Networking protocol implementation method and system applied to ad hoc network

Abstract

The invention relates to the technical field of networking protocol implementation methods, in particular to a networking protocol implementation method and system applied to an ad hoc network, and the method comprises the following steps: creating an Rf channel table; creating an Rf node table; creating an Rf forwarding table; creating an Rf control table, performing storage management on the data information of the networking channel through the Rf control table, and realizing dynamic adjustment of the data transmission priority through a data priority strategy so as to ensure that data with different priorities meet preset transmission requirements in a transmission process; in a communication node management process, through an algorithm based on a composite mathematical model, adaptive weight calculation, dynamic expansion threshold setting, progressive sorting and Markov chain regression prediction, multi-layer progressive control and dynamic optimization of communication node priorities are realized in sequence, the node priorities can be updated in real time by an adaptive weight model, and the communication node priorities can be effectively optimized. And flexible response is performed according to the weight adjustment requirements of the node in different network states, so that the response capability of the network to the burst load is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of networking protocol implementation methods, and in particular to a networking protocol implementation method and a system thereof applied to a self-organizing network. Background Art

[0002] In the field of ad-hoc networks, with the continuous expansion of network scale and diversification of application scenarios, how to ensure the stability of communication and the efficiency of data transmission under the rapid increase of network nodes has become the focus and difficulty of network technology development. Existing technologies usually rely on fixed networking protocols or preset static priority algorithms to manage network nodes and transmit data. However, these methods have obvious bottlenecks and limitations when facing sudden data traffic or dynamically changing network topology.

[0003] Traditional self-organizing networking protocols generally adopt a static hierarchical structure, using a single node weight as the basis for priority division, or simply relying on the node's bandwidth and latency for simple sorting. Such methods lack dynamic adaptive capabilities. When the network load suddenly changes or nodes frequently join and exit, it is often impossible to flexibly adjust the node priority, resulting in the inability to guarantee the priority transmission rights of high-priority nodes. In addition, the setting of static thresholds in the prior art is usually out of touch with the actual data traffic and node status, and it is difficult to adjust in real time with the network load. The limitation of this static threshold causes a sharp increase in transmission delay when a sudden high load occurs in the network, and even causes the loss of critical data, which seriously affects the overall transmission efficiency of the network.

[0004] In terms of dynamic priority adjustment, most existing methods use a simple weight update model or a single hierarchical structure, which cannot achieve effective node priority optimization and data traffic management under various network conditions. These traditional methods lack a dynamic adaptive adjustment mechanism in the hierarchical weight allocation, resulting in the inability of node priorities in the data transmission process to adapt to the changing network environment. At the same time, because the existing methods fail to fully consider the synergy between algorithms at different levels, the adjustment speed of node weights and priority sorting is slow and the response is delayed. In addition, the existing technology lacks an effective prediction mechanism in node priority sorting and data transmission control, making it difficult to achieve reasonable regulation of future data transmission loads, resulting in unstable communication quality under high network load conditions.

[0005] Especially in the transmission scenarios involving high-priority data, the division and sorting of node priorities in traditional methods are often achieved through a single-level weight adjustment, ignoring the mutual complementation and synergy of different algorithm structures. For example, node priority sorting in traditional ad hoc networks is mostly based on real-time node status, lacks sufficient consideration of historical status, and fails to effectively achieve dynamic adjustment of stability scores. In addition, the lack of support for prediction mechanisms means that priority sorting can only rely on real-time changing network parameters, and cannot respond to node load fluctuations in advance. Due to the lack of a dynamic hierarchical optimization mechanism, in a high-load environment, traditional methods often cause confusion in node priority sorting, making it impossible to effectively meet the data transmission needs of key nodes, affecting the overall stability of the network and the timeliness of data transmission. Summary of the invention

[0006] Based on the various deficiencies existing in the above-mentioned background technology, the present invention proposes a networking protocol implementation method and system for self-organizing networks, aiming to solve technical problems in the prior art such as inaccurate node priority division, delayed dynamic adjustment of priority, lack of stability score prediction mechanism, and insufficient transmission stability under sudden high load conditions.

[0007] The present invention provides a networking protocol implementation method for self-organizing networks, comprising the following steps: creating an Rf channel table, managing data transmission of a networking channel through the Rf channel table, wherein the Rf channel table contains preset channel parameters and divides the channel quantity and number based on the channel parameters; creating an Rf node table, managing the communication node status of each access device through the Rf node table, marking the communication node as a fixed node or a floating node according to the network status of the communication node, and assigning a node level to each communication node; creating an Rf forwarding table, managing the level information of the communication node through the Rf forwarding table, and controlling the data transmission operation of the communication node based on preset rules; creating an Rf control table, storing and managing the data information of the networking channel through the Rf control table, and realizing dynamic adjustment of the data transmission priority through a data priority strategy to ensure that data of different priorities meet the preset transmission requirements during the transmission process; in the communication node management process, realizing multi-layer progressive control and dynamic optimization of the communication node priority in turn through an algorithm based on a composite mathematical model, adaptive weight calculation, dynamic expansion threshold setting, progressive sorting and Markov chain regression prediction.

[0008] Preferably, the communication node priority allocation is implemented by a composite mathematical model, which includes the following steps: receiving input data of each communication node, wherein the data includes delay, bandwidth occupancy and stability score information; calculating the priority score of the communication node, the formula is: in For Node The priority score, is the number of layers for composite calculation, For the Layer composite weight factor, For Node In the The transmission delay of the layer, For Node In the The bandwidth utilization of the layer, For Node The stability score of is the stability weighted coefficient; the priority score of each communication node is obtained according to the formula , and pass it to the progressive priority weight adaptive calculation step for the next step of weight adjustment.

[0009] Preferably, the priority weight adaptive calculation is performed based on a hierarchical model, and the hierarchical model includes the following steps: Calculate the weight adjustment factor for each communication node , and its calculation formula is: in, For Node The weight adjustment factor is Indicates the hierarchical level. is the adaptive layer weight, To prevent a small constant from being zero in the denominator; we get Used to update the priority of the communication node and adjust the priority basis in the next threshold setting.

[0010] Preferably, the priority threshold is set by a dynamic expansion model, and the dynamic expansion model comprises the following steps: based on the weight adjustment factor Calculate the high priority threshold of the communication node , and its calculation formula is: in, is the high priority threshold, To control the parameters, is the basic threshold constant, is the natural logarithm base; the setting result of the threshold is used to perform dynamic priority sorting in the next step and is used for priority improvement adjustment under high load conditions.

[0011] Preferably, the priority dynamic sorting is implemented by a time series progressive model, and the sequence progressive model includes the following steps: calculating the progressive sorting value of each node based on the time series data progressively , the formula is: in, For Node The progressive sorting value of To adjust the time response rate parameter of the weight, and are the time change rates of priority and weight respectively; the dynamic priority sorting result is obtained through the sorting model, and the dynamic optimization control of the communication node is performed in the next step.

[0012] Preferably, the stability prediction of the communication node is implemented by a Markov chain regression prediction model, which includes the following steps: acquiring and analyzing the historical data of the communication node, and calculating the future stability score of the node based on the Markov chain, and the formula is: in, For time Time Node The predicted stability score of is the number of states, For Node To Node The state transition probability matrix of the communication node is used to optimize the communication node priority and update the node priority score. and weight .

[0013] Preferably, the composite mathematical model of priority allocation and the calculation results of the hierarchical layered model cooperate to form a communication node priority distribution structure, and realize dynamic management of data transmission channels according to different priorities of communication nodes.

[0014] Preferably, the priority threshold setting and priority dynamic sorting steps realize the improvement or reduction of the priority of the communication node, so that the communication level of the node can be efficiently adjusted within the preset threshold range.

[0015] Preferably, the calculation result of the stability prediction model updates the ranking value of the communication node in real time under high load conditions. , and is used to dynamically adjust node transmission priorities.

[0016] A networking protocol implementation system applied to a self-organizing network, comprising: an Rf channel management unit, used to create an Rf channel table and manage the data transmission of the networking channel based on the Rf channel table; an Rf node management unit, used to create an Rf node table and manage the state of each communication node through the Rf node table; an Rf networking management unit, used to create an Rf forwarding table and manage the level information of the communication node based on the Rf forwarding table; a priority dynamic adjustment unit, used to create an Rf control table, realize the dynamic adjustment of the data transmission priority through the Rf control table, and optimize the priority management of the communication node based on a composite mathematical model, an adaptive weight model, a dynamic expansion model, a time series progressive model and a Markov chain prediction model. Advantages and beneficial effects of the present invention: The present invention uses five progressive algorithms, including an innovative composite mathematical model, an adaptive weight model, a dynamically extended priority threshold setting, a time series progressive sorting model, and a Markov chain regression prediction model, to collaboratively improve the accuracy of node priority and the efficiency of transmission management.

[0017] The present invention constructs a hierarchical structure of node priorities through a composite mathematical model, dynamically allocates node priorities, and solves the problem of inaccurate priority division under a single hierarchical structure in existing methods. The adaptive weight model can update node priorities in real time, flexibly respond to the weight adjustment requirements of nodes under different network states, and improve the network's responsiveness to sudden loads. The dynamically extended priority threshold setting is further dynamically adjusted by setting high and low thresholds, making the priority setting of nodes under high load more flexible, ensuring the transmission success rate of high-priority data.

[0018] The time series progressive sorting model of the present invention can achieve efficient update of node priority sorting in a dynamic environment by sorting and controlling the time series change rate of node priority, thus solving the problem of sorting lag in the prior art. The Markov chain regression prediction model uses the probability transfer of the node historical state to predict the stability score of the future network state, ensuring stability control under burst data traffic, and making up for the lack of stability prediction in the prior art.

[0019] Therefore, the present invention establishes a node priority control mechanism with high adaptability and dynamic adjustment capabilities through the collaborative work and progressive optimization of multi-level algorithms, which not only improves the data transmission efficiency and stability in the self-organizing network environment, but also significantly improves the network response speed and the accuracy of node priority division in high-load environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention is a flow chart of the method.

[0021] Figure 2It is a flow chart of the Rf networking management unit and the priority dynamic adjustment unit of the present invention.

[0022] Figure 3 This is a flow chart of the priority calculation and adjustment algorithm of the present invention.

[0023] Figure 4 This is a structural diagram of the system core module of the present invention. DETAILED DESCRIPTION

[0024] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects thereof are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0025] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0026] Example 1 See also Figure 1-4The present invention relates to a networking protocol implementation method and system for self-organizing networks, which aims to effectively manage networking channels and communication nodes in a self-organizing network environment through a series of tables, nodes and innovative algorithms for priority control, and realize dynamic adjustment of data transmission priorities of communication nodes. The system realizes data transmission management and dynamic priority control optimization of the networking protocol through the collaborative work of modules such as Rf channel table, Rf node table, Rf forwarding table, and Rf control table. In the present invention, Rf usually refers to the abbreviation of "Radio Frequency". Rf channel table, Rf node table, Rf forwarding table and Rf control table are key data table structures set for networking and transmission management in radio frequency communication. Each table has different functions in the self-organizing network networking protocol implementation method and system, which are as follows: Rf channel table is used to manage the configuration of radio frequency channels and data transmission paths. The table stores the numbers of each channel and the preset channel parameters, which are used to divide the number of channels and assign channel numbers so as to effectively manage and store radio frequency data. This helps to ensure the orderliness and efficiency of data transmission between communication nodes in the self-organizing network environment. The Rf node table is responsible for managing nodes in RF communication, especially recording the status of access devices, node levels and network status. Each communication node (such as access devices) is marked as a fixed node or a floating node, and is assigned a corresponding node level. This structure ensures that the system can achieve efficient data flow control and node priority adjustment under different network loads. The Rf forwarding table is used to mark and manage the level information of communication nodes, and is responsible for the operation and management of data transmission of communication nodes based on the node level and preset transmission rules. Through this table, the system can dynamically adjust the priority of data transmission according to the node level to ensure that high-priority nodes can obtain priority data transmission rights. The Rf control table stores the data transmission priority and dynamic adjustment rules of the RF channel. By dynamically setting the channel data transmission threshold, the priority transmission of data of different priorities in the self-organizing network is realized. The Rf control table also includes the setting and sending of priority strategies to ensure real-time adjustment of data transmission under high load conditions.

[0027] The design of these tables is based on the needs of RF communication, taking advantage of the flexibility of RF (radio frequency) channels to manage and optimize data transmission paths, node status, and transmission priorities in ad hoc networks.

[0028] First, the present invention provides a networking protocol implementation method for self-organizing networks, which mainly includes creating an Rf channel table, an Rf node table, an Rf forwarding table and an Rf control table, and gradually forming a collaborative management system. The Rf channel management unit 1 is used to create an Rf channel table, and divides the number of channels and numbers them by setting channel parameters. Preferably, the channel parameter range can be set between 0 and 255 to support efficient data transmission of multiple nodes. In addition, the Rf node management unit 2 manages the communication nodes of each access device through the Rf node table, assigns communication node levels and distinguishes fixed nodes from floating nodes. The node level is used to determine the data transmission priority of the node, so that it can still ensure the transmission of important data under high load.

[0029] The Rf forwarding table is established by the Rf networking management unit 3, which is mainly used to record the level information of the communication node, and to control the data transmission operation based on this information and preset rules. The present invention also realizes the data information storage and priority dynamic adjustment of the networking channel through the Rf control table. By setting the channel data transmission quantity threshold, the preferred range is set to 100 to 1000, which can ensure that high priority data is transmitted first, and meet the data transmission requirements of different nodes in the self-organizing network.

[0030] Preferably, the present invention provides a composite mathematical model for node priority assignment, aiming to effectively assign node priorities. Specifically, the present invention calculates the priority score of a communication node by the following formula: in, For Node The priority score of Indicates the number of layers of composite calculation, preferably set to 3 layers to more finely analyze the priority requirements under different network loads; is the weight factor of each layer, the value range is set to 0 to 1, and the sum of the weights of each layer is 1; Representation Node In the Transmission delay of the layer; Indicates bandwidth occupancy, preferably the ratio of bandwidth occupied by the node; Score the stability of the node on a scale of 0 to 10; is a stability weighting coefficient, and its range is preferably set to 0.1 to 0.5. The above calculation result is passed to the subsequent steps as the priority score of the node, so that the priority of the node can be effectively distinguished according to different levels of the network environment.

[0031] Preferably, the present invention adopts a hierarchical priority weight adaptive calculation model. The Rt forwarding table calculates the weight adjustment factor of each communication node according to the priority score. , and adopt the following hierarchical stratification formula: in, For Node The weight adjustment factor is Indicates the hierarchical level. is the number of layers, preferably set to 3; is the adaptive stratification weight, the value range is set to 0 to 1, and the sum is 1; To prevent a tiny constant whose denominator is zero, the value is preferably 0.001. Through this hierarchical model, the system can automatically adjust the priority according to the delay and bandwidth conditions of the node to adapt to the dynamic environment of the network.

[0032] The present invention further provides a dynamically extended priority threshold setting model, based on the weight adjustment factor Calculate the high priority threshold of the communication node , and use the following formula: in, is the high priority threshold; is a control parameter, preferably in the range of 0.5 to 2, to adjust the response amplitude of the threshold; is a basic threshold constant, preferably 10, to set the lowest priority standard; is the base of the natural logarithm. By adjusting The present invention can dynamically set the threshold value so that it can automatically increase the priority of the node under high load conditions to ensure that the priority of the key node is not affected by the traffic.

[0033] Based on the priority threshold setting, the present invention realizes the dynamic sorting of priorities through the time series progressive model. Specifically, the progressive sorting value of each node is calculated. , the formula is as follows: in, For Node The progressive sorting value of ; is a time response rate parameter, preferably set in the range of 0.1 to 1.5 to adjust the sorting response rate; and They represent the time change rates of node priority and weight respectively, and dynamic sorting is used to ensure that key nodes can always maintain priority transmission rights in instantaneous changes.

[0034] In order to ensure the long-term stability of node priority, the present invention adopts a Markov chain regression prediction model to achieve future prediction of node stability scores. Specifically: in, For time Time Node 's predicted stability score; Indicates the number of different states of the network. The preferred value is 4 to ensure the score prediction of nodes in multiple states. is the state transition probability matrix. The algorithm incorporates the state transition probability into the prediction of the stability score, so that the node priority can be proactively adjusted in a changing environment.

[0035] Preferably, the present invention calculates the priority distribution structure of each communication node through the composite mathematical model and the hierarchical model, thereby realizing dynamic management of the data transmission channel. Preferably, the present invention enables the node to maintain efficient data flow under high load environment through automatic adjustment of the priority distribution.

[0036] Furthermore, the priority threshold setting and dynamic priority sorting steps can automatically adjust the priority level of the communication node so that the priority increase or decrease strategy can accurately match the actual network demand. Exceed , the node is automatically promoted to high priority to achieve optimal resource configuration.

[0037] When the network is highly loaded, the results of the stability prediction model are used to dynamically update the ranking values ​​of the communication nodes. , to ensure the stability of high priority nodes in transmission priority. Preferably, The update frequency is set to every 5 seconds to ensure dynamic adjustment of the response speed when the data traffic changes.

[0038] The present invention also provides a networking protocol implementation system for a self-organizing network, including: an Rf channel management unit 1, used to create an Rf channel table and manage the data transmission of the networking channel based on the Rf channel table; an Rf node management unit 2, used to create an Rf node table and manage the state of each communication node through the Rf node table; an Rf networking management unit 3, used to create an Rf forwarding table and manage the level information of the communication node based on the Rf forwarding table; a priority dynamic adjustment unit 4, used to create an Rf control table, realize the dynamic adjustment of the data transmission priority through the Rf control table, and optimize the priority management of the communication node based on the composite mathematical model, the adaptive weight model, the dynamic expansion model, the time series progressive model and the Markov chain prediction model. The modules in this system work closely together to improve the communication efficiency and network stability of the self-organizing network through innovative algorithms, thereby realizing the data transmission management and priority dynamic optimization of the networking protocol.

[0039] In order to verify the superiority of the present invention in the implementation of the networking protocol of the self-organizing network, the embodiments of the present invention were compared with the existing networking protocol under the same conditions. The innovative advantages of the present invention were verified by comparing the key performance indicators in the self-organizing network environment, including data transmission delay, dynamic adjustment response speed of node priority, stability score prediction accuracy, and transmission success rate under high load environment. The detection method uses the industry-recognized data set "NS-3 network simulation data set" and follows the ISO / IEC9646 protocol compliance test standard to ensure the reliability and scientificity of the test process.

[0040] In Example 1, the networking protocol implementation method of the present invention optimizes node priority allocation and data transmission management in a self-organizing network environment through the synergy of multiple algorithms such as composite mathematical models, adaptive weight models, dynamic expansion threshold setting, progressive sorting, and Markov chain regression prediction models. The environment is set to 500 communication nodes, the bandwidth occupancy rate does not exceed 60%, the data flow is 200kb to 400kb per second, and the test period is 60 seconds.

[0041] Data transmission delay: A composite mathematical model is used to optimize the delay and dynamically adjust the priority, so that the data transmission delay is kept at a low level. The measured data shows that the average delay is around 28ms.

[0042] Node priority dynamically adjusts response speed: In burst data traffic, the system can quickly adjust node priority response according to the progressive sorting model, with a response speed of about 100ms, which can effectively improve the transmission success rate of high-priority nodes.

[0043] Stability score prediction accuracy: The node stability score is predicted through the Markov chain regression prediction model with an accuracy of 95%, enabling the system to automatically adjust the data transmission priority under sudden high load conditions, thereby ensuring the stability of communication.

[0044] Transmission success rate under high-load environment: When the data traffic suddenly increases to 600kb, the transmission success rate remains above 96%, which fully proves the stability and superiority of the present invention under high-load environment.

[0045] Comparative Example 1 adopts the traditional hierarchical weight calculation and static threshold setting method, and the environmental conditions are consistent with the embodiment, and the number of test nodes, bandwidth limit and test cycle are the same.

[0046] Data transmission delay: The composite mathematical model of the present invention is not adopted, so the delay optimization effect is limited. The average delay test result is 47ms, which is significantly higher than that of the embodiment.

[0047] Node priority dynamic adjustment response speed: Due to the use of static threshold adjustment method, the response speed is slow, about 400ms, and it is unable to respond to burst data traffic in time, resulting in a significant decrease in the data transmission efficiency of high-priority nodes.

[0048] Stability score prediction accuracy: No prediction model is used, so the stability score accuracy under high load conditions is low, only 70%, and the communication stability guarantee effect under sudden conditions is poor.

[0049] Transmission success rate under high load conditions: When the data flow increases to 600kb, the transmission success rate drops to about 72%, proving that traditional solutions are difficult to maintain high stability under high load conditions.

[0050] The test results are summarized as follows: index Example 1 Comparative Example 1 Data transmission delay (ms) 28 47 Node priority dynamic adjustment response speed (ms) 100 400 Stability score prediction accuracy (%) 95 70 Transmission success rate under high load environment (%) 96 72 It can be seen from the above test data that the embodiments of the present invention show significant superiority in a self-organizing network environment. First, through the use of a composite mathematical model, the data transmission delay is reduced to 28ms, which is significantly better than the 47ms of the control group. This shows that the present invention can effectively shorten the delay and improve the efficiency of data transmission under the condition of multi-layer optimization priority. Secondly, in terms of the dynamic adjustment response speed of node priority, due to the use of a progressive sorting model, the response speed is increased to 100ms, which is nearly 4 times faster than the control group. This superiority shows the ability to respond quickly, especially under burst traffic conditions, making the data transmission of high-priority nodes more efficient.

[0051] In terms of stability score prediction accuracy, by introducing the Markov chain prediction model, the score accuracy of the present invention reaches 95%, which is significantly improved compared to the 70% of the control group, ensuring high transmission stability in complex network environments. In addition, in the transmission success rate test under high load conditions, the embodiment can maintain a transmission success rate of 96%, which is significantly better than the 72% of the control group. This verifies the innovative effect of the present invention in dynamic priority adjustment and node transmission guarantee. In summary, the present invention shows excellent results in indicators such as data transmission delay, response speed, prediction accuracy and high-load transmission success rate, demonstrating its innovation and applicability in a self-organizing network environment.

[0052] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for implementing a networking protocol applied to a self-organizing network, characterized in that: The following steps are involved: Creating an Rf channel table, managing data transmission of networking channels through the Rf channel table, wherein the Rf channel table includes preset channel parameters, and divides the number and number of channels based on the channel parameters; Creating an Rf node table, managing the communication node status of each access device through the Rf node table, marking the communication node as a fixed node or a floating node according to the network status of the communication node, and assigning a node level to each communication node; Creating an Rf forwarding table, managing the level information of the communication nodes through the Rf forwarding table, and controlling the data transmission operation of the communication nodes based on preset rules; An Rf control table is created, through which the data information of the networking channel is stored and managed, and the data transmission priority is dynamically adjusted through the data priority strategy to ensure that data of different priorities meet the preset transmission requirements during the transmission process; in the communication node management process, multi-layer progressive control and dynamic optimization of the communication node priority are implemented in turn through an algorithm based on a composite mathematical model, adaptive weight calculation, dynamic expansion threshold setting, progressive sorting and Markov chain regression prediction.

2. The method for implementing a networking protocol applied to an ad hoc network according to claim 1, characterized in that: The communication node priority allocation is implemented through a composite mathematical model, which includes the following steps: receiving input data of each communication node, wherein the data includes delay, bandwidth occupancy and stability score information; calculating the priority score of the communication node, the formula is: ； in For Node The priority score, is the number of layers for composite calculation, For the Layer composite weight factor, For Node In the The transmission delay of the layer, For Node In the The bandwidth utilization of the layer, For Node The stability score of is the stability weighted coefficient; the priority score of each communication node is obtained according to the formula , and pass it to the progressive priority weight adaptive calculation step for the next step of weight adjustment.

3. The method for implementing a networking protocol applied to an ad hoc network according to claim 2, characterized in that: The priority weight adaptive calculation is performed based on a hierarchical model, and the hierarchical model includes the following steps: Calculate the weight adjustment factor for each communication node , and its calculation formula is: ； in, For Node The weight adjustment factor is Indicates the hierarchical level. is the adaptive layer weight, To prevent a small constant from being zero in the denominator; we get Used to update the priority of the communication node and adjust the priority basis in the next threshold setting.

4. The method for implementing a networking protocol applied to an ad hoc network according to claim 3, characterized in that: The priority threshold is set by a dynamic expansion model, and the dynamic expansion model includes the following steps: based on the weight adjustment factor Calculate the high priority threshold of the communication node , and its calculation formula is: ； in, is the high priority threshold, To control the parameters, is the basic threshold constant, is the natural logarithm base; the setting result of the threshold is used to perform dynamic priority sorting in the next step and is used for priority improvement adjustment under high load conditions.

5. The method for implementing a networking protocol applied to an ad hoc network according to claim 4, characterized in that: The dynamic priority sorting is implemented through a time series progressive model, which includes the following steps: calculating the progressive sorting value of each node based on the time series data progressively , the formula is: ； in, For Node The progressive sorting value of To adjust the time response rate parameter of the weight, and are the time change rates of priority and weight respectively; the dynamic priority sorting result is obtained through the sorting model, and the dynamic optimization control of the communication node is performed in the next step.

6. The method for implementing a networking protocol applied to a self-organizing network according to claim 5, characterized in that: The stability prediction of the communication node is realized by a Markov chain regression prediction model, which includes the following steps: acquiring and analyzing the historical data of the communication node, and calculating the future stability score of the node based on the Markov chain, and the formula is: ； in, For time Time Node The predicted stability score of is the number of states, For Node To Node The state transition probability matrix of the communication node is used to optimize the communication node priority and update the node priority score. and weight .

7. The method for implementing a networking protocol applied to an ad hoc network according to claim 6, characterized in that: The composite mathematical model of priority allocation and the calculation results of the hierarchical model cooperate to form a communication node priority distribution structure, and realize dynamic management of data transmission channels according to different priorities of communication nodes.

8. The method for implementing a networking protocol applied to an ad hoc network according to claim 7, characterized in that: The priority threshold setting and priority dynamic sorting steps realize the improvement or reduction of the priority of the communication node, so that the communication level of the node can be efficiently adjusted within the preset threshold range.

9. The method for implementing a networking protocol applied to a self-organizing network according to claim 8, characterized in that: The calculation results of the stability prediction model update the ranking value of the communication node in real time under high load conditions , and is used to dynamically adjust node transmission priorities.

10. A networking protocol implementation system for an ad hoc network that implements the method according to any one of claims 1 to 9, characterized in that: include: An Rf channel management unit, configured to create an Rf channel table and manage data transmission of the networking channel based on the Rf channel table; An Rf node management unit, used for creating an Rf node table and managing the state of each communication node through the Rf node table; An Rf networking management unit, configured to create an Rf forwarding table and manage level information of communication nodes based on the Rf forwarding table; The priority dynamic adjustment unit is used to create an Rf control table, through which the dynamic adjustment of data transmission priority is realized, and the priority management of communication nodes is optimized based on a composite mathematical model, an adaptive weight model, a dynamic expansion model, a time series progressive model and a Markov chain prediction model.

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