A Beidou dual-network channel control method and system based on AI control device

Abstract

The application discloses a Beidou double-network channel control method and system based on an AI control device, and the method comprises the following steps: when a transmission signal sent by a central control device is received, it is judged whether a Beidou communication channel is an optimal communication channel; when the Beidou communication channel is not the optimal communication channel, the positions of AI control devices in each water conservancy monitoring area are used to construct a water conservancy monitoring ad hoc network; communication abnormal nodes in the water conservancy monitoring ad hoc network are screened to obtain communication coverage blind area nodes; the communication coverage blind area nodes and the communication signal strongest nodes are networked, and it is judged whether the direct connection condition is met; if the direct connection condition is met, direct connection is performed; if the communication coverage blind area nodes and the communication signal strongest nodes do not meet the direct connection condition, a relay node of the water conservancy monitoring ad hoc network is acquired; the relay node is connected with the communication coverage blind area nodes to obtain a target water conservancy monitoring ad hoc network. The application can effectively improve the accuracy of water regime monitoring.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the technical field of communication, in particular to a Beidou dual-network channel control method and system based on an AI control device. BACKGROUND

[0002] Water conservancy hub engineering is an important part of the water conservancy engineering system, which is usually composed of various types of hydraulic structures to achieve the goal of flood control and water resources development. Water regime management of water conservancy hub engineering is of great significance, as it directly affects the safe operation of the project, flood control and disaster reduction effect, efficient use of water resources, and protection of the ecological environment.

[0003] Currently, water regime monitoring of water conservancy hub engineering is mainly through hydrological monitoring stations, which are usually set up in remote areas, such as rivers, lakes, reservoirs, and other water areas, to directly monitor and record hydrological data. These areas often lack stable communication network coverage, such as 4G / 5G signals and optical fiber networks, due to remote geographical location, complex terrain, and weak infrastructure, resulting in delayed communication data transmission and affecting the efficiency and accuracy of hydrological monitoring.

[0004] Therefore, there is an urgent need for a Beidou dual-network channel control method and system based on an AI control device to improve the accuracy of water regime monitoring. SUMMARY

[0005] The embodiment of the present application provides a Beidou dual-network channel control method and system based on an AI control device to improve the accuracy of water regime monitoring.

[0006] To achieve the above-mentioned purpose, the embodiments of the present application adopt the following technical solutions:

[0007] In a first aspect, a Beidou dual-network channel control method based on an AI control device is provided, applied to an AI control device, the number of AI control devices is at least two, the AI control device is set in a water conservancy monitoring area, and the water conservancy monitoring area is connected with a central control device. The method comprises:

[0008] In response to receiving a transmission signal sent by the central control device, it is determined whether the Beidou communication channel is the best communication channel, wherein the transmission signal is used to transmit water conservancy monitoring data;

[0009] When the Beidou communication channel is not the best communication channel, the positions of all AI control devices in each water conservancy monitoring area are obtained;

[0010] A water conservancy monitoring ad hoc network is constructed based on the positions of all AI control devices in each water conservancy monitoring area, wherein each AI control device is a node of the water conservancy monitoring ad hoc network;

[0011] Obtaining node information of the water conservancy monitoring ad hoc network;

[0012] Filtering communication abnormal nodes in the water conservancy monitoring ad hoc network according to the node information to obtain communication coverage blind area nodes in the water conservancy monitoring ad hoc network;

[0013] Obtaining the node with the strongest communication signal in the water conservancy monitoring ad hoc network;

[0014] Networking the node in the communication blind area with the node with the strongest communication signal, and determining whether the node in the communication blind area and the node with the strongest communication signal meet direct connection conditions;

[0015] If the node in the communication blind area meets the direct connection condition with the node with the strongest communication signal, the node in the communication blind area is directly connected to the node with the strongest communication signal to obtain the target water conservancy monitoring ad hoc network, wherein the direct connection condition is that the communication protocol of the node in the communication blind area is the same as that of the node with the strongest communication signal;

[0016] If the communication coverage blind area node and the node with the strongest communication signal do not meet the direct connection condition, obtain the relay node of the water conservancy monitoring ad hoc network;

[0017] The relay node is connected to the communication coverage blind area node to obtain a target water conservancy monitoring ad hoc network.

[0018] In another possible implementation of the first aspect, the method further includes:

[0019] When the Beidou communication channel is the optimal communication channel, converting the water conservancy monitoring data into a water conservancy monitoring digital signal;

[0020] Transmitting the water conservancy monitoring digital signal to the Beidou satellite using the AI ​​control device;

[0021] Acquire a target area covered by the BeiDou satellite signal, and determine the central control device in the target area;

[0022] Transmitting the water conservancy monitoring digital signal to the central control device;

[0023] The water conservancy monitoring digital signal transmitted by the Beidou satellite is decoded by the central control device to obtain readable water conservancy monitoring data.

[0024] In another possible implementation of the first aspect, determining whether the Beidou communication channel is an optimal communication channel includes:

[0025] Determining a communication scenario for each of the water conservancy monitoring areas based on the geographic location information;

[0026] By using the communication scenario, the water conservancy monitoring area is divided into a plurality of target monitoring areas;

[0027] Obtaining signal strength change data and data volume change values ​​of the water conservancy monitoring data transmitted by each target monitoring area within a preset time period;

[0028] Determining a communication stability threshold of each target monitoring area by using the signal strength change data and the signal data volume change data within a preset time period;

[0029] Acquiring communication parameters of each target monitoring area;

[0030] Inputting the communication parameters into a pre-trained model to obtain a current communication stability value of each target monitoring area;

[0031] Comparing the communication stability value corresponding to each communication scenario with the communication stability threshold, and marking the number of the communication scenarios in which the communication stability value is greater than the communication stability threshold;

[0032] If the number is greater than a preset threshold, determining that the Beidou communication channel is the optimal communication channel;

[0033] If the number is less than a preset threshold, it is determined that the Beidou communication channel is not the optimal communication channel.

[0034] In another possible implementation of the first aspect, determining the communication stability threshold of each target monitoring area by using the signal strength change data and the signal data volume change data within a preset time period includes:

[0035] Using a preset recognition algorithm, identifying data with a signal-to-noise ratio less than a preset noise ratio threshold and data with a signal-to-noise ratio greater than a preset noise ratio threshold in the signal strength change data of each target monitoring area;

[0036] Using a preset recognition algorithm, identifying data with a signal packet loss rate less than a preset packet loss rate threshold and data with a signal packet loss rate greater than a preset packet loss rate threshold in the signal data volume change data of each target monitoring area;

[0037] Marking the data whose signal-to-noise ratio is less than a preset noise ratio threshold and the data whose signal packet loss rate is less than a preset packet loss rate threshold as true stable cases;

[0038] Marking the data whose signal-to-noise ratio is greater than a preset noise ratio threshold and the data whose signal packet loss rate is greater than a preset packet loss rate threshold as false stable cases;

[0039] inputting the true positive examples into the trained binary classification algorithm, and outputting true positive examples and false positive examples;

[0040] inputting the false positive examples into the binary classification algorithm, and outputting true negative examples and false negative examples;

[0041] calculating a true stability rate through the true positive examples and the false negative examples;

[0042] calculating a false stability rate through the false positive examples and the true negative examples;

[0043] drawing a communication stability threshold curve of each target monitoring area by taking the false stability rate as the horizontal axis and the true stability rate as the vertical axis;

[0044] determining a threshold boundary point of the communication stability threshold curve of each target monitoring area through a preset optimal threshold determination formula;

[0045] determining a communication stability threshold of each target monitoring area through the threshold boundary point.

[0046] In a possible implementation of the first aspect, the method further includes:

[0047] acquiring, in the node information, a total number of times of sending data by each node and a number of times of successful sending data;

[0048] calculating a communication success rate of each node through the total number of times of sending data and the number of times of successful sending data;

[0049] calculating a correlation coefficient between any two nodes through the communication success rate;

[0050] performing cluster analysis on the correlation coefficient to obtain a node with an abnormal correlation coefficient;

[0051] determining a target monitoring area in which the node with the abnormal correlation coefficient is located, to obtain an abnormal target monitoring area;

[0052] acquiring all node information in the abnormal target monitoring area;

[0053] acquiring a topological structure of the abnormal target monitoring area and extracting a topological structure feature;

[0054] inputting the topological structure feature and the all node information in the abnormal target monitoring area into a trained communication connection effect level model, and outputting a target connection effect of the abnormal target monitoring area;

[0055] The target connection effect is compared with a preset standard connection effect. If the target connection effect is less than the pre-calculated standard connection effect, a communication coverage blind spot node in the water conservancy monitoring ad hoc network is obtained.

[0056] In another possible implementation of the first aspect, obtaining the relay node of the water conservancy monitoring ad hoc network and connecting the relay node to the communication coverage blind spot node to obtain the target water conservancy monitoring ad hoc network includes:

[0057] All the node information is exchanged arbitrarily among all the nodes in the water conservancy monitoring ad hoc network through a preset communication protocol;

[0058] By performing preliminary matching on any node information received, candidate relay nodes are obtained;

[0059] Calculating a hash value between the candidate relay node and the node in the communication blind spot using a preset hash value calculation algorithm, wherein if the hash value is less than a preset threshold, the node in the communication blind spot is configured to send a connection request to the candidate relay node;

[0060] In response to the connection request, rematching the information of the candidate relay node with the information of the node in the communication coverage blind area;

[0061] Among them, when the matching degree of the re-matching is greater than the preset matching degree threshold, the candidate relay node replies to the connection request of the communication coverage blind spot node and connects with the communication coverage blind spot node to obtain the target water conservancy monitoring self-organizing network. When the matching degree is less than the preset matching degree threshold, the candidate relay node does not reply to the connection request of the communication coverage blind spot node, and the communication coverage blind spot node will repeat the step of performing preliminary matching on the node information received through any node information to obtain the candidate relay node.

[0062] In another possible implementation of the first aspect, after the candidate relay node replies to the connection request of the communication blind area node and connects to the communication blind area node to obtain the target water conservancy monitoring ad hoc network, the method further includes:

[0063] Obtaining a first transmission performance of the water conservancy monitoring ad hoc network that meets a direct connection condition and a first node transmission performance between each of the AI ​​control devices;

[0064] Obtaining a second transmission performance of the target water conservancy monitoring ad hoc network and a second node transmission performance between each of the AI ​​control devices and the relay node;

[0065] comparing the first transmission performance with the second transmission performance, and comparing the first node transmission performance with the second node transmission performance;

[0066] if the first transmission performance is less than or equal to the second transmission performance, and the first node transmission performance is less than or equal to the second node transmission performance, obtaining the target water conservancy monitoring ad hoc network after the communication coverage blind area node is connected with the candidate relay node.

[0067] In a possible implementation of the first aspect, the method further includes:

[0068] obtaining climate monitoring data of each water conservancy monitoring area in real time;

[0069] inputting the climate monitoring data into a prediction model to obtain climate prediction information of each water conservancy monitoring area;

[0070] collecting water regime monitoring data of each water conservancy monitoring area in real time through the AI control device;

[0071] obtaining water regime change data of each water conservancy monitoring area in a future trend through the water regime monitoring data and the climate prediction information;

[0072] generating a water conservancy regulation scheme of each water conservancy monitoring area through the water regime change data of each water conservancy monitoring area in the future trend.

[0073] In a second aspect, the present application provides a machine readable storage medium, which stores instructions for causing a machine to execute the Beidou double-network channel control method based on an AI control device.

[0074] In a third aspect, the present application provides a Beidou double-network channel control system based on an AI control device, which includes:

[0075] a memory configured to store instructions; and

[0076] a processor configured to call the instructions from the memory and enable the Beidou double-network channel control method based on the AI control device when the instructions are executed.

[0077] By the above technical solution, by judging whether the Beidou communication channel is the best communication channel, and constructing a water conservancy monitoring ad hoc network in a non-optimal situation, the method can dynamically adjust the communication path, ensure the real-time and stable transmission of water conservancy monitoring data, help to reduce communication delay and packet loss rate, and improve the overall communication efficiency. By identifying the communication coverage blind area node and connecting it with the communication signal strongest node or relay node, the method can significantly optimize the network coverage of the water conservancy monitoring area. This ensures that stable data transmission can be achieved even in remote or weak signal areas, improving the reliability and comprehensiveness of the monitoring system. In the process of constructing the ad hoc network, the method filters the communication abnormal nodes and ensures the consistency of the communication protocol, enhancing the security of the network. This helps to prevent data leakage and illegal access, protecting the security and integrity of water conservancy monitoring data. Combined with AI technology and Beidou communication technology, intelligent management of the water conservancy monitoring system is realized. Through real-time monitoring and analysis of network status, the AI control device can automatically adjust the communication strategy, improving the response speed and decision-making ability of the monitoring system.

[0078] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific embodiments section. BRIEF DESCRIPTION OF DRAWINGS

[0079] Figure 1 A flowchart of a Beidou double-network channel control method based on an AI control device provided by the embodiments of the present application;

[0080] Figure 2 A flowchart of determining a candidate relay node provided by the embodiments of the present application;

[0081] Figure 3 A structural diagram of a water conservancy monitoring ad hoc network provided by the embodiments of the present application. DETAILED DESCRIPTION

[0082] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. It should be understood that the specific embodiments described here are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present application.

[0083] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement condition, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly.

[0084] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0085] Figure 1 The following schematically shows a flow chart of a Beidou dual network channel control method based on an AI control device according to an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a Beidou dual-network channel control method based on an AI control device, which is applied to the AI ​​control device. The number of AI control devices is at least two. The AI ​​control device is arranged in a water conservancy monitoring area, and the water conservancy monitoring area is connected to the central control equipment. The method may include the following steps.

[0086] S101, in response to receiving a transmission signal sent by a central control device, determining whether a Beidou communication channel is an optimal communication channel, wherein the transmission signal is used to transmit water conservancy monitoring data;

[0087] S102. When the Beidou communication channel is not the optimal communication channel, obtain the positions of all AI control devices in each water conservancy monitoring area;

[0088] S103: Building a water conservancy monitoring ad hoc network based on the locations of all AI control devices in each water conservancy monitoring area, wherein each AI control device is a node of the water conservancy monitoring ad hoc network;

[0089] S104, obtaining node information of the water conservancy monitoring ad hoc network;

[0090] S105. Filtering communication abnormal nodes in the water conservancy monitoring ad hoc network based on the node information to obtain communication blind area nodes in the water conservancy monitoring ad hoc network;

[0091] S106, obtaining the node with the strongest communication signal in the water conservancy monitoring ad hoc network;

[0092] S107, networking the node in the communication blind area with the node with the strongest communication signal, and determining whether the node in the communication blind area and the node with the strongest communication signal meet direct connection conditions;

[0093] S108, if the communication coverage blind area node and the communication signal strongest node meet the direct connection condition, the communication coverage blind area node and the communication signal strongest node are directly connected to obtain the target water conservancy monitoring ad hoc network, wherein the direct connection condition is that the communication protocol of the communication coverage blind area node and the communication signal strongest node is the same;

[0094] S109, if the communication coverage blind area node and the communication signal strongest node do not meet the direct connection condition, a relay node of the water conservancy monitoring ad hoc network is acquired;

[0095] S110, the relay node and the communication coverage blind area node are connected to obtain the target water conservancy monitoring ad hoc network.

[0096] The AI control device receives the transmission signal sent by the central control equipment, wherein the transmission signal is used to transmit water conservancy monitoring data, and in this embodiment, the AI control device can be a telemetry terminal equipment, which is a microprocessor controlled electronic device. The AI control device is a key equipment in the water conservancy monitoring area, responsible for receiving transmission signals from the central control equipment, which contains water conservancy monitoring data to be transmitted and related control instructions. After receiving the transmission signal, the AI control device will immediately evaluate the current Beidou communication channel and determine whether the Beidou communication channel is the nearest communication channel. If the Beidou communication channel is determined to be the best communication channel, the AI control device will directly use the channel for water conservancy monitoring data transmission. If the Beidou communication channel is not the best communication channel, the AI control device will take further action to find other available communication channels, such as other satellite communication channels or ground communication networks, or build a water conservancy monitoring ad hoc network to optimize the data transmission path.

[0097] When the Beidou communication channel is not the best communication channel, the positions of all AI control devices in each water conservancy monitoring area are acquired, that is, due to various factors such as weather, terrain, building shielding, etc., the Beidou communication channel may be affected, resulting in poor communication quality. At this time, the positions of all AI control devices in each water conservancy monitoring area are acquired, which can be realized by a geographic information system, which is a comprehensive technical system integrating computer hardware, software and geographic data, used for comprehensive and systematic collection, storage, processing, analysis, display and description of geographic information. The geographic information system is used to digitize the water conservancy monitoring area, and the position information of the AI control device is marked on the map in the form of point, line and surface, etc. to obtain the positions of all AI control devices in each water conservancy monitoring area.

[0098] After obtaining the positions of all AI control devices in each water conservancy monitoring area, a water conservancy monitoring ad hoc network is constructed based on the positions of the AI ​​control devices in each water conservancy monitoring area. In this embodiment, each AI control device is used as a node of the water conservancy monitoring ad hoc network. Through the location information of the AI ​​control devices in each water conservancy monitoring area, a water conservancy monitoring ad hoc network is constructed, which can collect, transmit and process water conservancy monitoring data in real time. The AI ​​control device in each water conservancy monitoring area is used as a node of the water conservancy monitoring ad hoc network, and each node is set and configured, and a unique identifier, such as an IP address or MAC address, is configured for unique identification in the network. Subsequently, according to the characteristics and needs of the water conservancy monitoring area, the network topology of the water conservancy monitoring ad hoc network is designed, and a suitable network topology is selected, such as a star, mesh or hybrid topology. Taking the mesh structure as an example, the embodiment of the present application provides a structural schematic diagram of a water conservancy monitoring ad hoc network, as shown in FIG. Figure 3 As shown, in Figure 3 In the example, nodes 1, 2, 3, 4, and 6 form a water conservancy monitoring ad hoc network, which is used to monitor water conditions in the water conservancy system. Nodes are connected in no fixed order. After selecting the appropriate topology, network parameters such as the communication protocol, data transmission rate, and network address need to be configured. Based on the communication capabilities of the nodes and the characteristics of the water conservancy monitoring data, appropriate communication protocols such as ZigBee, LoRa, and NB-IoT are selected. Communication module parameters such as the communication frequency, transmit power, and receive sensitivity are also configured. Finally, a water conservancy monitoring ad hoc network is constructed based on the location of the AI ​​control devices in each water conservancy monitoring area.

[0099] After building a water conservancy monitoring ad hoc network, you can obtain node information for each network by directly accessing the network management system. This network is equipped with a network management system, allowing you to directly query each node's status, configuration, data transmission, and other information. For some node devices, you can directly read node-related information such as device identification, status, and data through their built-in interfaces.

[0100] After obtaining the node information of each water conservancy monitoring self-organizing network, the communication abnormal nodes in the water conservancy monitoring self-organizing network are screened according to the node information to obtain the communication coverage blind spot nodes in the water conservancy monitoring self-organizing network. In this embodiment, the communication coverage blind spot nodes are nodes that cannot effectively receive signals from other nodes due to obstruction of signal transmission or insufficient signal strength, thus becoming nodes in the communication coverage blind spot. That is to say, by analyzing the node communication status, such as the node's signal strength, communication rate, packet loss rate and other data, if the node's signal strength is lower than a certain threshold, it may indicate that the node is in a communication coverage blind spot; if the node's communication rate is far lower than the normal level, it may indicate that the node has a communication problem; if the node's packet loss rate is too high, it may indicate that the node's communication link is unstable; by analyzing the node's signal strength, communication rate, packet loss rate and other data, it is possible to screen out nodes with abnormal communication. After screening out the communication abnormal nodes, it is necessary to further analyze the location information of these nodes to determine whether there is a communication coverage blind spot. First, based on the location information of the nodes, the network topology diagram of the water conservancy monitoring self-organizing network is drawn, and the nodes with communication abnormalities are marked in the network topology diagram. The distribution of the nodes with communication abnormalities is analyzed and observed. If the number of abnormal nodes in certain areas is large or appears continuously, it may indicate that there is a communication blind spot in the area. The communication blind spot nodes in the water conservancy monitoring self-organizing network are obtained.

[0101] Subsequently, the node with the strongest communication signal in the water conservancy monitoring ad hoc network can be obtained by collecting communication data, analyzing the collected communication data, especially the signal strength data, and comparing the signal strength values ​​of different nodes to find the node with the strongest signal.

[0102] After obtaining the node with the strongest communication signal in the water conservancy monitoring ad hoc network, the node in the blind area is networked with the node with the strongest communication signal to determine whether the blind area node and the node with the strongest communication signal meet the direct connection conditions. In this embodiment, the direct connection condition is met only if the communication protocol between the blind area node and the node with the strongest communication signal is the same. In other words, by determining whether the communication protocol between the blind area node and the node with the strongest communication signal is the same, it is determined whether the blind area node and the node with the strongest communication signal can be directly connected.

[0103] If the node in the coverage blind spot meets the direct connection conditions with the node with the strongest communication signal—that is, if they use the same communication protocol—then the node in the coverage blind spot and the node with the strongest communication signal can be directly connected to form the target water conservancy monitoring ad hoc network. The consistency of the communication protocol is key to ensuring normal communication between the two nodes. After directly connecting the node in the coverage blind spot to the node with the strongest communication signal, the architecture of the water conservancy monitoring ad hoc network needs to be designed based on actual needs to achieve the target water conservancy monitoring ad hoc network.

[0104] If the node in the blind spot does not meet the direct connection conditions with the node with the strongest communication signal, obtain a relay node in the water conservancy monitoring ad hoc network. In other words, when the communication protocol of the node in the blind spot is different from that of the node with the strongest communication signal, for example, due to compatibility issues, some devices may not support a specific communication protocol version or function, resulting in an inability to communicate with other devices. Obtain a relay node and deploy it near the node in the blind spot by considering factors such as the relay node's location, communication range, transmission power, receiving sensitivity, and compatibility with other nodes to ensure the relay node's communication stability.

[0105] Connect relay nodes to nodes in blind areas to create the target water conservancy monitoring ad hoc network. Use appropriate communication technologies and equipment, such as Wi-Fi, microwave, and satellite communications, to connect relay nodes to nodes in blind areas. Wirelessly connect relay nodes to nodes in blind areas to create the target water conservancy monitoring ad hoc network.

[0106] By determining whether the Beidou communication channel is optimal and constructing a water conservancy monitoring ad hoc network when it is not, this method dynamically adjusts the communication path to ensure real-time and stable transmission of water conservancy monitoring data, helping to reduce communication delays and packet loss, and improving overall communication efficiency. By identifying nodes in communication blind spots and connecting them to nodes with the strongest communication signals or relay nodes, this method significantly optimizes network coverage in the water conservancy monitoring area. This ensures stable data transmission even in remote or weak signal areas, improving the reliability and comprehensiveness of the monitoring system. During the construction of the ad hoc network, this method enhances network security by screening out communication anomalies and ensuring consistency of communication protocols. This helps prevent data leakage and unauthorized access, protecting the security and integrity of water conservancy monitoring data. Combining AI and Beidou communication technologies enables intelligent management of the water conservancy monitoring system. By monitoring and analyzing network status in real time, the AI ​​control device automatically adjusts communication strategies, improving the monitoring system's responsiveness and decision-making capabilities.

[0107] In one implementation of this embodiment, the method further includes:

[0108] S210, when the Beidou communication channel is the optimal communication channel, converting the water conservancy monitoring data into a water conservancy monitoring digital signal;

[0109] S220, transmits water conservancy monitoring digital signals to BeiDou satellites using an AI control device;

[0110] S230: Acquire a target area covered by BeiDou satellite signals and determine a central control device in the target area;

[0111] S240, transmitting the water conservancy monitoring digital signal to the central control equipment;

[0112] S250: Decode the water conservancy monitoring digital signal transmitted by the Beidou satellite through the central control equipment to obtain readable water conservancy monitoring data.

[0113] When the Beidou communication channel is determined to be the optimal communication channel, the water conservancy monitoring data is converted from analog signals to digital signals. This can be achieved through an analog-to-digital converter, which can convert continuous analog signals into discrete digital signals. During the conversion process, it is necessary to select an appropriate sampling rate and resolution to ensure that the digital signal can accurately reflect the characteristics of the original analog signal.

[0114] After obtaining the water conservancy monitoring digital signal, the water conservancy monitoring digital signal is transmitted to the Beidou satellite using the AI ​​control device. First, a stable connection needs to be established between the AI ​​control device and the water conservancy monitoring digital signal. The connection between the AI ​​control device and the water conservancy monitoring digital signal is established through antenna alignment, signal capture and other steps. Subsequently, in order to ensure the integrity and security of the data during transmission, the AI ​​control device will encode and package the water conservancy monitoring digital signal. The encoding process may involve measures such as data compression and encryption; the packaging process usually includes adding data packet headers, checksums and other information. After establishing the connection and completing the data encoding and packaging, the AI ​​control device will transmit the data packet to the designated receiving end through the Beidou satellite communication system. During this process, it is necessary to ensure that key indicators such as data transmission rate and bit error rate meet the requirements.

[0115] After the water conservancy monitoring digital signal is transmitted to the Beidou satellite using the AI ​​control device, the target area covered by the Beidou satellite signal is obtained, and the central control equipment in the target area is determined. That is to say, first, the target area covered by the Beidou satellite navigation system signal is determined. Among them, within the target area covered by the Beidou satellite signal, the central control equipment is a key part to ensure the normal operation and data processing of the Beidou satellite navigation system. The central control equipment in the target area is determined. In this embodiment, the central control equipment can be a data processing center, a device that processes and analyzes the received data. The central control equipment can be a telemetry terminal device. The telemetry terminal device is a multi-functional device that can collect data from various sensors and devices, and transmit the data to the control center or data center for analysis and processing.

[0116] After determining the central control equipment in the target area, the water conservancy monitoring digital signal is transmitted to the central control equipment. The water conservancy monitoring digital signal is transmitted to the central control equipment using communication satellites such as Beidou satellites. The Beidou satellite navigation system is a global satellite navigation system that can provide a high-precision positioning and navigation basic system.

[0117] When the central control equipment receives the water conservancy monitoring digital signal from the Beidou satellite, it may be encoded and compressed to save bandwidth and improve efficiency during transmission. First, the received signal is preprocessed, including signal amplification, filtering, and noise removal, to improve signal quality and readability. Subsequently, the received signal needs to be further decoded to be converted into readable water conservancy monitoring data. Based on the signal format and encoding method transmitted by the Beidou satellite, the corresponding decoding algorithm is selected, such as binary phase shift keying or orthogonal phase shift keying. The preprocessed signal is decoded by the decoding algorithm to extract the original water conservancy monitoring digital signal and obtain readable water conservancy monitoring data.

[0118] By using Beidou satellites to transmit water conservancy monitoring digital signals to central control equipment, not only can efficient and reliable transmission of water conservancy monitoring data be achieved, but it can also promote the intelligent development of water conservancy management systems and provide strong support for the rational development and utilization of water resources.

[0119] In one implementation of this embodiment, determining whether the Beidou communication channel is the optimal communication channel includes:

[0120] S310, determining a communication scenario for each water conservancy monitoring area based on geographic location information;

[0121] S320, dividing the water conservancy monitoring area into multiple target monitoring areas through the communication scenario;

[0122] S330, obtaining signal strength change data and data volume change values ​​of water conservancy monitoring data transmitted in each target monitoring area within a preset time period;

[0123] S340, determining a communication stability threshold of each target monitoring area based on the signal strength change data and the signal data volume change data within a preset time period;

[0124] S350, obtaining communication parameters of each target monitoring area;

[0125] S360: Input the communication parameters into the pre-trained model to obtain the current communication stability value of each target monitoring area;

[0126] S370: Compare the communication stability value corresponding to each communication scenario with the communication stability threshold, and mark the number of communication scenarios whose communication stability values ​​are greater than the communication stability threshold;

[0127] S380: If the number is greater than a preset threshold, determine that the Beidou communication channel is the best communication channel;

[0128] S390: If the number is less than the preset threshold, determine that the Beidou communication channel is not the optimal communication channel.

[0129] Determine whether the Beidou communication channel is the best communication channel. Specifically, first, determine the communication scenario of each water conservancy monitoring area through geographic location information. In this embodiment, the communication scenario can be the foot of a remote mountain area, a river with wide water area, etc. Determine the communication environment of each water conservancy monitoring area through a geographic information system. According to the geographic location information, the influence of topography on signal propagation, the influence of climate characteristics on communication stability, and whether there are other interference factors such as electromagnetic interference, building shielding, etc. can be determined.

[0130] After determining the communication scenario of each water conservancy monitoring area, the water conservancy monitoring area is divided into multiple target monitoring areas through the communication scenario. In this embodiment, the target monitoring area is a monitoring area divided according to the communication scenario, that is, each water conservancy monitoring area is divided into a target monitoring area according to the communication scenario obtained previously. For example, the communication scenario of part of the water conservancy monitoring area is a river with wide water area, and there is no mountain around the water conservancy monitoring area to affect the communication environment. These communication scenarios are divided into the same type of target monitoring area.

[0131] After determining the target monitoring area, the signal strength change data and data volume change value of each target monitoring area in a preset time period are obtained. The telemetry terminal device is a microprocessor controlled device that integrates data acquisition, transmission, and storage functions. It can collect data from various sensors and devices and transmit the data to the control center or data center for analysis and processing. The preset time period can be determined according to the actual situation. Through the signal strength change data and data volume change value of each target monitoring area in a preset time period, the communication stability threshold of each target monitoring area can be determined.

[0132] Subsequently, a communication stability threshold is determined for each target monitoring area. In this embodiment, the communication stability threshold refers to the critical value or limit of the communication system's ability to maintain stable communication under specific conditions, measured by signal strength and signal transmission rate. Specifically, first, a preset recognition algorithm is used to identify data in the signal strength change data for each target monitoring area with a signal-to-noise ratio less than a preset noise ratio threshold and data with a signal-to-noise ratio greater than a preset noise ratio threshold, as well as data in the signal data volume change data for each target monitoring area with a signal packet loss rate less than a preset packet loss rate threshold and data with a signal packet loss rate greater than a preset packet loss rate threshold. In this embodiment, the preset packet loss rate threshold and the preset noise ratio threshold can be determined based on actual conditions. The preset recognition algorithm can be a machine learning algorithm, such as a support vector machine or decision tree, which learns patterns and features in the data through training and determines whether the data exceeds the preset threshold. Subsequently, data with a signal-to-noise ratio less than the preset threshold and data with a signal packet loss rate less than the preset threshold are marked as true stability cases, and data with a signal-to-noise ratio greater than the preset threshold and data with a signal packet loss rate greater than the preset threshold are marked as false stability cases. The true stable examples and false stable examples are then input into the trained binary classification algorithm, which outputs true positive examples, false positive examples, true negative examples, and false negative examples. In this embodiment, true stable examples refer to data with a signal-to-noise ratio less than a preset threshold and a signal packet loss rate less than a preset threshold, while false stable examples refer to data with a signal-to-noise ratio greater than a preset threshold and a signal packet loss rate greater than a preset threshold. True positive examples are data points that are predicted to be positive and are actually positive; false positive examples are data points that are predicted to be positive but are actually negative; false negative examples are data points that are predicted to be negative but are actually positive; and true negative examples are data points that are predicted to be negative and are actually negative.

[0133] After obtaining true positive examples and false positive examples, true negative examples and false negative examples, the true stability rate is calculated through the true positive examples and false negative examples, and the true stability rate can be calculated through the preset true stability rate; the false stability rate is calculated through the false positive examples and true negative examples, and the false stability rate can be calculated through the preset false stability rate formula; and the false stability rate is used as the horizontal axis and the true stability rate as the vertical axis to draw the communication stability threshold curve of each target monitoring area. Subsequently, the threshold boundary point of the communication stability threshold curve of each target monitoring area is determined by the preset optimal threshold discrimination formula. The preset optimal threshold can use the statistical method of the maximum inter-class variance method to calculate the stability index of the data point. This method obtains the threshold boundary point of the communication stability threshold curve by finding a threshold so that the inter-class variance between the stable channel and the unstable channel is maximized, thereby obtaining the communication stability threshold of each target monitoring area.

[0134] After determining the communication stability threshold of each target monitoring area, the communication parameters of each target monitoring area are obtained. Through network monitoring, network monitoring equipment or software can be deployed to obtain data such as signal strength, bit error rate, signal-to-noise ratio, jitter, delay, and packet loss rate.

[0135] After obtaining the communication parameters, they are input into a pre-trained model to determine the current communication stability value for each target monitoring area. A pre-trained model is a trained model that predicts communication stability based on the communication parameters. It can be a machine learning or deep learning model, such as a regression model or neural network. The communication parameters for each target monitoring area are input into the regression model, and the output is the current communication stability value for each target monitoring area. The communication stability value is a metric used to quantify the ability of a communication network or device to maintain stable communication under specific conditions.

[0136] Subsequently, the communication stability value corresponding to each communication scenario is compared with the communication stability threshold, and the number of communication scenarios whose communication stability values ​​are greater than the communication stability threshold is marked. That is, the communication stability value corresponding to each communication scenario is compared with the communication stability threshold. For example, the communication scenario is a river with a vast water area, and the corresponding communication stability value is 0.7, and the communication stability threshold is 0.6. This communication scenario is marked. In the same way, the communication stability value corresponding to each communication scenario is compared with the communication stability threshold, and the number of communication scenarios greater than the communication stability threshold is counted. If the number is greater than the preset threshold, the Beidou communication channel is determined to be the optimal communication channel. If the number is less than the preset threshold, it is determined that the Beidou communication channel is not the optimal communication channel. The preset threshold can be determined according to actual conditions.

[0137] By determining whether the Beidou communication channel is the optimal communication channel, we can accurately assess whether the Beidou communication channel is the optimal communication channel in the water conservancy monitoring area, which helps to improve monitoring efficiency and accuracy and ensure the reliability and integrity of water conservancy monitoring data.

[0138] In one implementation of this embodiment, determining the communication stability threshold of each target monitoring area by using signal strength change data and signal data volume change data within a preset time period includes:

[0139] S401, using a preset recognition algorithm, identifying data with a signal-to-noise ratio less than a preset noise ratio threshold and data with a signal-to-noise ratio greater than a preset noise ratio threshold in the signal strength change data of each target monitoring area;

[0140] S402: using a preset recognition algorithm, identifying data with a signal packet loss rate less than a preset packet loss rate threshold and data with a signal packet loss rate greater than a preset packet loss rate threshold in the signal data volume change data of each target monitoring area;

[0141] S403, marking the data with a signal-to-noise ratio less than a preset noise ratio threshold and the data with a signal packet loss rate less than a preset packet loss rate threshold as true stable cases;

[0142] S404, marking the data with a signal-to-noise ratio greater than a preset noise ratio threshold and the data with a signal packet loss rate greater than a preset packet loss rate threshold as false stable cases;

[0143] S405, inputting the true stable cases into the trained binary classification algorithm to output true cases and false positive cases;

[0144] S406, inputting the false stable cases into the binary classification algorithm to output true negative cases and false negative cases;

[0145] S407, calculating a true stable rate through the true cases and the false negative cases;

[0146] S408, calculating a false stable rate through the false positive cases and the true negative cases;

[0147] S409, drawing a communication stability threshold curve of each target monitoring area by taking the false stable rate as the horizontal axis and the true stable rate as the vertical axis;

[0148] S410, determining a threshold boundary point of the communication stability threshold curve of each target monitoring area through a preset optimal threshold determination formula;

[0149] S411, determining the communication stability threshold of each target monitoring area through the threshold boundary point.

[0150] Firstly, the data with a signal-to-noise ratio less than a preset noise ratio threshold and the data with a signal-to-noise ratio greater than a preset noise ratio threshold in the signal strength change data of each target monitoring area are identified by using a preset identification algorithm. The signal-to-noise ratio is the ratio of signal power and noise power. In this embodiment, the preset identification algorithm can be a machine learning algorithm, such as a support vector machine, a decision tree, etc. The mode and features in the data are learned through training to determine whether the data exceeds the preset threshold. That is, the signal strength change data of each target monitoring area can be processed by using a support vector machine algorithm to determine the signal-to-noise ratio of each data point. The obtained signal-to-noise ratio is compared with the preset noise ratio threshold to identify the data with a signal-to-noise ratio less than a preset noise ratio threshold and the data with a signal-to-noise ratio greater than a preset noise ratio threshold in the signal strength change data.

[0151] The preset identification algorithm is used to identify data with a signal packet loss rate less than the preset packet loss rate threshold and data with a signal packet loss rate greater than the preset packet loss rate threshold in the signal data volume change data of each target monitoring area. The signal packet loss rate refers to the proportion of lost data packets in the data transmission process. In this embodiment, the preset identification algorithm can be a machine learning algorithm, such as a support vector machine or a decision tree, which learns the patterns and features in the data through training to determine whether the data exceeds the preset threshold. That is, the support vector machine algorithm is used to process the signal packet loss rate data of each target monitoring area, determine the signal packet loss rate of each data point, compare the obtained signal packet loss rate ratio with the preset packet loss rate threshold, and identify the data with a signal packet loss rate less than the preset packet loss rate threshold and the data with a signal packet loss rate greater than the preset packet loss rate threshold in the signal data volume change data of each target monitoring area, respectively. The preset identification algorithm can be the same algorithm or different algorithms.

[0152] Secondly, data with a signal-to-noise ratio less than the preset threshold and a signal packet loss rate less than the preset packet loss rate threshold are marked as true stability examples. In this embodiment, the true stability example refers to data with a signal-to-noise ratio less than the preset signal-to-noise threshold and a signal packet loss rate less than the preset signal packet loss threshold. That is, by traversing all signal data, comparing the signal-to-noise ratio of each data with the preset noise ratio threshold, and if the signal-to-noise ratio is less than the preset noise ratio threshold and the signal packet loss rate is less than the preset threshold, the data is marked as a true stability example. Similarly, data with a signal-to-noise ratio greater than the preset noise ratio threshold and a signal packet loss rate greater than the preset packet loss rate threshold are marked as false stability examples. In this embodiment, the false stability example refers to data with a signal-to-noise ratio greater than the preset threshold and a signal packet loss rate greater than the preset threshold. That is, by traversing all signal data, comparing the signal-to-noise ratio of each data with the preset noise ratio threshold, and if the signal-to-noise ratio is greater than the preset noise ratio threshold and the signal packet loss rate is greater than the preset threshold, the data is marked as a false stability example.

[0153] After obtaining the true positive examples and the false positive examples, the true positive examples are input into the trained binary classification algorithm, and the true positive examples and the false positive examples are output, and the false positive examples are input into the binary classification algorithm, and the true negative examples and the false negative examples are output. The trained binary classification algorithm can be a method based on traditional machine learning, such as a logistic regression, a support vector machine, a decision tree, a random forest algorithm, and the like. In this embodiment, the true positive examples refer to data points that are predicted to be positive samples and are actually positive samples, that is, the samples output by inputting the true positive examples into the trained binary classification algorithm are also data that are less than the preset noise ratio threshold and the preset packet loss rate threshold. The false positive examples refer to data points that are predicted to be positive samples but are actually negative samples, that is, the samples output by inputting the true positive examples into the trained binary classification algorithm are data that are greater than the preset noise ratio threshold and the preset packet loss rate threshold. The false negative examples refer to data points that are predicted to be negative samples but are actually positive samples, that is, the samples output by inputting the false positive examples into the trained binary classification algorithm are data that are less than the preset noise ratio threshold and the preset packet loss rate threshold. The true negative examples refer to data points that are predicted to be negative samples and are actually negative samples, that is, the samples output by inputting the false positive examples into the trained binary classification algorithm are data that are greater than the preset noise ratio threshold and the preset packet loss rate threshold.

[0154] After obtaining the true positive examples, the false positive examples, the true negative examples, and the false negative examples, the true stability rate is calculated through the true positive examples and the false negative examples, and the false stability rate is calculated through the false positive examples and the true negative examples. The true stability rate can be calculated through a preset true stability rate formula, and the preset true stability rate formula is as follows:

[0155]

[0156] The false stability rate can be calculated through a preset false stability rate formula, and the preset false stability rate formula is as follows:

[0157]

[0158] After obtaining the true stability rate and the false stability rate, the false stability rate is taken as the horizontal axis, and the true stability rate is taken as the vertical axis to draw a communication stability threshold curve of each target monitoring area, that is, the true stability rate and the false stability rate in a preset time period are drawn to obtain the communication stability threshold curve. The values of the true stability rate and the false stability rate per minute in the preset time period are drawn in a manner that the false stability rate is taken as the horizontal axis and the true stability rate is taken as the vertical axis. In this embodiment, the communication stability threshold curve is a curve of a threshold for evaluating the stability of a communication system.

[0159] Subsequently, the threshold boundary point of the communication stability threshold curve of each target monitoring area is determined by a preset optimal threshold determination formula. The preset optimal threshold can be calculated by using a statistical method of the maximum inter-class variance method to obtain the stability index of the data points. The threshold boundary point of the communication stability threshold curve is obtained by finding a threshold that maximizes the inter-class variance between the stable channels and the unstable channels. After obtaining the threshold boundary point, the threshold boundary point is taken as the communication stability threshold of each target monitoring area, thereby obtaining the communication stability threshold of each target monitoring area.

[0160] By determining the communication stability threshold of each target monitoring area, the communication quality can be improved, the resource allocation can be optimized, the user experience can be improved, and a more stable, efficient and intelligent communication network can be constructed.

[0161] In one of the embodiments of the present embodiment, according to the node information, the communication abnormal nodes in the water conservancy monitoring ad hoc network are screened to obtain the communication coverage blind area nodes in the water conservancy monitoring ad hoc network, comprising:

[0162] S510, in the node information, the total number of data sent by each node and the number of successful data sending are obtained;

[0163] S520, the communication success rate of each node is calculated by the total number of data sent and the number of successful data sending;

[0164] S530, the correlation coefficient between any two nodes is calculated by the communication success rate;

[0165] S540, the correlation coefficient is subjected to cluster analysis to obtain the nodes with abnormal correlation coefficients;

[0166] S550, the target monitoring area where the nodes with abnormal correlation coefficients are located is determined to obtain the abnormal target detection area;

[0167] S560, all node information in the abnormal target detection area is obtained;

[0168] S570, the topological structure of the abnormal target detection area is obtained, and the topological structure features are extracted;

[0169] S580, the topological structure features and all node information in the abnormal target detection area are input into the trained communication connection effect level model to output the target connection effect of the abnormal target detection area;

[0170] S590, the target connection effect is compared with the preset standard connection effect. If the target connection effect is less than the pre-calculated standard connection effect, the communication coverage blind area nodes in the water conservancy monitoring ad hoc network are obtained.

[0171] Figure 2A flow chart for determining a candidate relay node is provided for the embodiments of the present application, as shown in Figure 2 For the process of determining a candidate relay node, first, all node information is exchanged arbitrarily, the node information is preliminarily matched to obtain a candidate relay node; subsequently, the hash value of the candidate relay node and the communication coverage blind area node is calculated, and it is judged whether the hash value is less than a preset threshold value, when the hash value is less than the preset threshold value, the communication coverage blind area node sends a connection request to the candidate relay node; next, the information of the candidate relay node and the information of the communication coverage blind area node are matched again, it is judged whether the matching degree of the re-matching is greater than a preset matching degree threshold value, when the matching degree of the re-matching is greater than the preset matching degree threshold value, the candidate relay node replies to the connection request of the communication coverage blind area node, and connects with the communication coverage blind area node to obtain a target water conservancy monitoring ad hoc network; when the matching degree of the re-matching is less than the preset matching degree threshold value, the step of iteratively screening the candidate relay node is executed.

[0172] According to the node information, the communication abnormal node in the water conservancy monitoring ad hoc network is screened to obtain the communication coverage blind area node in the water conservancy monitoring ad hoc network. Specifically, first, in the node information, the total number of data sent by each node and the number of successful data sending are obtained, that is, the total number of data sent by each node is counted, which can be obtained by recording the node communication activity system or database, and at the same time, the number of successful data sending of each node can also be counted by recording the node communication activity system or database. Subsequently, the communication success rate of each node is calculated by the total number of data sent and the number of successful data sending, that is, the communication success rate of each node is obtained by dividing the number of successful data sending by the total number of data sent.

[0173] After obtaining the communication success rate of each node, the correlation coefficient between any two nodes is calculated. By calculating the correlation coefficient between any two nodes, that is, when the communication success rate of one node increases, the communication success rate of the other node also increases, it can be judged whether the communication success rate between any two nodes has a certain correlation change trend. If the correlation between the communication success rates of any two nodes is small, it indicates that the communication stability between the two nodes exists an abnormal situation, that is, when the communication success rate of one node increases, the communication success rate of the other node decreases. The correlation coefficient between any two nodes can be calculated by the Pearson correlation coefficient formula, and the Pearson correlation coefficient formula is as follows:

[0174]

[0175] Wherein, r is the Pearson correlation coefficient, n represents the sample quantity; x i and y i represent the i-th observation value of variables X and Y; and respectively represent the sample mean of X and Y;

[0176] After obtaining the correlation coefficient between any two nodes, the correlation coefficient is subjected to cluster analysis to obtain the node with abnormal correlation coefficient. The cluster analysis can use K-means clustering, which is a widely used unsupervised learning algorithm for dividing data points into K clusters. Through iterative means, the sum of squared distances of each data point to its cluster center is minimized to find K clusters in the data set. That is, the cluster analysis of the correlation coefficient between any two nodes can identify the node with smaller correlation coefficient between any two nodes, indicating that there is an abnormal communication situation between the two nodes. Therefore, the node with abnormal correlation coefficient is obtained.

[0177] After determining the node with abnormal correlation coefficient, the target monitoring area where the node with abnormal correlation coefficient is located is determined. In this embodiment, the number of target monitoring areas is at least one. In this embodiment, the target monitoring area is a monitoring area divided according to the communication scenario. Each water conservancy monitoring area is divided into a target monitoring area according to the communication scenario of each water conservancy monitoring area obtained before. That is, the area of the communication scenario where the node with abnormal correlation coefficient is located is determined, and this area is taken as the abnormal target detection area.

[0178] After determining the abnormal target detection area, all node information in the abnormal target detection area is obtained. That is, all node-related data is collected from the monitoring system of the abnormal target detection area, including the identifier, attributes, state data of the node, such as position, type, function, state information, state code, alarm information, and other information.

[0179] Subsequently, the topology structure of the abnormal target detection area is obtained, and the topology structure features are extracted. That is, through the supervision system of the abnormal target detection area, the topology structure of the abnormal target detection area is obtained, and the topology structure features are extracted, including basic features and connectivity features. The basic feature extraction includes node number, edge number, and average degree. The connectivity feature includes connected components, diameter, and average path length.

[0180] After obtaining the topological structure feature, the topological structure feature and all node information in the abnormal target detection area are input into the trained communication connection effect level model, and the target connection effect of the abnormal target detection area is output. In this embodiment, the trained communication connection effect level model can be a deep learning model, that is, the topological structure of the abnormal target detection area in which the node with the abnormal correlation coefficient exists, and all node information in the abnormal target detection area, such as the identifier, attribute, state data, such as position, type, function, state information, state code, alarm information and the like, are input into the deep learning model, so that the target connection effect of the abnormal target detection area can be obtained. In this embodiment, the target connection effect refers to the effect of the stability of communication between nodes. The model will predict the target connection effect level of the abnormal target detection area according to the input data, such as outputting “excellent”, “good”, “general”, “poor” and “very poor” target connection effects.

[0181] Then, the target connection effect is compared with the preset standard connection effect. If the target connection effect is less than the pre-calculated standard connection effect, the communication coverage blind area node in the water conservancy monitoring ad hoc network is obtained. Specifically, according to the specific business requirements of water conservancy monitoring, a threshold of the standard connection effect can be set. If the target connection effect of a node is lower than the preset threshold of the standard connection effect, and the duration exceeds a certain threshold, such as continuous hours or days, the node is marked as a communication coverage blind area node.

[0182] By screening the communication abnormal nodes in the water conservancy monitoring ad hoc network, the communication coverage blind area node in the water conservancy monitoring ad hoc network is obtained. The accurate identification of the communication coverage blind area node in the water conservancy monitoring ad hoc network not only improves the reliability and stability of the network, but also provides strong support for subsequent network optimization and resource allocation.

[0183] In one of the embodiments of the present embodiment, the relay node of the water conservancy monitoring ad hoc network is obtained, the relay node is connected with the communication coverage blind area node, and the target water conservancy monitoring ad hoc network is obtained, comprising:

[0184] S610, all node information is exchanged in all nodes of the water conservancy monitoring ad hoc network through a preset communication protocol;

[0185] S620, the node information is preliminarily matched by receiving the arbitrary node information, and the candidate relay node is obtained;

[0186] S630, the hash value of the candidate relay node and the communication coverage blind area node is calculated by a preset hash value calculation algorithm, wherein in the case that the hash value is less than a preset threshold, the communication coverage blind area node is used to send a connection request to the candidate relay node;

[0187] S640: In response to the connection request, rematch the information of the candidate relay node with the information of the node in the communication coverage blind area;

[0188] S650, wherein, when the matching degree of the second match is greater than the preset matching degree threshold, the candidate relay node responds to the connection request of the communication coverage blind spot node and connects with the communication coverage blind spot node to obtain the target water conservancy monitoring self-organizing network. When the matching degree is less than the preset matching degree threshold, the candidate relay node does not respond to the connection request of the communication coverage blind spot node, and the communication coverage blind spot node will repeat the steps of performing preliminary matching on the node information through any node information received to obtain the candidate relay node.

[0189] Obtain the relay node of the water conservancy monitoring self-organizing network, connect the relay node with the communication coverage blind spot node, and obtain the target water conservancy monitoring self-organizing network. Specifically, first, all node information is arbitrarily exchanged among all nodes of the water conservancy monitoring self-organizing network through a preset communication protocol. The preset communication protocol can be TCP / IP, UDP, Zigbee, LoRa and other communication protocols. The information that each node needs to collect and prepare to exchange, including monitoring data, node status, location information, etc., is encapsulated and packaged according to the preset format, and the encapsulated information is sent to other nodes in the water conservancy monitoring self-organizing network in a broadcast manner through the preset communication protocol.

[0190] By performing a preliminary match on the node information received from any node, candidate relay nodes are obtained. That is, when other nodes receive the broadcast information, they parse and process the received information and perform a preliminary match on the node information based on the type and content of the information. The matching conditions can be set according to the network requirements and actual application scenarios to screen the candidate relay nodes. Matching can be based on conditions such as the link quality, geographical location, and remaining energy between nodes. When the preliminary match of the node information is successful, a candidate relay node is obtained. In this embodiment, a candidate relay node refers to a candidate object in the communication network that is considered likely to be selected as a relay node after preliminary screening and evaluation. Relay nodes play an important role in the network. They can forward, amplify, or regenerate signals, thereby expanding the coverage of the network and improving the transmission quality and reliability of the signal.

[0191] After the candidate relay node is obtained, a preset hash value calculation algorithm is used to calculate the hash value of the candidate relay node and the communication coverage blind area node. The preset hash value calculation algorithm can be MD5 algorithm. The MD5 algorithm takes any length of data as input and outputs a 128-bit hash value. Specifically, a node can store specific information in a distributed hash table and mark itself as a candidate relay node. The specific information can be the geographic location, communication capability and other information of the node. These information can be used as the key in the distributed hash table. The node can broadcast a message to inform other nodes of its existence and willingness to be a candidate relay node. Other nodes can use the query mechanism of the distributed hash table to find and discover these candidate relay nodes. After the candidate relay node is determined, the MD5 algorithm is used to calculate the hash value of the candidate relay node and the communication coverage blind area node.

[0192] When the hash value is less than the preset threshold, the communication coverage blind area node is configured to send a connection request to the candidate relay node. The preset threshold should be set according to the specific situation of the network, such as node distribution, communication demand, etc. That is, the smaller the hash value between nodes, the more matching the information between nodes. When the hash value is less than the preset threshold, it means that the two nodes can be connected to each other. Therefore, the communication coverage blind area node is configured to send a connection request to the candidate relay node.

[0193] In response to the connection request, the information of the candidate relay node is matched with the information of the communication coverage blind area node again. Specifically, when the candidate relay node receives the connection request from the communication coverage blind area node, the candidate relay node parses the connection request and evaluates its own information, including geographic location, communication capability, remaining bandwidth, etc., to determine whether it meets the communication demand of the communication coverage blind area node. When the matching degree of the second matching is greater than the preset matching degree threshold, that is, the candidate relay node evaluates its own information to meet the demand of the communication coverage blind area node, the candidate relay node will reply to the connection request of the communication coverage blind area node and connect with the communication coverage blind area node to obtain the target water conservancy monitoring ad hoc network.

[0194] When the candidate relay node evaluates its own information to meet the demand of the communication coverage blind area node, the candidate relay node does not reply to the connection request of the communication coverage blind area node. The communication coverage blind area node will perform the step of performing preliminary matching on the received node information to obtain the candidate relay node. The process of finding the candidate relay node for secondary matching again until the communication coverage blind area node and the candidate relay node are successfully matched.

[0195] By acquiring the relay node of the water conservancy monitoring ad hoc network, connecting the relay node and the communication coverage blind area node, and obtaining the target water conservancy monitoring ad hoc network, the coverage and reliability of the network can be significantly improved, the node selection and connection efficiency can be optimized, the network performance and scalability can be improved, and the operation and maintenance cost and maintenance difficulty can be reduced.

[0196] In one of the implementation manners of the embodiment, after the candidate relay node replies to the connection request of the communication coverage blind area node and connects with the communication coverage blind area node to obtain the target water conservancy monitoring ad hoc network, the method further includes:

[0197] S710, acquiring the first transmission performance of the water conservancy monitoring ad hoc network under the direct connection condition and the first node transmission performance between each AI control device;

[0198] S720, acquiring the second transmission performance of the target water conservancy monitoring ad hoc network and the second node transmission performance between each AI control device and the relay node;

[0199] S730, comparing the first transmission performance with the second transmission performance, and comparing the first node transmission performance with the second node transmission performance;

[0200] S740, if the first transmission performance is less than or equal to the second transmission performance, and the first node transmission performance is less than or equal to the second node transmission performance, then obtaining the target water conservancy monitoring ad hoc network after the communication coverage blind area node is connected with the candidate relay node.

[0201] The first transmission performance of the water conservancy monitoring ad hoc network under the direct connection condition and the first node transmission performance between each AI control device are acquired. In the embodiment, the first transmission performance refers to the transmission performance of the water conservancy monitoring ad hoc network, and the first node transmission performance refers to the transmission performance between each AI control device. The first transmission performance of the water conservancy monitoring ad hoc network under the direct connection condition and the first node transmission performance between each AI control device can be acquired by professional test tools and software, such as network performance testers, throughput testers, etc. The data transmission rate, delay, packet loss rate and other key performance indicators between two nodes under the direct connection condition can be directly measured.

[0202] Subsequently, the second transmission performance of the target water conservancy monitoring ad hoc network and the second node transmission performance between each AI control device and the relay node are acquired, and the second transmission performance in this embodiment refers to the transmission performance of the target water conservancy monitoring ad hoc network, that is, the transmission performance of the water conservancy monitoring ad hoc network with the relay node, and the second node transmission performance refers to the transmission performance between each AI control device and the relay node. The second transmission performance of the target water conservancy monitoring ad hoc network and the second node transmission performance between each AI control device and the relay node can be acquired by professional test tools and software, such as a network performance tester, a throughput tester, and the like, and the data transmission rate, delay, packet loss rate, and the like between two nodes under direct connection conditions can be directly measured.

[0203] The first transmission performance and the second transmission performance are compared, and the first node transmission performance and the second node transmission performance are compared, so that the transmission speed, transmission stability, and transmission efficiency in the first transmission performance and the second transmission performance can be compared; similarly, the transmission speed, transmission stability, and transmission efficiency in the first node transmission performance and the second node transmission performance are compared; the transmission stability can be manifested by whether there is packet loss, delay, and the like in the data transmission process, and the transmission efficiency can be manifested by the amount of data successfully transmitted per unit time.

[0204] When the first transmission performance is less than or equal to the second transmission performance, and the first node transmission performance is less than or equal to the second node transmission performance, the target water conservancy monitoring ad hoc network after the communication coverage blind area node is connected with the candidate relay node is obtained, that is, when the transmission speed, transmission stability, and transmission efficiency of the water conservancy monitoring ad hoc network are less than the transmission speed, transmission stability, and transmission efficiency of the target water conservancy monitoring ad hoc network, and the transmission speed, transmission stability, and transmission efficiency between each AI control device are less than the transmission speed, transmission stability, and transmission efficiency between each AI control device and the relay node, it is indicated that the overall communication performance of the current target water conservancy monitoring ad hoc network and each AI control device and the relay node is not affected, and stable communication can be performed, and therefore, the target water conservancy monitoring ad hoc network after the communication coverage blind area node is connected with the candidate relay node is obtained.

[0205] By evaluating the communication performance of the target water conservancy monitoring ad hoc network after the communication coverage blind area node is connected with the candidate relay node, the effectiveness of the relay node can be verified, and the reliability and stability of the network can be improved, and convenience and efficiency are provided for subsequent maintenance and optimization work.

[0206] In one of the embodiments of the present embodiment, the method further comprises:

[0207] S810, real-time acquisition of climate monitoring data of each water conservancy monitoring area;

[0208] S820, inputting climate monitoring data into a prediction model to obtain climate prediction information for each water conservancy monitoring area;

[0209] S830, through AI control device, collects water monitoring data of each water conservancy monitoring area in real time;

[0210] S840, obtaining water regime change data for each water conservancy monitoring area within future trends through water regime monitoring data and climate forecast information;

[0211] S850. Generate a water control plan for each water monitoring area based on the water situation change data of each water monitoring area within the future trend.

[0212] Real-time climate monitoring data for each water conservancy monitoring area can be obtained from weather station data, such as rainfall. Once this data is obtained, it is input into a prediction model to obtain climate forecast information for each water conservancy monitoring area. The prediction model can be a neural network model. By inputting key climate parameters such as temperature, precipitation, wind speed, wind direction, and humidity into the neural network model, climate forecast information for each water conservancy monitoring area for a future time period can be obtained, such as whether it will rain in the water conservancy monitoring area within the future time period. The neural network model is a pre-trained model.

[0213] Through the AI ​​control device, the water situation monitoring data of each water conservancy monitoring area is collected in real time, that is, the water level and flow data of each water conservancy monitoring area are collected in real time through the AI ​​control device; through the water situation monitoring data and climate forecast information, the water situation change data of each water conservancy monitoring area in the future trend is obtained, that is, through the rainfall in each water conservancy monitoring area in the future time period, as well as the water level and flow data of each water conservancy monitoring area, the water situation change data of each water conservancy monitoring area in the future trend can be obtained. For example, based on the rainfall, water level and flow data, it is judged whether the water level of each water conservancy monitoring area will exceed the standard and a flood warning will occur.

[0214] By using the water situation change data of each water conservancy monitoring area in the future trend, a water conservancy regulation plan for each water conservancy monitoring area is generated. That is to say, the water situation of each water conservancy monitoring area is regulated according to the water situation change data of each water conservancy monitoring area. For example, floods may occur in the upstream water conservancy monitoring area, and flood warnings need to be issued for the downstream water conservancy monitoring area. Based on the water level data of the downstream water conservancy monitoring area, it is judged whether to open the gates to discharge floodwaters, as well as the water level conditions of each downstream water conservancy monitoring area, to determine each downstream water conservancy monitoring area where the gates can be opened to discharge floodwaters.

[0215] By generating the water conservancy regulation scheme of each water conservancy monitoring area according to the water regime change data of each water conservancy monitoring area within future trends, the real-time and accuracy of water conservancy monitoring can be improved, the climate prediction capability can be enhanced, the water conservancy regulation scheme can be optimized, the water conservancy safety can be ensured, and the sustainable utilization of water resources and the healthy development of the water conservancy cause can be achieved.

[0216] The application also provides a machine readable storage medium, which stores instructions for causing a machine to execute the Beidou double-network channel control method based on an AI control device.

[0217] The application further provides a Beidou double-network channel control system based on an AI control device, comprising:

[0218] a memory configured to store instructions; and

[0219] a processor configured to call the instructions from the memory and capable of implementing the Beidou double-network channel control method based on an AI control device when the instructions are executed.

[0220] In the embodiment, the electronic device can be a tablet computer, a desktop computer, a laptop computer, a handheld computer, a wearable device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, or the like device with a processor. Of course, the electronic device can also be a server. The specific form of the electronic device is not specially limited in the embodiment.

[0221] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system or a computer program product. Therefore, the application can be in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0222] The application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), computer program products according to the embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a machine that implements the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocks Figure 1A device that provides the functions specified in a block or multiple blocks.

[0223] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0224] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0225] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0226] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0227] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0228] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0229] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included within the scope of the claims of the present application.

Claims

1. A BeiDou dual-network channel control method based on an AI control device, characterized in that: Applied to an AI control device, where there are at least two AI control devices, and the AI ​​control devices are arranged in a water conservancy monitoring area, and the water conservancy monitoring area is connected to a central control device, the method includes: In response to receiving a transmission signal sent by the central control device, determining whether the Beidou communication channel is an optimal communication channel, wherein the transmission signal is used to transmit water conservancy monitoring data; When the Beidou communication channel is not an optimal communication channel, obtaining positions of all the AI ​​control devices in each of the water conservancy monitoring areas; Building a water conservancy monitoring ad hoc network based on the locations of all the AI ​​control devices in each water conservancy monitoring area, wherein each of the AI ​​control devices is a node of the water conservancy monitoring ad hoc network; Obtaining node information of the water conservancy monitoring ad hoc network; Filtering communication abnormal nodes in the water conservancy monitoring ad hoc network according to the node information to obtain communication coverage blind area nodes in the water conservancy monitoring ad hoc network; Obtaining the node with the strongest communication signal in the water conservancy monitoring ad hoc network; Networking the node in the communication blind area with the node with the strongest communication signal, and determining whether the node in the communication blind area and the node with the strongest communication signal meet direct connection conditions; If the node in the communication blind area meets the direct connection condition with the node with the strongest communication signal, the node in the communication blind area is directly connected to the node with the strongest communication signal to obtain the target water conservancy monitoring ad hoc network, wherein the direct connection condition is that the communication protocol of the node in the communication blind area is the same as that of the node with the strongest communication signal; If the communication coverage blind area node and the node with the strongest communication signal do not meet the direct connection condition, obtain the relay node of the water conservancy monitoring ad hoc network; The relay node is connected to the communication coverage blind area node to obtain a target water conservancy monitoring ad hoc network.

2. The method according to claim 1, characterized in that The method further comprises: When the Beidou communication channel is the optimal communication channel, converting the water conservancy monitoring data into a water conservancy monitoring digital signal; Transmitting the water conservancy monitoring digital signal to the Beidou satellite using the AI ​​control device; Acquire a target area covered by the BeiDou satellite signal, and determine the central control device in the target area; Transmitting the water conservancy monitoring digital signal to the central control device; The water conservancy monitoring digital signal transmitted by the Beidou satellite is decoded by the central control device to obtain readable water conservancy monitoring data.

3. The method according to claim 1, characterized in that Determining whether the Beidou communication channel is the best communication channel includes: Determining a communication scenario for each of the water conservancy monitoring areas based on geographic location information; By using the communication scenario, the water conservancy monitoring area is divided into a plurality of target monitoring areas; Obtaining signal strength change data and data volume change values ​​of the water conservancy monitoring data transmitted by each target monitoring area within a preset time period; Determining a communication stability threshold of each target monitoring area by using the signal strength change data and the signal data volume change data within a preset time period; Acquiring communication parameters of each target monitoring area; Inputting the communication parameters into a pre-trained model to obtain a current communication stability value of each target monitoring area; Comparing the communication stability value corresponding to each communication scenario with the communication stability threshold, and marking the number of the communication scenarios in which the communication stability value is greater than the communication stability threshold; If the number is greater than a preset threshold, determining that the Beidou communication channel is the optimal communication channel; If the number is less than a preset threshold, it is determined that the Beidou communication channel is not the optimal communication channel.

4. The method according to claim 3, characterized in that The determining of the communication stability threshold of each target monitoring area by using the signal strength change data and the signal data volume change data within a preset time period includes: Using a preset recognition algorithm, identifying data with a signal-to-noise ratio less than a preset noise ratio threshold and data with a signal-to-noise ratio greater than a preset noise ratio threshold in the signal strength change data of each target monitoring area; Using a preset recognition algorithm, identifying data with a signal packet loss rate less than a preset packet loss rate threshold and data with a signal packet loss rate greater than a preset packet loss rate threshold in the signal data volume change data of each target monitoring area; Marking the data whose signal-to-noise ratio is less than a preset noise ratio threshold and the data whose signal packet loss rate is less than a preset packet loss rate threshold as true stable cases; Marking the data whose signal-to-noise ratio is greater than a preset noise ratio threshold and the data whose signal packet loss rate is greater than a preset packet loss rate threshold as false stable cases; Inputting the true stable examples into the trained binary classification algorithm and outputting true positive examples and false positive examples; Inputting the false stable examples into the binary classification algorithm and outputting true negative examples and false negative examples; Calculating a true stability rate through the true positive examples and the false negative examples; Calculating a false stability rate through the false positive examples and the true negative examples; Using the false stability rate as the horizontal axis and the true stability rate as the vertical axis, a communication stability threshold curve of each target monitoring area is drawn; Determine the threshold point of the communication stability threshold curve of each target monitoring area by a preset optimal threshold judgment formula; The communication stability threshold of each target monitoring area is determined by the threshold boundary point.

5. The method according to claim 3, characterized in that The step of screening, based on the node information, nodes with abnormal communication in the water conservancy monitoring ad hoc network to obtain nodes in communication blind spots in the water conservancy monitoring ad hoc network includes: In the node information, the total number of times each node sends data and the number of times the data is successfully sent are obtained; Calculating the communication success rate of each node according to the total number of times the data is sent and the number of times the data is successfully sent; Calculating the correlation coefficient between any two nodes based on the communication success rate; Performing cluster analysis on the correlation coefficients to obtain nodes with abnormal correlation coefficients; Determine the target monitoring area where the node with the abnormal correlation coefficient is located to obtain the abnormal target detection area; Obtaining all node information within the abnormal target detection area; Acquire the topological structure of the abnormal target detection area and extract the topological structure features; Inputting the topological structure features and all node information in the abnormal target detection area into the trained communication connection effect level model, and outputting the target connection effect of the abnormal target detection area; The target connection effect is compared with a preset standard connection effect. If the target connection effect is less than the pre-calculated standard connection effect, a communication coverage blind spot node in the water conservancy monitoring ad hoc network is obtained.

6. The method according to claim 1, characterized in that The step of obtaining the relay node of the water conservancy monitoring ad hoc network and connecting the relay node to the communication coverage blind area node to obtain the target water conservancy monitoring ad hoc network includes: All the node information is exchanged arbitrarily among all the nodes in the water conservancy monitoring ad hoc network through a preset communication protocol; By performing preliminary matching on any node information received, candidate relay nodes are obtained; Calculating a hash value between the candidate relay node and the node in the communication blind spot using a preset hash value calculation algorithm, wherein if the hash value is less than a preset threshold, the node in the communication blind spot is configured to send a connection request to the candidate relay node; In response to the connection request, rematching the information of the candidate relay node with the information of the node in the communication coverage blind area; Among them, when the matching degree of the re-matching is greater than the preset matching degree threshold, the candidate relay node replies to the connection request of the communication coverage blind spot node and connects with the communication coverage blind spot node to obtain the target water conservancy monitoring self-organizing network. When the matching degree is less than the preset matching degree threshold, the candidate relay node does not reply to the connection request of the communication coverage blind spot node, and the communication coverage blind spot node will repeat the step of performing preliminary matching on the node information received through any node information to obtain the candidate relay node.

7. The method according to claim 6, characterized in that After the candidate relay node replies to the connection request of the communication blind area node and connects with the communication blind area node to obtain the target water conservancy monitoring ad hoc network, the method further includes: Obtaining a first transmission performance of the water conservancy monitoring ad hoc network that meets a direct connection condition and a first node transmission performance between each of the AI ​​control devices; Obtaining a second transmission performance of the target water conservancy monitoring ad hoc network and a second node transmission performance between each of the AI ​​control devices and the relay node; comparing the first transmission performance with the second transmission performance, and comparing the first node transmission performance with the second node transmission performance; If the first transmission performance is less than or equal to the second transmission performance, and the first node transmission performance is less than or equal to the second node transmission performance, the target water conservancy monitoring ad hoc network is obtained after the communication coverage blind spot node is connected to the candidate relay node.

8. The method according to claim 1, characterized in that The method further comprises: Obtain climate monitoring data for each water conservancy monitoring area in real time; Inputting the climate monitoring data into a prediction model to obtain climate prediction information for each of the water conservancy monitoring areas; The AI ​​control device collects water monitoring data of each water conservancy monitoring area in real time; Obtaining water regime change data for each of the water conservancy monitoring areas within a future trend through the water regime monitoring data and the climate forecast information; A water control plan for each water monitoring area is generated based on the water situation change data of each water monitoring area within the future trend.

9. A machine-readable storage medium, characterized in that The machine-readable storage medium stores instructions for enabling a machine to execute the Beidou dual-network channel control method based on an AI control device according to any one of claims 1 to 8.

10. A BeiDou dual-network channel control system based on an AI control device, characterized in that: include: a memory configured to store instructions; as well as The processor is configured to call the instruction from the memory and implement the Beidou dual-network channel control method based on the AI ​​control device according to any one of claims 1 to 8 when executing the instruction.

Images