model.

[0009] As a preferred solution of the intelligent gas data transmission control method based on the Internet of Things according to the present invention, wherein: defining a path planning objective function, including the following steps, Based on the attribute indicators of communication links, calculate the comprehensive available resources and comprehensive quality levels of each graph vertex and input them into the gas network model to form a weighted graph; According to the weighted graph, define a path planning objective function and calculate the total cost values of all paths.

[0010] As a preferred solution of the intelligent gas data transmission control method based on the Internet of Things according to the present invention, wherein: using a path planning algorithm to calculate the optimal transmission path between a transmission node and a target node in the gas network model, including the following steps, Based on the total cost values of all paths, select the ant colony algorithm as the path planning algorithm, and set the number of ants, pheromone evaporation coefficient, pheromone concentration, and heuristic factor; Command each ant to start from the starting graph vertex, determine the next graph vertex to move to by combining the pheromone concentration and the heuristic factor. When the ant moves one step, record the path quality of all the graph vertices and communication links passed through until it reaches the target graph vertex, and obtain the path quality data; Adjust the pheromone concentration according to the path quality data, and traverse all the paths passed by the ants, and form all the paths into a candidate path set; Use the Pareto optimization method to screen out the Pareto optimal solutions from the candidate path set to form a Pareto optimal solution set; Combined with the actual requirements and the data priority of the gas, select a path from the Pareto optimal solution set as the optimal transmission path between the graph vertex and the target graph vertex.

[0011] As a preferred solution of the intelligent gas data transmission control method based on the Internet of Things described in the present invention, wherein: combined with the communication link characteristics between the transmission nodes on the optimal transmission path, use the bandwidth allocation algorithm to allocate bandwidth resources to the optimal transmission path to obtain a bandwidth allocation plan, including the following steps, Divide the data priority of the gas into high-priority data and low-priority data; Set the transmission time requirement, data volume size of the high-priority data and the maximum delay range of the low-priority data, and determine the bandwidth requirements of the high-priority data and the low-priority data; Based on the bandwidth requirements, reserve part of the bandwidth on each communication link of the optimal transmission path as a dynamic adjustment pool and allocate bandwidth to the high-priority data, and allocate the remaining bandwidth to the low-priority data to obtain the allocation result; According to the allocation result, use the intelligent scheduling algorithm to analyze the health status of the optimal transmission path, identify the bottleneck points and adjust the distribution of the bandwidth data flow to obtain a complete bandwidth allocation strategy; Organize the bandwidth allocation strategy and the optimal transmission path into a structured form to form a bandwidth allocation plan.

[0012] As a preferred solution of the intelligent gas data transmission control method based on the Internet of Things described in the present invention, wherein: execute the transmission task of the Internet of Things gas status data according to the bandwidth allocation plan. During the transmission process, monitor the communication link status in real time to obtain the transmission log, including the following steps, Use the optimal transmission path to send the Internet of Things gas status data from the starting graph vertex to the target graph vertex; During the transmission process, collect the packet loss rate, throughput and network congestion degree of the Internet of Things gas status data from each communication link as the core indicators of the dynamic transmission performance; Set an anomaly detection threshold, and determine whether there is an anomaly in the transmission of Internet of Things (IoT) gas status data based on the interval of the core indicators of dynamic transmission performance within the anomaly detection threshold; When an anomaly occurs, select an alternative path and re - execute the bandwidth allocation policy to obtain IoT gas transmission performance data; Integrate the bandwidth allocation plan and the IoT gas transmission performance data to obtain a transmission log.

[0013] In a second aspect, the present invention provides an IoT - based intelligent gas data transmission control system, including a model construction module that collects IoT gas status data, abstracts transmission nodes as graph vertices using a graph theory model, abstracts communication links between transmission nodes as edges, and sets the computing power and bandwidth resources of each transmission node to generate a gas network model; A path generation module that defines a path planning objective function and uses a path planning algorithm to calculate the optimal transmission path between a transmission node and a target node in the gas network model; A bandwidth allocation module that combines the communication link characteristics between transmission nodes on the optimal transmission path and uses a bandwidth allocation algorithm to allocate bandwidth resources to the optimal transmission path to obtain a bandwidth allocation plan; A log generation module that executes the transmission task of IoT gas status data according to the bandwidth allocation plan, and monitors the communication link status in real - time during the transmission process to obtain a transmission log.

[0014] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the IoT - based intelligent gas data transmission control method described in the first aspect of the present invention is implemented.

[0015] In a fourth aspect, the present invention provides a computer - readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the IoT - based intelligent gas data transmission control method described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: Define a path planning objective function, and use a path planning algorithm to calculate the optimal transmission path between transmission nodes and target nodes in the gas network model. Execute the transmission task of IoT gas status data according to the bandwidth allocation plan, and monitor the communication link status in real time. It significantly improves the intelligent level of path planning, optimizes the transmission efficiency and energy consumption, and at the same time ensures the real-time performance and reliability of high-priority data. The path planning algorithm selects an ant colony algorithm with high adaptability, which greatly improves the flexibility and robustness of path selection, can quickly find the optimal path that meets the performance requirements in a complex network environment, reduces the failure risk, and at the same time ensures the real-time performance of high-priority data and the flexibility of low-priority data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a flowchart for constructing the gas network model in Embodiment 1.

[0019] Figure 2 It is a flowchart of the path planning algorithm in Embodiment 1.

[0020] Figure 3 It is a schematic diagram of the bandwidth allocation strategy in the embodiment.

[0021] Figure 4 It is a module structure diagram of the intelligent gas data transmission control system in the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to make the above-mentioned objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification.

[0023] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0024] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation of the present invention. The phrase "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that excludes other embodiments.

[0025] Example 1, referring to Figure 1 , Figure 2 , Figure 3 , Figure 4 , which is the first embodiment of the present invention. This embodiment provides a method for intelligent gas data transmission and control based on the Internet of Things, including the following steps: S1. The Internet of Things gas status data includes the Internet of Things network interface status, resource utilization rate, data priority of gas (i.e., classifying gas status data according to the importance and real-time requirements of the data), temperature and humidity of the gas status, gas pressure, and gas flow rate (i.e., the volume or mass of gas passing through the gas pipeline per unit time).

[0026] S2. Use the graph theory model to abstract the transmission nodes as graph vertices, abstract the communication links between the transmission nodes as edges, and set the computing power and bandwidth resources of each transmission node to generate a gas network model.

[0027] Including the following steps, The transmission nodes include intelligent gas meters, gateways, and servers; Use the graph theory model to traverse all the transmission nodes and regard each transmission node as a graph vertex; In this step, abstracting the transmission nodes as vertices in the graph theory model makes the topological structure of the entire network clear at a glance, providing an intuitive basis for subsequent path planning, bandwidth allocation, and fault diagnosis; Use SNMP to send detection signals (i.e., custom handshake messages) to all graph vertices. When the graph vertices respond to the detection signals between each other, determine the communication links between each graph vertex, and at the same time create an edge and connect the corresponding graph vertices to form an initial graph structure; When a graph vertex receives a detection signal, it automatically checks whether it is in a normal working state, whether it can recognize and understand the content of the detection signal, and whether there is a direct communication link with the sending node as judgment conditions (when all three judgment conditions are met, the graph vertex will generate an acknowledgment message and send it back to the sending graph vertex. When any one of the three judgment conditions cannot be met, the graph vertex will not generate an acknowledgment message and send it back to the sending graph vertex, and the detection signal needs to be resent. If multiple attempts are unsuccessful, the sending graph vertex will mark this link as unavailable and select an alternative path). When the sending graph vertex receives the acknowledgment message, further determine the communication links between the graph vertices; Statistically analyze the attribute indicators of the communication links in the initial graph structure, set weights for the communication link attribute indicators according to actual requirements, and obtain the graph structure after weight allocation (after completing the weight allocation, each communication link will be assigned a set of weight values, and this weight value will be embedded in the graph structure); The actual requirements depend on specific application scenarios (high - bandwidth demand scenarios, long - distance communication scenarios, and reliability scenarios). For example, in a long - distance communication scenario (such as satellite communication), the physical distance becomes a key factor; For high - bandwidth demand scenarios, the weights are assigned as 60% for signal strength, 30% for transmission success rate, and 10% for physical distance. For long - distance communication scenarios, the weights are assigned as 50% for physical distance, 30% for signal strength, and 20% for transmission success rate. For reliability scenarios, the weights are assigned as 70% for transmission success rate, 20% for signal strength, and 10% for physical distance; The attribute metrics of the communication link include signal strength, physical distance, and transmission success rate; According to the specific technical specifications of the hardware device (i.e., the performance parameters determined during the design and manufacturing of the hardware device, such as memory capacity and data - processing speed), set the computing power and bandwidth resources of each graph vertex; Evaluate the computing power of the graph vertex. For example, for an intelligent gas meter, if its processor main frequency is 500MHz and its memory is 64MB, its computing power can be defined as low. While for a server, if its processor main frequency is 3.5GHz and its memory is 32GB, its computing power can be defined as high. Convert the evaluation results into specific levels, such as level 1 for low level, level 2 for medium level, and level 3 for high level (the same applies to bandwidth resources); Combine the computing power and bandwidth resources of each device to form a complete description of the graph vertex attributes. For example, a certain gateway is defined as having medium computing power and high bandwidth resources; Use Graphviz to visualize the graph structure with assigned weights and the computing power and bandwidth resources of each graph vertex as a gas network model; Use Graphviz to distinguish the attributes of graph vertices by drawing shapes (e.g., graph vertices with sufficient bandwidth resources are represented by large circles, and graph vertices with limited bandwidth resources are represented by small circles), and mark the edges between communication links with colors (e.g., edges with higher weights are represented by dark red, and edges with lower weights are represented by light gray), form the basic framework of the graph and automatically calculate the layout between graph vertices and edges (avoid edge crossings and place graph vertices closer. If the automatic layout does not achieve the desired effect, the positions of some graph vertices can be adjusted manually to optimize the readability of the graph. For example, place the core graph vertices (such as servers) in the center of the graph, and other graph vertices are distributed around it); Finally, add labels next to each graph vertex and edge to indicate its type (such as intelligent gas meter) and communication - link attributes (such as computing - power level, bandwidth - resource level), obtain the visualization result and output it in vector format (SVG) to form a gas network model.

[0028] S3. Define the path - planning objective function.

[0029] It includes the following steps: Based on the attribute metrics of the communication links, the comprehensive available resources and comprehensive quality levels of each graph vertex are statistically calculated and input into the gas network model to form a weighted graph. Perform latency tests (recording the time required for a data packet to be sent and received) and packet loss rate tests on each communication link (considering a link with a packet loss rate lower than 1% as a high-quality communication link, a link between 1% and 5% as a medium-quality communication link, and a link higher than 5% as a low-quality communication link). Based on the latency tests and packet loss rate tests, evaluate the quality of the signal strength (for example, for a communication link with a latency of 50 ms and a packet loss rate of 1%, the quality of its signal strength is high). Integrate the data of the latency test, packet loss rate test, and evaluation of signal strength quality for each communication link to obtain the comprehensive quality level of each graph vertex. According to the weighted graph, define the path planning objective function and calculate the total cost value of all paths. The path planning objective function includes total latency, total energy consumption, and total transmission success rate. The expression for the total cost value of all paths is: ; where F represents the total cost value of all paths, T i represents the total latency of the i-th path, E i represents the total energy consumption of the i-th path, S i represents the total transmission success rate of the i-th path, α represents the weight coefficient of the total latency of the i-th path, β represents the weight coefficient of the total energy consumption of the i-th path, and γ represents the weight coefficient of the total transmission success rate of the i-th path.

[0030] S4. Use the path planning algorithm to calculate the optimal transmission path between the transmission node and the target node in the gas network model.

[0031] It includes the following steps: Based on the total cost value of all paths, select the ant colony algorithm as the path planning algorithm and set the number of ants (determined according to the size of the gas network model, specifically 2 to 3 times the number of graph vertices), the pheromone evaporation coefficient (0.1 to 0.5, selected according to the complexity of the path), the pheromone concentration (when an ant completes a path selection, update the pheromone concentration of the corresponding path according to F, using a constant 1 divided by F to obtain), and the heuristic factor (i.e., the prior attraction that guides the ant to preferentially select certain paths, set according to the attribute metrics of the communication links, with a specific value range of 1 to 5). Command each ant to start from the starting graph vertex (such as the gateway), and determine the next graph vertex to move to by combining the pheromone concentration and the heuristic factor (at the current graph vertex, check all adjacent graph vertices directly connected to it and exclude the already visited graph vertices. For each unvisited adjacent graph vertex, combine the pheromone concentration and the heuristic factor to determine the probability that the ant selects the next graph vertex to move to. If the pheromone concentration of an edge is high and the heuristic factor is large, the probability of selecting this edge is higher). When the ant moves one step, record the path quality of all the graph vertices and communication links passed through until it reaches the target graph vertex, and obtain the path quality data; The path quality data includes jitter (i.e., the degree of fluctuation of the packet arrival time), energy consumption, and the security of the communication link; Adjust the pheromone concentration according to the path quality data, and traverse all the paths passed by the ants (when multiple ants select the same path, only one needs to be retained), and form a candidate path set by all the paths; After each ant completes a path search, it will generate a complete path and record it as path quality data. For a better path (low jitter, low energy consumption, and high security of the communication link), increase the pheromone concentration of its corresponding edge, and for a worse path, reduce the pheromone concentration of its corresponding edge; Use the Pareto optimization method to screen out the Pareto optimal solutions from the candidate path set to form a Pareto optimal solution set; Taking the delay, energy consumption, and transmission success rate of the path as the goals, compare the delay, energy consumption, and transmission success rate of any two paths in the candidate path set. If one path is not inferior to another path in all three goals and is superior to another path in at least one goal, then the former is said to dominate the latter. If path A is dominated by other paths, then mark path A as a non-Pareto optimal solution. If path A is not dominated by any other path, then retain path A as a Pareto optimal solution; Combined with the actual requirements and the data priority of the gas, select a path from the Pareto optimal solution set as the optimal transmission path between the graph vertex and the target graph vertex; Suppose there are three paths in the Pareto optimal solution set (path A has a delay of 10 ms, an energy consumption of 5 units, and a transmission success rate of 95%, path B has a delay of 15 ms, an energy consumption of 3 units, and a transmission success rate of 98%, path C has a delay of 12 ms, an energy consumption of 4 units, and a transmission success rate of 96%). According to the requirements of the current task, clarify the main optimization goal for path selection (for example, when processing leakage alarm signals, low delay and high transmission success rate are given priority). Compare the performance of the three indicators of the three paths. Therefore, select path A (with the lowest delay and a relatively high transmission success rate) as the optimal transmission path.

[0032] S5. Combine the communication link characteristics between the transmission nodes on the optimal transmission path, and use the bandwidth allocation algorithm to allocate bandwidth resources to the optimal transmission path to obtain a bandwidth allocation plan.

[0033] It includes the following steps: The communication link characteristics between the transmission nodes on the optimal transmission path include the storage capacity, computing power, and bandwidth utilization rate of the graph vertices; Divide the data priority of the gas into high-priority data and low-priority data; Set the transmission time requirements (set according to the specific requirements of the application scenario, such as the leakage alarm signal needs to be transmitted to the control center within one second) and data volume size (determined according to the type of quantity) of the high-priority data, as well as the maximum delay range of the low-priority data (set according to the load situation, such as backup data can be transmitted within a few minutes or even a few hours), and determine the bandwidth requirements of the high-priority data and low-priority data (taking the high-priority data as an example, if the data volume of the leakage alarm signal is 50 bytes, each byte is 8 bits, and the transmission time is 1 second, then the required bandwidth is 400 bps); Based on the bandwidth requirements, reserve a part of the bandwidth on each communication link of the optimal transmission path as a dynamic adjustment pool (for example, if the total bandwidth of the communication link is 1 Mbps, 10% can be reserved, that is, 100 kbp as the dynamic adjustment pool) and allocate bandwidth to the high-priority data, and allocate the remaining bandwidth to the low-priority data (the remaining bandwidth is the total bandwidth minus the reserved part of the bandwidth minus the bandwidth allocated to the high-priority data) to obtain the allocation result; According to the allocation result, use the intelligent scheduling algorithm to analyze the health status of the optimal transmission path (taking the signal strength of the communication link on the optimal transmission path as an example, when the signal strength is less than the minimum requirement of the signal strength (set according to the receiving sensitivity of the device), it is considered that there is an abnormal problem with this communication link, which will affect the health status of the optimal transmission path), identify the bottleneck points (that is, the graph vertices or links with the worst performance or the most tense resources in the path, taking the bandwidth utilization rate as an example, check the bandwidth utilization rate of each communication link, when a certain link is close to the maximum capacity of the bandwidth utilization rate (more than 90%), it is the bottleneck point) and adjust the distribution of the bandwidth data stream (reduce the bandwidth occupied by the low-priority data, and allocate the saved bandwidth to the bottleneck point. For example, if the transmission of the low-priority data can tolerate a certain delay, such as less than 100 milliseconds, the bandwidth allocation of the low-priority data can be appropriately reduced. If the bottleneck point cannot be solved by adjusting the bandwidth, try to divert part of the data stream to other paths. For example, transfer part of the low-priority data to the standby link with lower load) to obtain a complete bandwidth allocation strategy; Organize the bandwidth allocation strategy and the optimal transmission path into a structured form (a four-dimensional table) to form a bandwidth allocation plan; The bandwidth allocation plan in this step records the bandwidth allocation and performance metrics of each link in a structured form, enabling real-time monitoring of the path status and timely detection of potential problems. When problems occur, the bottleneck point can be quickly located and measures can be taken.

[0034] S6. Execute the transmission task of the Internet of Things gas status data according to the bandwidth allocation plan. During the transmission process, monitor the communication link status in real time to obtain the transmission log.

[0035] It includes the following steps: Use the optimal transmission path to send the Internet of Things gas status data from the starting graph vertex to the target graph vertex; During the transmission process, collect the packet loss rate, throughput, and network congestion degree of the Internet of Things gas status data from each communication link as the core indicators of dynamic transmission performance; The purpose of selecting the packet loss rate, throughput, and network congestion degree as the core indicators of dynamic transmission performance is that when the packet loss rate is high, it indicates that the link quality is poor, resulting in data transmission failure or the need for retransmission, increasing latency and energy consumption. The throughput directly affects the data transmission speed. If the throughput is too low, it cannot meet the real-time requirements, especially for large-scale data transmission (such as video surveillance). By monitoring the network congestion degree, bottleneck points can be identified in advance, and measures such as traffic diversion or dynamic bandwidth adjustment can be taken to relieve the transmission pressure; Set the anomaly detection threshold, and judge whether there are abnormal situations in the transmission process of the Internet of Things gas status data according to the interval where the core indicators of dynamic transmission performance are within the anomaly detection threshold; The range of the anomaly detection threshold needs to be set according to the actual application scenario and link performance. Taking the network congestion degree as an example (in actual applications, a comprehensive indicator or the indicator with the highest priority can be selected according to specific requirements to unify the specific value of the entire threshold), if the normal range of the network congestion degree is 0% - 60%, then the anomaly detection threshold is set to 60%; When the core indicator of dynamic transmission performance (network congestion degree) is less than or equal to 60% and greater than 0%, there are no abnormal situations in the transmission process. When the core indicator of dynamic transmission performance is greater than 60%, there are abnormal situations in the transmission process (which may include a decrease in the actual amount of data transmitted and, in extreme cases, the link may be temporarily unable to work properly due to excessive congestion, resulting in data transmission interruption); When abnormal situations occur, select an alternative path and re-execute the bandwidth allocation strategy to obtain the Internet of Things gas transmission performance data (i.e., the packet loss rate, throughput, signal strength, network congestion degree, and latency after re-bandwidth); Integrate the bandwidth allocation plan and the IoT gas transmission performance data (synchronize the timestamps of the bandwidth allocation plan and the IoT gas transmission performance data, and correspond the bandwidth allocation plan and the transmission performance data to each communication link in the order of the communication links) to obtain a transmission log; The transmission log in this step can trace back the time, location, and cause of abnormal problems, so as to quickly locate and solve the problems, and can also help decision-makers formulate more reasonable bandwidth allocation strategies and emergency plans.

[0036] This embodiment also provides an IoT-based intelligent gas data transmission control system, including: a model construction module that collects IoT gas status data, abstracts transmission nodes as graph vertices using a graph theory model, abstracts communication links between transmission nodes as edges, and sets the computing power and bandwidth resources of each transmission node to generate a gas network model; A path generation module that defines a path planning objective function and uses a path planning algorithm to calculate the optimal transmission path between a transmission node and a target node in the gas network model; A bandwidth allocation module that combines the communication link characteristics between transmission nodes on the optimal transmission path and uses a bandwidth allocation algorithm to allocate bandwidth resources to the optimal transmission path to obtain a bandwidth allocation plan; A log generation module that executes the transmission task of the IoT gas status data according to the bandwidth allocation plan, and monitors the communication link status in real time during the transmission process to obtain a transmission log.

[0037] This embodiment also provides a computer device applicable to the situation of the IoT-based intelligent gas data transmission control method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the IoT-based intelligent gas data transmission control method proposed in the above embodiment.

[0038] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, trackball, or touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0039] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for realizing the intelligent gas data transmission and control based on the Internet of Things as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read-Only Memory (EPROM for short), Programmable Read-Only Memory (PROM for short), Read-Only Memory (ROM for short), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0040] In summary, the present invention: defines a path planning objective function, uses a path planning algorithm to calculate the optimal transmission path between transmission nodes and target nodes in the gas network model, executes the transmission task of Internet of Things gas status data according to the bandwidth allocation plan, and monitors the communication link status in real time. It significantly improves the intelligent level of path planning, optimizes the transmission efficiency and energy consumption, and at the same time ensures the real-time performance and reliability of high-priority data. The path planning algorithm selects a highly adaptable ant colony algorithm, which greatly improves the flexibility and robustness of path selection, can quickly find the optimal path that meets the performance requirements in a complex network environment, reduces the failure risk, and at the same time ensures the real-time performance of high-priority data and the flexibility of low-priority data.

[0041] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A smart gas data transmission control method based on the Internet of Things, characterized by: include, Collect IoT gas status data, use graph theory models to abstract transmission nodes as graph vertices, abstract communication links between transmission nodes as edges, set the computing power and bandwidth resources of each transmission node, and generate a gas network model; Define the path planning objective function and use the path planning algorithm to calculate the optimal transmission path between the transmission node and the target node in the gas network model; Combined with the communication link characteristics between transmission nodes on the optimal transmission path, bandwidth allocation algorithm is used to allocate bandwidth resources for the optimal transmission path to obtain a bandwidth allocation plan; The transmission task of IoT gas status data is carried out according to the bandwidth allocation plan. During the transmission process, the communication link status is monitored in real time to obtain the transmission log.

2. The method for controlling smart gas data transmission based on the Internet of Things according to claim 1, characterized in that: The IoT gas status data includes IoT network interface status, resource utilization, gas data priority, gas status temperature and humidity, gas pressure and gas flow.

3. The method for controlling and managing smart gas data transmission based on the Internet of Things according to claim 2, characterized in that: Using the graph theory model, the transmission nodes are abstracted as graph vertices, the communication links between the transmission nodes are abstracted as edges, and the computing power and bandwidth resources of each transmission node are set to generate the gas network model, which includes the following steps: The transmission node includes a smart gas meter, a gateway and a server; Utilize graph theory model to traverse all transmission nodes and regard each transmission node as a graph vertex; Use SNMP to send detection signals to all graph vertices. When the graph vertices respond to the detection signals, the communication link between each graph vertex is determined, and an edge is created and connected to the corresponding graph vertices to form the initial graph structure. Count the attribute indicators of the communication links in the initial graph structure, set weights for the communication link attribute indicators according to actual needs, and obtain the graph structure after weight allocation; Set the computing power and bandwidth resources for each graph vertex according to the specific technical specifications of the hardware device; Graphviz is used to visualize the graph structure after weight allocation and the computing power and bandwidth resources of each graph vertex as a gas network model.

4. The method for controlling and managing smart gas data transmission based on the Internet of Things according to claim 3, characterized in that: Defining the path planning objective function includes the following steps: Based on the attribute indicators of the communication link, the comprehensive available resources and comprehensive quality level of each graph vertex are counted and input into the gas network model to form a weighted graph; According to the weighted graph, the path planning objective function is defined and the total cost of all paths is calculated.

5. The method for controlling smart gas data transmission based on the Internet of Things according to claim 4, characterized in that: The optimal transmission path between the transmission node and the target node in the gas network model is calculated using a path planning algorithm, including the following steps: Based on the total cost of all paths, the ant colony algorithm is selected as the path planning algorithm, and the number of ants, pheromone volatility coefficient, pheromone concentration and heuristic factor are set; Command each ant to start from the starting vertex, and determine the next vertex to move to based on the pheromone concentration and heuristic factor. When the ant moves one step at a time, the path quality of all the vertices and communication links it passes through is recorded until it reaches the target vertex, and the path quality data is obtained. Adjust the pheromone concentration according to the path quality data, traverse all the paths that the ants have passed, and form all the paths into a candidate path set; Use the Pareto optimization method to filter out the Pareto optimal solution from the candidate path set to form a Pareto optimal solution set; Combined with actual demand and gas data priority, a path is selected from the Pareto optimal solution set as the optimal transmission path between the graph vertex and the target graph vertex.

6. The method for controlling smart gas data transmission based on the Internet of Things according to claim 5, characterized in that: Combined with the communication link characteristics between the transmission nodes on the optimal transmission path, the bandwidth allocation algorithm is used to allocate bandwidth resources for the optimal transmission path to obtain a bandwidth allocation plan. The following steps are included: Divide the gas data priority into high priority data and low priority data; Set the transmission time requirements and data volume of high-priority data and the maximum delay range of low-priority data, and determine the bandwidth requirements of high-priority data and low-priority data; Based on bandwidth requirements, a portion of bandwidth is reserved on each communication link of the optimal transmission path as a dynamic adjustment pool and the bandwidth is allocated to high-priority data, and the remaining bandwidth is allocated to low-priority data to obtain an allocation result; According to the allocation results, an intelligent scheduling algorithm is used to analyze the health of the optimal transmission path, identify bottlenecks and adjust the distribution of bandwidth data flows to obtain a complete bandwidth allocation strategy; The bandwidth allocation strategy and the optimal transmission path are organized into a structured form to form a bandwidth allocation plan.

7. The method for controlling and managing smart gas data transmission based on the Internet of Things according to claim 6, characterized in that: According to the bandwidth allocation plan, the task of transmitting the IoT gas status data is performed. During the transmission process, the communication link status is monitored in real time to obtain the transmission log, including the following steps: Use the optimal transmission path to send IoT gas status data from the starting graph vertex to the target graph vertex; During the transmission process, the packet loss rate, throughput and network congestion of IoT gas status data are collected from each communication link as the core indicators of dynamic transmission performance; Set an anomaly detection threshold, and determine whether there is an abnormality in the transmission of IoT gas status data based on the interval within the anomaly detection threshold of the core indicator of dynamic transmission performance; When an abnormal situation occurs, an alternative path is selected and the bandwidth allocation strategy is re-executed to obtain IoT gas transmission performance data; The bandwidth allocation plan and IoT gas transmission performance data are integrated to obtain the transmission log.

8. A smart gas data transmission control system based on the Internet of Things, based on the smart gas data transmission control method based on the Internet of Things according to any one of claims 1 to 7, characterized in that: include, The model building module collects IoT gas status data, uses the graph theory model to abstract the transmission nodes as graph vertices, abstracts the communication links between the transmission nodes as edges, and sets the computing power and bandwidth resources of each transmission node to generate a gas network model; The path generation module defines the path planning objective function and uses the path planning algorithm to calculate the optimal transmission path between the transmission node and the target node in the gas network model; The bandwidth allocation module combines the communication link characteristics between the transmission nodes on the optimal transmission path and uses the bandwidth allocation algorithm to allocate bandwidth resources for the optimal transmission path to obtain a bandwidth allocation plan; The log generation module executes the transmission task of IoT gas status data according to the bandwidth allocation plan. During the transmission process, it monitors the communication link status in real time and obtains the transmission log.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the smart gas data transmission control method based on the Internet of Things described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the smart gas data transmission control method based on the Internet of Things described in any one of claims 1 to 7 are implemented.

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