Data transmission method, medium, system and device of ecological flow monitoring station

Abstract

This invention discloses a data transmission method, medium, system, and device for an ecological flow monitoring station. The method includes: S1, each node broadcasts a ranging message and receives a response message to obtain the distance between nodes; S2, three adjacent nodes interact and compare to determine the intermediate node, which is then connected to the two adjacent nodes to form a link; S3, each node is sequentially assumed to be the selected aggregation node, and the expected value and variance value of the energy consumption rate for the entire link to complete data transmission in one operation are obtained; S4, a function y = -kx + b is specified as the selection criterion for the optimal expected value and variance value, and the variance tolerance b is sequentially increased until the first point satisfies y < -kx + b and the mobile signal strength reaches a preset strength, at which point is considered the optimal aggregation node; data is received and transmitted through this optimal aggregation node. This invention can improve the online rate of the station's remote data center, reduce the station's requirements for mobile signal coverage, and ensure reliable data transmission.

Description

Technical Field

[0001] This invention mainly relates to the field of ecological flow monitoring technology, specifically to a data transmission method, medium, system, and equipment for ecological flow monitoring stations. Background Technology

[0002] Ecological flow monitoring stations are typically distributed non-uniformly and linearly in application environments, with intervals within 250 meters. The transmission equipment within the stations is powered by solar panels and batteries, and connects to remote cloud data centers via GPRS or 4G channels. Previous data transmission equipment has encountered the following technical problems in application:

[0003] 1. When the GPRS or 4G signal is unstable, the data is exhausted, or the device is damaged, it will immediately disconnect from the remote cloud data center. Especially after running for a certain period of time, the offline rate of all data transmission devices managed by the remote cloud data center remains high, requiring immediate on-site maintenance.

[0004] 2. The ideal geographical location for sampling device sensor data is not ideal for GPRS or 4G signals, so a compromise must be made to choose the installation location.

[0005] To improve the online rate of data transmission equipment in remote cloud data centers and reduce the limitations imposed by mobile signal coverage on the installation location of outdoor data transmission equipment, some solutions employ the WSN linear routing algorithm. However, this algorithm still needs to address the following issues:

[0006] 1. Existing algorithms have complex networking methods that take too long to implement, and the optimal path may miss some sites.

[0007] 2. Existing algorithms for selecting the optimal sink node do not effectively consider energy balance and minimum energy consumption, and are complex and impractical. They are still prone to causing the linear network to disconnect due to the rapid consumption of a single node.

[0008] 3. Existing algorithms do not consider the impact of mobile signals such as 4G on WSN networks. Summary of the Invention

[0009] The technical problem to be solved by this invention is: in view of the technical problems existing in the prior art, this invention provides a data transmission method, medium, system and equipment for ecological traffic monitoring stations that improves the online rate of remote data centers and reduces the requirements of stations for mobile signal coverage.

[0010] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0011] A data transmission method for an ecological flow monitoring station, comprising the following steps:

[0012] S1. Each node broadcasts ranging messages and receives response messages from responding nodes, and analyzes the distances of all responding nodes within the wireless signal range.

[0013] S2. Each node selects at most two nearest nodes to determine its two adjacent nodes; the three adjacent nodes are compared interactively to determine the middle node, and then the middle node connects directly with the two adjacent nodes to form the entire WSN network link.

[0014] S3. From the two end nodes of the WSN network link toward the center, each node reports its remaining energy, mobile signal, and routing information in sequence until they meet at the aggregation node; assuming each node in the WSN network link is selected as the aggregation node, obtain the expected value and variance of the energy consumption rate of the entire link in one data transmission.

[0015] S4. Specify a linear function y = -kx + b as the criterion for selecting the optimal expected value and variance value. Gradually increase the variance tolerance b in the function until the first point satisfies y < -kx + b and the signal strength reaches the preset strength. Then, this point is considered the optimal aggregation node. Data from each node is received and transmitted through this optimal aggregation node. Here, k is the variance and expected value weighting coefficient; x is the variance value, and y is the expected value.

[0016] Preferably, the specific process of step S1 is as follows: when broadcasting the ranging message, the broadcast sending time T1 is recorded simultaneously; when receiving the response message from the responding node, the receiving time T2 is recorded; wherein the response message contains the response time t of the responding node;

[0017] The formula for calculating the distance between two sensing nodes is:

[0018]

[0019] Where d is the distance between the two sensing nodes and v is the message propagation speed.

[0020] Preferably, the specific process for obtaining the expected value and variance of the energy consumption rate for data transmission completed in one step of the entire link in step S3 is as follows:

[0021] The distance between node i and node i+1 is d(i,i+1), and the amount of data it receives and transmits is s*i. The energy consumption for transmitting and receiving s*i bits of data at a distance of d(i,i+1) using a first-order wireless communication model is shown in the following formula:

[0022] e Tx (i,d(i,i+1))=s·i·E elec +s·i·ξ·d 2

[0023] eRx (i)=s·i·E elec

[0024] Where s is the amount of data fused by a single node, E elec Let ξ be the energy consumed by the circuit to send and receive 1 bit, and e be the energy required by the power amplifier to send a unit of data. exp Energy consumed for sending and receiving;

[0025] If the linear network has a total of N nodes, and M is the number of the sink node, considering that the sink node can choose any point, calculate the energy consumption of the node to the left of the sink node, the energy consumption of the node to the right of the sink node, and the energy consumption of the sink node itself according to the location of the sink node, and obtain the expected value and variance of the energy consumption rate of the entire link to complete data transmission in one go.

[0026] Preferably, the energy consumed by the node to the left of the convergence node is as follows:

[0027] e Tx (i,d(i,i+1))=s·i·E elec +s·i·ξ·d 2

[0028] e Rx (i)=s·(i-1)·E elec

[0029] e exp (i)=e Tx (i,d(i,i+1))+e Rx (i)

[0030] i∈(1, 2, ..., M-1)

[0031] The energy consumed by the right-hand node of the convergence node is as follows:

[0032] e Tx (i,d(i,i-1))=s·(N-i+1)·E elec +s·(N-i+1)·ξ·d 2

[0033] e Rx (i)=s·(Ni)·E elec

[0034] e exp (i)=e Tx (i,d(i,i+1))+e Rx (i)

[0035] i∈(M+1、、、、N)

[0036] The energy consumed by the convergence node itself is as follows:

[0037] e Tx (M,l(M)=s·N·E GPRS +s·N·η·l 2

[0038] e Rx (M)=s·(M-1)·E GPRS +s·(N-M+1)·E GPRS

[0039] Where l(M) is the distance between the node and the base station, E GPRS The energy consumed by the internal transmission and reception of 1 bit of a mobile signal transmission circuit; e pre This represents the remaining energy from the node's last transmission.

[0040] Preferably, a node with the number j is selected as the aggregation node, and the expected value and variance of the energy consumption rate for the entire link to complete data transmission in one cycle are as follows:

[0041]

[0042]

[0043] Preferably, in step S4, the optimal convergence node is selected according to a fixed period.

[0044] Preferably, in step S4, the signal is a GPRS, 4G, or 5G mobile signal.

[0045] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, the computer program performing the steps of the method described above when run by a processor.

[0046] The present invention also discloses a data transmission system for an ecological flow monitoring station, including a memory and a processor. The memory stores a computer program, which executes the steps of the method described above when run by the processor.

[0047] This invention also discloses a data transmission device for an ecological flow monitoring station, comprising a data acquisition module, a processing module, a storage module, a transmission module, and a wireless networking module; the data acquisition module is connected to the processing module, the wireless networking module is used to connect with other transmission devices and exchange data, and the transmission module is used to remotely exchange data with a cloud data center; the storage module stores a computer program, which executes the steps of the method described above when the processing module is running.

[0048] Compared with the prior art, the advantages of the present invention are as follows:

[0049] This invention considers both the energy efficiency balance and minimum energy consumption of the network, as well as the quality of mobile signals such as GPRS or 4G. By applying a novel linear WSN algorithm, it greatly improves the online rate of remote data centers of ecological flow monitoring stations that are not uniformly distributed on both sides of the watershed and reduces the requirements of the stations for mobile signal coverage.

[0050] This invention periodically and dynamically selects a device as the aggregation node for connecting to a remote cloud data center, taking into account the principles of 4G signal strength, balanced energy efficiency, and minimum energy consumption. Non-aggregation nodes transmit data from the previous node and their own data to the aggregation node in a linear fashion along the network. All data is ultimately transmitted out through the aggregation node.

[0051] The method of the present invention includes wireless ranging, dynamic network entry and exit, and shortest path organization of linear network; periodically calculates energy efficiency balance, energy consumption and mobile signal, and then selects the optimal point as the aggregation node, thereby avoiding the network energy efficiency imbalance caused by using a single site as the aggregation node for a long time, which would cause a single site in the network to run out of power too quickly and the linear network to disconnect. Attached Figure Description

[0052] Figure 1 This is a block diagram of the transmission device in an embodiment of the present invention.

[0053] Figure 2 This is a schematic diagram of the network configuration of each node in this invention.

[0054] Figure 3 This is a schematic diagram of the wireless ranging process of the present invention.

[0055] Figure 4 This is a schematic diagram illustrating the determination of the intermediate node position in this invention.

[0056] Figure 5 This is a schematic diagram illustrating the optimal convergence node selection according to the present invention.

[0057] Figure 6 This is a flowchart of an embodiment of the data transmission method of the present invention. Detailed Implementation

[0058] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0059] like Figure 6 As shown, this embodiment of the invention provides a data transmission method for an ecological flow monitoring station, including the following steps:

[0060] S1. Each node broadcasts ranging messages and receives response messages from responding nodes, and analyzes the distances of all responding nodes within the wireless signal range.

[0061] S2. Each node selects at most two nearest nodes to determine its two adjacent nodes; the three adjacent nodes are compared interactively to determine the middle node, and then the middle node connects directly with the two adjacent nodes to form the entire WSN network link.

[0062] S3. From the two end nodes of the WSN network link toward the center, each node reports its remaining energy, mobile signal, and routing information in sequence until they meet at the aggregation node; assuming each node in the WSN network link is selected as the aggregation node, obtain the expected value and variance of the energy consumption rate of the entire link in one data transmission.

[0063] S4. Specify a linear function y = -kx + b as the criterion for selecting the optimal expected value and variance value. Gradually increase the variance tolerance b in the function until the first point satisfies y < -kx + b and the signal strength reaches the preset strength. Then, this point is considered the optimal aggregation node. Data from each node is received and transmitted through this optimal aggregation node. Here, k is the variance and expected value weighting coefficient; x is the variance value, and y is the expected value.

[0064] This invention considers both the energy efficiency balance and minimum energy consumption of the network, as well as the quality of mobile signals such as GPRS or 4G. By applying a novel linear WSN algorithm, it greatly improves the online rate of remote data centers of ecological flow monitoring stations that are not uniformly distributed on both sides of the watershed and reduces the requirements of the stations for mobile signal coverage.

[0065] The method of the present invention will be further illustrated below through a complete specific embodiment:

[0066] Phase 1: Wireless Signal Ranging

[0067] like Figure 3 As shown, any sensing node not connected to the network broadcasts a ranging message, recording the broadcast time T1. Simultaneously, it receives response messages from nearby sensing nodes that can respond to this broadcast ranging message and records the reception time T2. The response message includes the responding node's response time t. If there is no response, the node abandons the network and operates independently.

[0068] The formula for calculating the distance between two sensing nodes is:

[0069] Each node maintains a distance table containing the distances to all responding nodes within the wireless signal range.

[0070] Phase Two, Chain Formation

[0071] Each node interacts with its two nearest neighbors in the distance table to determine its two adjacent nodes. This process includes the network entry discrimination mode for intermediate nodes and link endpoint nodes.

[0072] like Figure 2 and Figure 4 As shown, three adjacent nodes are compared interactively, and the distance d between the nodes is... AC d CA Since B is the largest node, it becomes the intermediate node. The node then connects to the nodes on either side to establish a network topology, forming the entire WSN network link. The node stops broadcasting ranging messages but can still respond to dynamically added ranging messages to update the link.

[0073] Phase 3: Energy Efficiency Model Calculation

[0074] From both endpoints towards the center, each node sequentially reports its remaining energy, mobile signal, and routing information until they meet. Based on the overall routing table, starting from the endpoints, the energy efficiency model for the entire data chain's data forwarding is calculated, assuming each endpoint is the aggregation node. The specific process is as follows:

[0075] The distance between node i and node i+1 is d(i,i+1), and the amount of data it receives and transmits is s*i. The energy consumption for transmitting and receiving s*i bits of data at a distance of d(i,i+1) using a first-order wireless communication model is shown in the following formula:

[0076] e Tx (i,d(i,i+1))=s·i·E elec +s·i·ξ·d 2

[0077] e Rx (i)=s·i·E elec

[0078] Where s is the amount of data fused by a single node, E elec Let ξ be the energy consumed by the circuit to send and receive 1 bit, and e be the energy required by the power amplifier to send a unit of data. exp Energy is consumed for sending and receiving.

[0079] Assuming the linear network has N nodes in total, and M is the sink node number, considering that the sink node could choose any point, the calculation is based on the sink node's location:

[0080] The energy consumed by the node to the left of the convergence node is as follows:

[0081] e Tx (i,d(i,i+1))=s·i·E elec +s·i·ξ·d 2

[0082] e Rx (i)=s·(i-1)·E elec

[0083] e exp (i)=e Tx (i,d(i,i+1))+e Rx (i)

[0084] i∈(1, 2, ..., M-1)

[0085] The energy consumed by the right-hand node is as follows (for non-converging nodes):

[0086] e Tx (i,d(i,i-1))=s·(N-i+1)·E elec +s·(N-i+1)·ξ·d 2

[0087] e Rx (i)=s·(Ni)·E elec

[0088] e exp (i)=e Tx (i,d(i,i+1))+e Rx (i)

[0089] i∈(M+1、、、、N)

[0090] The energy consumption of the convergence node is as follows:

[0091] e Tx (M,l(M)=s·N·E GPRS +s·N·η·l 2

[0092] e Rx (M)=s·(M-1)·E GPRS +s·(N-M+1)·E GPRS

[0093] Where l(M) is the distance between the node and the base station, E GPRS The energy consumed by a mobile signal transmission circuit to transmit and receive 1 bit. pre Given the remaining energy from the previous transmission of a node, and considering a node with the ID j as the sink node, the expected and variance values ​​of the energy consumption rate for the entire link to complete a single data transmission are as follows:

[0094]

[0095]

[0096] Assuming each node in the chain is selected as the sink node, obtain two-dimensional data of expected and variance values, such as... Figure 5 As shown.

[0097] Phase Four: Optimal Optimum Selection

[0098] like Figure 5 As shown, in a coordinate system with variance as the x-axis and expected value as the y-axis, in the function y = -kx + b, -k represents the variance and expected value weighting coefficients, which can be determined through actual testing and then fixed. b is the variance tolerance, initially 0, and its value is increased periodically at a certain ratio until a point satisfies y ≤ -kx + b and the 4G mobile signal is good. This point is then the optimal aggregation node obtained by the algorithm, and its selection as the aggregation node and the overall routing table are broadcast to the entire linear chain.

[0099] The selection of the optimal sink node can be carried out according to a fixed cycle. If the selection frequency is too high, it will increase the energy consumption calculated by the model; if the selection frequency is too low, the running time of a single node as a fixed sink node will be too long, and the energy imbalance of the whole chain will become more serious.

[0100] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when run by a processor, performs the steps of the method described above. This invention further provides a data transmission system for an ecological flow monitoring station, including a memory and a processor, wherein the memory stores a computer program, which, when run by a processor, performs the steps of any of the methods described above.

[0101] like Figure 1 As shown, this embodiment of the invention also provides a data transmission device for an ecological flow monitoring station, including a data acquisition module, a processing module, a storage module, a 4G transmission module, and a wireless networking module. The storage module stores a computer program, which executes the steps of the method described above when the processing module runs. The 4G transmission module can remotely transmit data with a cloud data center, and the wireless networking module can connect and exchange data with other transmission devices. The data acquired by the acquisition module includes ecological monitoring data such as water level and flow rate. The transmission device installed outdoors is powered by solar energy and batteries. The processing module uses an ARM, microcontroller, or x86 chip.

[0102] The aforementioned transmission equipment is installed inside ecological flow stations, which are generally distributed at intervals along the edge of the watershed. Stations within a certain range can form a linear WSN network. Using the above method, a site with optimal overall performance is selected as the aggregation node to forward data from the entire network. When the GPRS or 4G signal at the equipment installation location is strong and no other devices require network integration, it operates independently. If the GPRS or 4G signal at the equipment installation location is poor or no other devices are available, a WSN network can be established, ultimately forming a linear WSN network by connecting the closest devices in a daisy-chain manner. This invention, considering 4G signal strength, balanced energy efficiency, and minimum energy consumption, periodically and dynamically selects a device as the aggregation node connecting to the remote cloud data center. Non-aggregation nodes transmit data from the previous node and their own data linearly towards the aggregation node, and all data is ultimately transmitted through the aggregation node.

[0103] The method of this invention includes wireless ranging, dynamic network entry and exit, and shortest path organization of a linear network; it periodically calculates energy efficiency balance, energy consumption, and mobile signal strength, and then selects the optimal point as the aggregation node. This avoids the network energy efficiency imbalance caused by using a single site as the aggregation node for an extended period, which could lead to individual sites in the network running out of power too quickly and causing the linear network to disconnect.

[0104] Of course, in other embodiments, the above method can also be applied to other occasions besides monitoring ecological flow sites.

[0105] As shown in this disclosure and the claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. The terms "first," "second," and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms "comprising" or "including" mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, without excluding other elements or objects. The terms "connected" or "linked" are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect.

[0106] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A data transmission method for an ecological flow monitoring station, characterized in that, Including the following steps: S1. Each node broadcasts ranging messages and receives response messages from responding nodes, and analyzes the distances of all responding nodes within the wireless signal range. S2. Each node selects at most two nearest nodes to determine its two adjacent nodes; the three adjacent nodes are compared interactively to determine the middle node, and then the middle node connects directly with the two adjacent nodes to form the entire WSN network link. S3. From the two end nodes of the WSN network link toward the center, each node reports its remaining energy, mobile signal, and routing information in sequence until they meet at the aggregation node; assuming each node in the WSN network link is selected as the aggregation node, obtain the expected value and variance of the energy consumption rate of the entire link in one data transmission. S4. Specify a linear function y = -kx + b as the criterion for selecting the optimal expected value and variance value. Gradually increase the variance tolerance b in the function until the first point satisfies y < -kx + b and the signal strength reaches the preset strength. This point is then considered the optimal aggregation node. Data from each node is received and transmitted through this optimal aggregation node. Here, k is the variance and expected value weighting coefficient; x is the variance value; and y is the expected value. The specific process of step S1 is as follows: when broadcasting the ranging message, the broadcast sending time T1 is recorded simultaneously; when receiving the response message from the responding node, the receiving time T2 is recorded; wherein the response message contains the response time t of the responding node; The formula for calculating the distance between two sensing nodes is: Where d is the distance between the two sensing nodes, and v is the message propagation speed; The specific process for obtaining the expected value and variance of the energy consumption rate for data transmission completed in one step of the entire link in step S3 is as follows: The distance between node i and node i+1 is d(i,i+1), and the amount of data it receives and transmits is s*i. The energy consumption for transmitting and receiving s*i bits of data at a distance of d(i,i+1) using a first-order wireless communication model is shown in the following formula: in For the amount of data merged by a single node, The energy consumed by the circuit to send and receive 1 bit. The energy required for a power amplifier to send a unit of data. Energy consumed for sending and receiving; Assuming the linear network has a total of N nodes, and M is the number of the sink node, considering that the sink node can choose any point, calculate the energy consumption of the node to the left of the sink node, the energy consumption of the node to the right of the sink node, and the energy consumption of the sink node itself according to the position of the sink node, and obtain the expected value and variance of the energy consumption rate of the entire link to complete data transmission in one go. The energy consumed by the node to the left of the convergence node is as follows: The energy consumed by the right-hand node of the convergence node is as follows: The energy consumed by the convergence node itself is as follows: in The distance between the node and the base station. The energy consumed by the internal transmission and reception of 1 bit of a mobile signal transmission circuit; This represents the remaining energy from the node's last transmission. If a node with the ID j is selected as the aggregation node, the expected value and variance of the energy consumption rate for the entire link to complete data transmission in one cycle are as follows: 。 2. The data transmission method for the ecological flow monitoring station according to claim 1, characterized in that, In step S4, the optimal convergence node is selected according to a fixed period.

3. The data transmission method for an ecological flow monitoring station according to any one of claims 1 to 2, wherein in step S4, the signal is a GPRS, 4G, or 5G mobile signal.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when run by a processor, performs the steps of the method as described in any one of claims 1 to 3.

5. A data transmission system for an ecological flow monitoring station, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, The computer program, when run by a processor, performs the steps of the method as described in any one of claims 1 to 3.

6. A data transmission device for an ecological flow monitoring station, characterized in that, It includes a data acquisition module, a processing module, a storage module, a transmission module, and a wireless networking module; the data acquisition module is connected to the processing module, the wireless networking module is used to connect with other transmission devices and exchange data, and the transmission module is used to remotely exchange data with a cloud data center; the storage module stores a computer program, which executes the steps of the method as described in any one of claims 1 to 3 when the processing module is running.

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