Self-organizing method, system and device for long-distance cognitive radio network

Abstract

The invention relates to the technical field of communication, and discloses a long-distance cognitive radio network self-organization method, system and device. The method comprises the steps of setting a self-organizing node, constructing a long-distance cognitive wireless network and setting a routing table, obtaining node data and routing data, calculating a routing score and updating the routing table, recombining the long-distance cognitive wireless network and configuring the distributed long-distance cognitive wireless network. The system and the device correspond to the method. According to the invention, the extension of the existing communication network is realized through the configuration of the self-organizing nodes and the corresponding architecture based on the completed long-distance cognitive radio network, the acquisition and analysis of real-time data, the updating of the routing table and the distributed long-distance cognitive radio network, and on the basis of dynamically establishing a new communication connection, the communication efficiency is improved. The performance of poor self-organization and self-repairing performance of an existing communication network is improved, and self-management of the nodes is achieved. And finally, self-organization of the long-distance cognitive wireless network is realized.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, system and device for self-organization of a long-distance cognitive wireless network. Background Art

[0002] Faced with the complex terrain within my country's territory, a large number of power transmission lines need to pass through uninhabited areas during actual laying. The quality of public network signals in these uninhabited areas is poor, and some areas even have no signal coverage, resulting in de facto no-network areas. In the absence of a communication network, emerging technologies such as automation, intelligence, and informatization are difficult to play an effective role. Based on the above background, the following technical problems arise in actual production work:

[0003] (1) Infrastructure projects require temporary networking during construction, and the cost of building a wired private network is high.

[0004] (2) There is no network coverage at high-risk work sites, and it is impossible to quickly build a network to transmit work site data back.

[0005] (3) In remote areas, mountainous areas, underground trenches and other special scenarios where power transmission and transformation equipment is located and there is no public network signal or the signal coverage is weak, on-site networking is limited by factors such as environment, equipment, technology, and cost. Full coverage cannot be achieved using the existing network. At the same time, the terrain, landforms and some nature reserves make networking very difficult, resulting in difficulties in construction work.

[0006] In summary, the prior art lacks a technical solution for long-distance cognitive wireless network self-organization. Summary of the invention

[0007] The purpose of the present application is to provide a long-distance cognitive wireless network self-organization method, system and device to solve the technical problems raised in the above background technology.

[0008] To achieve the above objectives, this application discloses the following technical solutions:

[0009] In a first aspect, the present application discloses a long-distance cognitive wireless network self-organization method, the method comprising:

[0010] S1: Setting up a self-organizing node with cognitive radio function;

[0011] S2: obtaining a long-distance cognitive wireless network based on the self-organizing node, and setting a routing table for controlling a transmission path for the long-distance cognitive wireless network;

[0012] S3: Acquire node data of each of the self-organizing nodes and routing data between the self-organizing nodes;

[0013] S4: Calculate a routing score based on the node data and the routing data, and update the routing table based on the routing score;

[0014] S5: reorganizing the long-range cognitive wireless network based on the updated routing table;

[0015] S6: Configure a distributed long-distance recognition wireless network corresponding to the data fusion center.

[0016] Preferably, the setting of the self-organizing node configured with cognitive radio function specifically comprises the following steps:

[0017] A1: Set up corresponding nodes based on actual communication needs;

[0018] A2: Perform cognitive radio technology configuration on the node and enable the node to have the cognitive radio function to obtain the self-organizing node.

[0019] Preferably, the step of obtaining a long-distance cognitive wireless network based on the self-organizing node and setting a routing table for controlling a transmission path for the long-distance cognitive wireless network specifically comprises the following steps:

[0020] B1: Setting a corresponding wireless network framework based on the actual communication requirements;

[0021] B2: inputting the self-organizing node into the wireless network framework accordingly, and obtaining a corresponding node network data set, wherein the node network data set includes frequency band information, user information and node number information corresponding to the self-organizing node;

[0022] The frequency band information is used to represent specific frequency range data and corresponding authorization information;

[0023] The user information is used to represent the user name and the corresponding level information;

[0024] The node number information is used to represent the identification coding information of the self-organizing node in the wireless network framework;

[0025] B3: defining the wireless network framework after the self-organizing node update based on the node network data set as the long-range cognitive wireless network;

[0026] B4: Setting the transmission path based on the actual demand; the setting of the transmission path includes path setting and level setting, the path setting is used to control the communication to be transmitted based on a preset route, and the level setting is used to control the communication to be transmitted in sequence based on a preset level.

[0027] Preferably, the acquiring of the node data of each of the self-organizing nodes and the routing data between the self-organizing nodes specifically includes:

[0028] The node data includes the node working status and the node network data set;

[0029] The routing data includes the routing working status between nodes, routing quality score, data service rate and bit error rate.

[0030] Preferably, the calculating of the routing score based on the node data and the routing data specifically includes:

[0031] Acquire real-time node-to-node data and real-time routing data, and calculate routing scores using a routing scoring formula, wherein the routing scoring formula is specifically:

[0032]

[0033] Wherein, α is the working state of the node, and when the self-organizing node is powered on, α=1, and when it is not powered on, α=0; β is the working state of the routing, and when the routing is connected, β=1, and when it is not connected, β=0; CQS is the routing quality score, DSR is the data service rate, BER is the bit error rate, UsI is the user information; RS is the routing score;

[0034] The route score is proportional to the communication quality of the route.

[0035] Preferably, the updating of the routing table based on the routing score specifically comprises the following steps:

[0036] C1: Eliminate the self-organizing node corresponding to α=0;

[0037] C2: Eliminate the self-organizing nodes at both ends corresponding to RS=1;

[0038] C3: sorting the routes from large to small based on the route scores, and updating the routing table based on the sorting result.

[0039] Preferably, the reorganization of the long-distance cognitive wireless network based on the updated routing table specifically includes the following steps:

[0040] D1: performing the routing setting based on the actual communication demand and the updated routing table;

[0041] D2: performing the level setting based on the actual communication demand and the result of the route reconstruction;

[0042] D3: Based on the routing setting in step D2 and the level setting in step D3, the reorganized long-distance cognitive wireless network is obtained and self-organized communication is performed.

[0043] Preferably, the configuration and the distributed long-distance recognition wireless network corresponding to the data fusion center specifically include:

[0044] By using the centralized model technology, the data fusion center in the current long-distance recognition wireless network is captured, and the self-organizing node communicates with the data fusion center, which performs fusion processing and then determines whether to perform corresponding communication configuration.

[0045] In a second aspect, the present application discloses a long-distance cognitive wireless network self-organizing system, the system is applicable to the long-distance cognitive wireless network self-organizing method as described above, and the system comprises:

[0046] A node setting module, wherein the self-organizing node setting module is configured to: set a self-organizing node configured with a cognitive radio function;

[0047] A network setting module, the network setting module is configured to: obtain a long-distance cognitive wireless network based on the self-organizing node, and set a routing table for controlling a transmission path for the long-distance cognitive wireless network;

[0048] A data acquisition module, wherein the data acquisition module is configured to: acquire node data of each of the self-organizing nodes and routing data between each of the self-organizing nodes;

[0049] A routing update module, wherein the routing update module is configured to: calculate a routing score based on the node data and the routing data, and update the routing table based on the routing score;

[0050] A network reorganization module, wherein the network reorganization module is configured to: reorganize the long-range cognitive wireless network based on the updated routing table;

[0051] A distributed network construction module, wherein the distributed network construction module is configured to: configure a distributed long-distance recognition wireless network corresponding to the data fusion center.

[0052] In a third aspect, the present application discloses a long-distance cognitive wireless network self-organizing device, which is applicable to the long-distance cognitive wireless network self-organizing method as described above, and the device includes:

[0053] A network core board, the network core board is configured to perform communication processing of the long-range cognitive wireless network and control devices corresponding to the long-range wireless communication;

[0054] An amplifier module, the amplifier module being configured to perform reception, amplification and filtering of a signal based on a radio frequency component;

[0055] A communication module, wherein the communication module is configured to communicate based on dual antennas, wherein there is one RF transmission channel and two RF receiving channels, and the antennas of the dual antennas are omnidirectional antennas.

[0056] Beneficial effects: The long-distance cognitive wireless network self-organizing method, system and device of the present application realize the extension of the existing communication network by configuring self-organizing nodes and corresponding architecture based on the completed long-distance cognitive wireless network, acquisition and analysis of real-time data, update of routing tables and distributed long-distance cognitive wireless networks. On the basis of dynamically establishing new communication connections, it improves the poor self-organization and self-healing performance of the existing communication network and realizes self-management of nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 A flowchart of a long-distance cognitive wireless network self-organization method provided in an embodiment of the present application;

[0059] Figure 2 A structural block diagram of a long-distance cognitive wireless network self-organizing system provided in an embodiment of the present application;

[0060] Figure 3 A schematic block diagram of a long-distance cognitive wireless network self-organizing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0062] In this article, the term "comprising" is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprising..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0063] In a first aspect, this embodiment discloses Figure 1 A long-distance cognitive wireless network self-organization method is shown, the method comprising:

[0064] S1: Setting up a self-organizing node configured with cognitive radio function.

[0065] S2: obtaining a long-distance cognitive wireless network based on the self-organizing nodes, and setting a routing table for controlling a transmission path for the long-distance cognitive wireless network.

[0066] S3: Obtain node data of each organization node and routing data between each organization node.

[0067] S4: Calculate the routing score based on the node data and the routing data, and update the routing table based on the routing score.

[0068] S5: Reorganize the long-range cognitive wireless network based on the updated routing table.

[0069] S6: Configure a distributed long-distance recognition wireless network corresponding to the data fusion center.

[0070] In this embodiment, by configuring self-organizing nodes and corresponding architecture based on a completed long-distance cognitive wireless network, acquiring and analyzing real-time data, updating routing tables and distributed long-distance cognitive wireless networks, the existing communication network is extended. On the basis of dynamically establishing new communication connections, the poor self-organization and self-healing performance of the existing communication network is improved, and self-management of nodes is achieved.

[0071] Specifically, configuring a self-organizing node with a cognitive radio function includes the following steps:

[0072] A1: Set up corresponding nodes based on actual communication requirements.

[0073] A2: Configure the node with cognitive radio technology and enable the node to have cognitive radio function to obtain a self-organizing node. It should be noted that the cognitive radio technology used in this embodiment is any one of the existing cognitive radio technologies that enables nodes to have spectrum sensing capability.

[0074] In this embodiment, all self-organizing nodes are equal, frequency management is simple, and spectrum utilization is high. Any node device can be used as an end node, relay node or command node in the network. A wireless communication network can be quickly established at any time and any place without relying on any other fixed communication network facilities, such as optical fiber, copper cable, etc. All centerless same-frequency self-organizing network devices can automatically form a wireless mesh network by simply turning on the power, communicating with each other in real time, and the self-organizing nodes can effectively alleviate network congestion problems by dynamically increasing the transmission bandwidth of the bottleneck node in the self-organizing network.

[0075] Specifically, obtaining a long-distance cognitive wireless network based on self-organizing nodes and setting a routing table for controlling a transmission path for the long-distance cognitive wireless network specifically includes the following steps:

[0076] B1: Set up the corresponding wireless network framework based on actual communication needs.

[0077] B2: Input the self-organizing node into the wireless network framework accordingly, and obtain the corresponding node network data set, which includes the frequency band information, user information and node number information corresponding to the self-organizing node. It should be noted that the network architecture technology used in the long-distance cognitive wireless network of this embodiment and the distributed long-distance cognitive wireless network described later all use any one of the existing network architecture technologies, aiming to realize the basic network function of self-organizing network communication.

[0078] The frequency band information is used to represent the specific frequency range data and the corresponding authorization information.

[0079] User information is used to represent the user name and the corresponding level information. The level information is used to modify the routing score.

[0080] The node number information is used to represent the identification coding information of the self-organizing node in the wireless network framework.

[0081] B3: The wireless network framework based on the self-organizing node update with the node network dataset is defined as a long-range cognitive wireless network.

[0082] B4: Set the transmission path based on actual needs. The transmission path setting includes path setting and level setting. The path setting is used to control the communication to be transmitted based on the preset route, and the level setting is used to control the communication to be transmitted in sequence based on the preset level. It should be noted that the transmission path setting here provides an initialization transmission path for the long-distance cognitive wireless network, and provides data reference for the self-organizing nodes in the following text to reorganize the long-distance cognitive wireless network.

[0083] Specifically, the node data of each organization node and the routing data between each organization node are obtained, including:

[0084] Node data includes node working status and node network data set.

[0085] The routing data includes the routing working status between nodes, the routing quality score and the score is obtained by any existing communication channel quality evaluation method to achieve a score proportional to the communication quality, data service rate and bit error rate.

[0086] Specifically, the routing score is calculated based on the node data and routing data, including:

[0087] Obtain real-time node-to-node data and real-time routing data, and use the routing scoring formula to calculate the routing score, where the routing scoring formula is specifically:

[0088]

[0089] Among them, α is the working state of the node, and when the self-organizing node is powered on, α=1, and when it is not powered on, α=0. β is the working state of the route, and when the route is connected, β=1, and when it is not connected, β=0. CQS is the route quality score, DSR is the data service rate, BER is the bit error rate, and UsI is the user information. RS is the route score.

[0090] The route score is proportional to the communication quality of the route.

[0091] Specifically, updating the routing table based on the routing score includes the following steps:

[0092] C1: Eliminate the self-organizing nodes corresponding to α=0.

[0093] C2: Eliminate the self-organizing nodes at both ends corresponding to RS=1.

[0094] C3: Sort the routes from large to small based on the route scores, and update the routing table based on the sorting results.

[0095] Specifically, reorganizing the long-distance cognitive wireless network based on the updated routing table specifically includes the following steps:

[0096] D1: Perform routing settings based on actual communication requirements and the updated routing table.

[0097] D2: Level setting is performed based on actual communication requirements and the results of route reconstruction.

[0098] D3: Based on the route setting in step D2 and the level setting in step D3, the reorganized long-distance cognitive wireless network is obtained and the communication after self-organization is performed. It should be noted that, in this embodiment, when the long-distance cognitive wireless network is reorganized, the self-organization and communication network reconstruction of each organization node is performed based on the updated routing table, and each organization node transmits information with a high user level first and preferentially selects a route with a high routing score for transmission.

[0099] In this example, the long-distance cognitive wireless network optimizes the existing communication network on the basis of realizing multiple nodes, rapid deployment, and flexible networking. When the working status of each self-organizing node changes, the routing relationship between multiple nodes is automatically recalculated and the routing table is updated based on various indicators such as channel quality, data service rate, and bit error rate between multiple self-organizing nodes, so as to achieve real-time update of network topology. Furthermore, if the remaining bandwidth of a certain optimal path link is insufficient, the suboptimal path can be automatically selected for information transmission.

[0100] It can be understood that in this embodiment, as the number of self-organizing nodes participating in the collaboration increases, the probability of false detection in the long-distance cognitive wireless network decreases, thereby improving the perception accuracy, achieving a balance between performance and complexity, and calculating the optimal number of collaborative nodes, thereby improving the transmission rate and network capacity.

[0101] Specifically, the distributed long-distance recognition wireless network corresponding to the data fusion center is configured, including:

[0102] By using centralized model technology, the data fusion center in the current long-distance recognition wireless network is captured, and the self-organizing nodes communicate with the data fusion center, which performs fusion processing and then determines whether to perform the corresponding communication configuration.

[0103] In a long-distance cognitive wireless network with cognitive capabilities, each organization node senses and uses idle spectrum while transmitting data, maximizing spectrum utilization without interfering with authorized users. However, the traditional technology used to establish the optimal number of collaborative nodes solves the model, ignoring the distribution distance difference and channel environment difference between the perception node and the authorized node, and therefore cannot fully reflect the differences in the topology changes of the actual cognitive self-organizing network. Therefore, in this embodiment, based on the long-distance cognitive virtual wireless network, and based on the centralized model technology, the distributed long-distance cognitive wireless network architecture is realized, which ensures ROC performance while effectively reducing the perception calculation complexity and node overhead.

[0104] In a second aspect, this embodiment discloses Figure 2 A long-distance cognitive wireless network self-organizing system is shown, and the system is applicable to the above long-distance cognitive wireless network self-organizing method, and the system includes:

[0105] The node setting module is configured to: set the self-organizing node with cognitive radio function.

[0106] The network setting module is configured to: obtain a long-distance cognitive wireless network based on the self-organizing nodes, and set a routing table for controlling a transmission path for the long-distance cognitive wireless network.

[0107] The data acquisition module is configured to acquire the node data of each organization node and the routing data between each organization node.

[0108] The routing update module is configured to: calculate a routing score based on the node data and the routing data, and update the routing table based on the routing score.

[0109] The network reorganization module is configured to reorganize the long-distance cognitive wireless network based on the updated routing table.

[0110] The distributed network construction module is configured to: configure a distributed long-distance recognition wireless network corresponding to the data fusion center.

[0111] In the third aspect, this embodiment discloses Figure 3 A long-distance cognitive wireless network self-organizing device is shown, and the device is applicable to the above long-distance cognitive wireless network self-organizing method, and the device includes:

[0112] The network core board is configured to perform communication processing of the long-distance cognitive wireless network and control the corresponding equipment of the long-distance wireless communication.

[0113] The amplifier module is configured to receive, amplify and filter the signal based on the radio frequency component.

[0114] The communication module is configured to communicate based on dual antennas, wherein there is one RF transmission channel and two RF receiving channels, and the antennas of the dual antennas are omnidirectional antennas.

[0115] In this embodiment, the long-distance cognitive wireless self-organizing network device is suitable for fast wireless self-organizing network transmission in various complex environments. It is mainly deployed in mobile mode, and can also be installed by stakes. It is mainly used as a vehicle-mounted station or a fixed stake access node for wide-area wireless coverage to aggregate data from multiple self-organizing nodes. Through interface settings, interconnection including wired Ethernet and wireless networks can be achieved. It can be quickly deployed at the emergency site and quickly build a private broadband network environment. The signal can automatically select the nearest path to continuously jump from one node to another, and finally reach the target node that cannot be directly line of sight.

[0116] In summary, the long-distance cognitive wireless network self-organizing method, system and device of the present embodiment realizes the extension of the existing communication network by configuring self-organizing nodes and the corresponding architecture based on the completed long-distance cognitive wireless network, the acquisition and analysis of real-time data, the updating of routing tables and the distributed long-distance cognitive wireless network. On the basis of dynamically establishing new communication connections, the poor self-organization and self-healing performance of the existing communication network is improved, and self-management of nodes is realized.

[0117] In the embodiments provided in the present application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following units: application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing device (DSPD), programmable logic device (PLD), field programmable gate array (FPGA), processor, controller, microcontroller, microprocessor, other electronic units designed to implement the functions described herein or their combination. For software implementation, part or all of the flow of the embodiment can be completed by instructing the relevant hardware through a computer program. When implemented, the above program can be stored in a computer-readable storage medium or transmitted as one or more instructions or codes on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein the communication medium includes any medium that is convenient for transmitting a computer program from one place to another. The storage medium can be any available medium that a computer can access. The computer-readable storage medium can include but is not limited to RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer.

[0118] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A self-organizing method for a long-distance cognitive wireless network, characterized in that: The method includes: S1: Setting up a self-organizing node with cognitive radio function; S2: obtaining a long-distance cognitive wireless network based on the self-organizing node, and setting a routing table for controlling a transmission path for the long-distance cognitive wireless network; S3: Acquire node data of each of the self-organizing nodes and routing data between the self-organizing nodes; S4: Calculate a routing score based on the node data and the routing data, and update the routing table based on the routing score; S5: reorganizing the long-range cognitive wireless network based on the updated routing table; S6: Configure a distributed long-distance recognition wireless network corresponding to the data fusion center.

2. The long-distance cognitive wireless network self-organizing method according to claim 1, characterized in that: The configuration of the self-organizing node with cognitive radio function specifically includes the following steps: A1: Set up corresponding nodes based on actual communication needs; A2: Perform cognitive radio technology configuration on the node and enable the node to have the cognitive radio function to obtain the self-organizing node.

3. The long-distance cognitive wireless network self-organizing method according to claim 1, characterized in that: The method of obtaining a long-distance cognitive wireless network based on the self-organizing node and setting a routing table for controlling a transmission path for the long-distance cognitive wireless network specifically includes the following steps: B1: Setting a corresponding wireless network framework based on the actual communication requirements; B2: inputting the self-organizing node into the wireless network framework accordingly, and obtaining a corresponding node network data set, wherein the node network data set includes frequency band information, user information and node number information corresponding to the self-organizing node; The frequency band information is used to represent specific frequency range data and corresponding authorization information; The user information is used to represent the user name and the corresponding level information; The node number information is used to represent the identification coding information of the self-organizing node in the wireless network framework; B3: defining the wireless network framework after the self-organizing node update based on the node network data set as the long-range cognitive wireless network; B4: Setting the transmission path based on the actual demand; the setting of the transmission path includes path setting and level setting, the path setting is used to control the communication to be transmitted based on a preset route, and the level setting is used to control the communication to be transmitted in sequence based on a preset level.

4. The long-distance cognitive wireless network self-organizing method according to claim 3, characterized in that: The obtaining of the node data of each of the self-organizing nodes and the routing data between the self-organizing nodes specifically includes: The node data includes the node working status and the node network data set; The routing data includes the routing working status between nodes, routing quality score, data service rate and bit error rate.

5. The long-distance cognitive wireless network self-organizing method according to claim 4, characterized in that: The calculating of the routing score based on the node data and the routing data specifically includes: Acquire real-time node-to-node data and real-time routing data, and calculate routing scores using a routing scoring formula, wherein the routing scoring formula is specifically: Wherein, α is the working state of the node, and when the self-organizing node is powered on, α=1, and when it is not powered on, α=0; β is the working state of the routing, and when the routing is connected, β=1, and when it is not connected, β=0; CQS is the routing quality score, DSR is the data service rate, BER is the bit error rate, UsI is the user information; RS is the routing score; The route score is proportional to the communication quality of the route.

6. The long-distance cognitive wireless network self-organizing method according to claim 5, characterized in that: The updating of the routing table based on the routing score specifically includes the following steps: C1: Eliminate the self-organizing node corresponding to α=0; C2: Eliminate the self-organizing nodes at both ends corresponding to RS=1; C3: sorting the routes from large to small based on the route scores, and updating the routing table based on the sorting result.

7. The long-distance cognitive wireless network self-organizing method according to claim 6, characterized in that: The reorganization of the long-distance cognitive wireless network based on the updated routing table specifically includes the following steps: D1: performing the routing setting based on the actual communication demand and the updated routing table; D2: performing the level setting based on the actual communication demand and the result of the route reconstruction; D3: Based on the routing setting in step D2 and the level setting in step D3, the reorganized long-distance cognitive wireless network is obtained and self-organized communication is performed.

8. The long-distance cognitive wireless network self-organizing method according to claim 1, characterized in that: The configuration described above corresponds to a distributed long-distance recognition wireless network of a data fusion center, specifically including: By using the centralized model technology, the data fusion center in the current long-distance recognition wireless network is captured, and the self-organizing node communicates with the data fusion center, which performs fusion processing and then determines whether to perform corresponding communication configuration.

9. A long-distance cognitive wireless network self-organizing system, the system being applicable to the long-distance cognitive wireless network self-organizing method as claimed in any one of claims 1 to 8, characterized in that: The system includes: A node setting module, wherein the self-organizing node setting module is configured to: set a self-organizing node configured with a cognitive radio function; A network setting module, the network setting module is configured to: obtain a long-distance cognitive wireless network based on the self-organizing node, and set a routing table for controlling a transmission path for the long-distance cognitive wireless network; A data acquisition module, wherein the data acquisition module is configured to: acquire node data of each of the self-organizing nodes and routing data between each of the self-organizing nodes; A routing update module, wherein the routing update module is configured to: calculate a routing score based on the node data and the routing data, and update the routing table based on the routing score; A network reorganization module, wherein the network reorganization module is configured to: reorganize the long-range cognitive wireless network based on the updated routing table; A distributed network construction module, wherein the distributed network construction module is configured to: configure a distributed long-distance recognition wireless network corresponding to the data fusion center.

10. A long-distance cognitive wireless network self-organizing device, the device being applicable to the long-distance cognitive wireless network self-organizing method as claimed in any one of claims 1 to 8, characterized in that: The device includes: A network core board, the network core board is configured to perform communication processing of the long-range cognitive wireless network and control devices corresponding to the long-range wireless communication; An amplifier module, the amplifier module being configured to perform reception, amplification and filtering of a signal based on a radio frequency component; A communication module, wherein the communication module is configured to communicate based on dual antennas, wherein there is one RF transmission channel and two RF receiving channels, and the antennas of the dual antennas are omnidirectional antennas.

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