Distributed device data transmission remote management method and system based on FRPS technology

Abstract

The invention provides a distributed device data transmission remote management method and system based on an FRPS technology, and the method is used for solving the problems of instruction regulation and state synchronization after nodes are disconnected, and achieving multi-node cooperation and intelligent control through a multi-level node management mechanism. The node connection state is sensed in real time by using a self-organizing topology index, and invalid instruction sending during disconnection is avoided; overstocked instructions are screened based on the instruction information entropy index, only high-priority instructions are reserved, and instruction overload after recovery is prevented. Virtual state mapping is generated through disconnected shadow child nodes, state change and instruction response during disconnection are simulated, and seamless connection after recovery is ensured; the rhythm regulation and control mechanism dynamically adjusts the instruction sending rhythm according to the state difference, and load fluctuation caused by instruction burst is avoided. Therefore, intelligent judgment, key instruction reservation, state connection and rhythm control during node disconnection are realized, and continuity and management precision of multiple nodes in a complex environment are ensured.

Description

Technical Field

[0001] The present application belongs to the field of remote management of data transmission of distributed scientific devices, and specifically relates to a method and system for remote management of data transmission of distributed devices based on FRPS technology. Background Art

[0002] In the operation and management of distributed large scientific facilities, the monitoring and control of the facilities often involve multi-node bidirectional communication. These nodes may be distributed in different geographical locations and connected to the master control node through a remote network, thereby realizing centralized management of each device. In data transmission tasks, since distributed devices may generate a large amount of high-frequency, multi-type real-time data, such as environmental monitoring data, experimental measurement data, and operating status data, high requirements are placed on the transmission bandwidth, data integrity, and timeliness of the communication link. The application of FRPS (Fast Reverse Proxy Service) technology aims to provide intranet penetration and remote communication support for these multiple nodes, so that devices distributed in multiple locations can smoothly exchange information. However, FRPS focuses more on the proxy function of one-way traffic forwarding, lacks real-time status perception and management of multi-node bidirectional communication links, which makes it limited in such complex scenarios.

[0003] In multi-node remote monitoring, FRPS is not equipped with a status monitoring mechanism for two-way communication, resulting in the inability of the master control node to promptly perceive the node's interrupted status when the connection between a node and the master control node is interrupted due to network jitter or short-term disconnection. In this case, the management system will continue to send operating instructions to the disconnected node in a short period of time, but because the node cannot receive them, the instructions are backlogged on the master control end. When the network is restored, these backlogged instructions may "explode" into the node in a short period of time, causing the data transmission device to "lose control", especially in data transmission tasks with high requirements for real-time and integrity. It may cause sudden congestion or delays in data transmission, destroy the stability of the transmission link, cause data loss or repeated transmission, and even have an adverse effect on the validity and subsequent analysis of the data. Summary of the invention

[0004] The purpose of this application is to overcome the defect of the prior art that it lacks real-time status perception and management of multi-node bidirectional communication links.

[0005] In order to achieve the above objectives, the present application proposes a distributed device data transmission remote management method based on FRPS technology, comprising:

[0006] Step S1, establishing a topology perception mechanism, collecting topology changes and connection status of network sub-nodes in real time, generating a self-organizing topology index of the sub-node connection status, and determining whether there is a sub-node disconnection;

[0007] Step S2, when a child node disconnects, calculate the information entropy index of the instructions of the disconnected child node based on the instruction information entropy model, discard non-critical instructions according to the information entropy index, and retain high-priority instructions;

[0008] Step S3, establish a disconnected shadow child node for the disconnected child node, generate a virtual state mapping, and simulate the state changes and instruction response behaviors during the disconnection of the child node;

[0009] Step S4, when the child node reconnects, control the instruction sending rhythm according to the virtual state mapping and the rhythm regulation parameters to ensure the smooth connection of the child node's instruction reception.

[0010] As an improvement of the above method, step S1 includes:

[0011] Step S1.1, each child node transmits a child node status data packet to the master node at a set interval; the master node constructs a network topology graph using the connectivity and path optimization algorithms in graph theory; the network topology graph G, G = (V, E) represents the entire node network; where V is the set of child nodes, and E is the set of edges between child nodes, reflecting the connection status of child nodes;

[0012] Step S1.2, based on the constructed network topology graph, calculate the self-organizing topology index STI as an overall evaluation index of the network state:

[0013]

[0014] where, Clust i represents the clustering coefficient of child node i; W MST is the sum of the weights of the minimum spanning tree; Cen ij is the path centrality between child nodes i and j;

[0015] Step S1.3, if the self-organizing topology index drops below the set topology index threshold, the master node determines that there is a disconnected child node and stops sending instructions to the disconnected child node.

[0016] As an improvement of the above method, step S2 includes:

[0017] Step S2.1, extract the key attributes of each backlogged instruction; the key attributes include the importance weight I k , the urgency weight U k and the historical execution success rate weight H k ;

[0018] Step S2.2, calculate the instruction information entropy index of each instruction through the information entropy model:

[0019]

[0020] Among them, CIE k represents the information entropy index of the k-th instruction; α represents the non-linear adjustment index;

[0021] Step S2.3: When the instruction information entropy index value is lower than the set screening threshold, the master node discards the corresponding instruction and retains the instructions with the instruction information entropy index value higher than the set screening threshold to form a high-priority instruction set C high ;

[0022] Step S2.4: After each decision to discard or retain an instruction, adjust the importance weight, urgency weight, and historical execution success rate weight, and recalculate the instruction information entropy index.

[0023] As an improvement of the above method, the adjustment of the importance weight, urgency weight, and historical execution success rate weight includes:

[0024] If the execution success rate of an instruction is lower than the set execution success rate threshold, increase the importance weight;

[0025] If the instruction information entropy index is greater than the set information entropy index threshold, increase its corresponding adjustment weight by 5% - 15%;

[0026] If the load of the sub-node where the instruction is located is higher than the set load threshold, reduce the importance weight and historical execution success rate weight.

[0027] As an improvement of the above method, the step S3 includes:

[0028] Step S3.1: When detecting the disconnection of a sub-node, the master node creates a shadow sub-node for the disconnected sub-node; the initial state of the shadow sub-node is set to the last state of the sub-node before disconnection; the state includes: the device state parameter set S 0 、the task progress parameter set P 0 and the historical instruction set C history ;

[0029] Step S3.2: The shadow sub-node simulates the state changes during the disconnection of the disconnected sub-node, establishes a state change model according to the physical characteristics and operation rules of the disconnected sub-node, and predicts the key physical parameters;

[0030] Step S3.3: The shadow sub-node simulates the execution process of the instruction in the virtual state according to the high-priority instruction set C high sent by the master node, including:

[0031] For each instruction c i ∈C high perform parsing to determine its operation target and expected effect; define a state update function f for each instructioni (S), the update function f i (S) is used to describe the impact of the instruction on the device state;

[0032] Apply the state update function of the instruction to the current virtual state of the shadow child node to obtain the updated state S': S′ = f i (S); Check whether the updated state meets the safe operating range and physical constraint conditions of the device; if not, generate an exception warning; if the conditions are met, store the updated state as the new virtual state for the next instruction or state change model to use;

[0033] Step S3.4, the shadow child node continuously records the virtual state at each time point during the disconnection period to form a virtual state mapping VSM = {(t k , S k )}, where t k is the time node and S k is the corresponding virtual state.

[0034] As an improvement of the above method, step S3 further includes:

[0035] Step S3.5, when the disconnected child node reconnects, the master node obtains the actual state S real of the child node and compares it with the last virtual state S virtual of the shadow child node; Calculate the state difference ΔS:

[0036] ΔS = |S real - S virtual |

[0037] Judge whether the state difference is within the set tolerance range ∈; if |ΔS| ≤ ∈, it is considered that the virtual state is consistent with the actual state, and execute step S3.7; if |ΔS| > ∈, execute step S3.6;

[0038] Step S3.6, generate a state correction instruction according to the state difference, insert the state correction instruction into the corresponding position of the instruction sequence, send the adjusted instruction sequence to the child node, and monitor the instruction execution situation to ensure successful state synchronization;

[0039] Step S3.7, after the state synchronization is completed, update the virtual state of the shadow child node to the actual state of the child node, clear the virtual state mapping, set the shadow child node to enter the standby state, and wait for the next disconnection event; the master node continues to perform normal instruction interaction and state monitoring with the child node according to the new instruction sequence.

[0040] As an improvement of the above method, the inserting the state correction instruction into the corresponding position of the instruction sequence includes:

[0041] The state correction instruction is preferentially inserted before all subsequent instructions that need to depend on the corrected state, but it cannot affect the instructions that can be correctly executed under the actual state of the current child node;

[0042] If the urgency of the state correction is higher than the set urgency threshold, and the subsequent instructions depend on the corrected state, then the state correction instruction is inserted at the front end of the sequence;

[0043] If some instructions can be executed independently of the state correction state, then the state correction instruction is inserted after the independent instructions.

[0044] As an improvement of the above method, step S4 includes:

[0045] Step S4.1, according to the state difference ΔS and the current load level L of the child node current , determine the rhythm regulation parameter R rate :

[0046]

[0047] where represents the maximum load value allowed for the child node; κ represents the response sensitivity parameter.

[0048] Step S4.2, using the rhythm regulation parameter, the master node sends instructions rhythmically according to the following steps:

[0049] Determine batching: Divide the set of instructions to be sent into B batches, and the size of each batch B size is determined by the rhythm regulation parameter:

[0050]

[0051] where N is the total number of instructions to be sent, represents rounding up;

[0052] Interval sending: According to R rate calculate the interval time T for sending each batch interval :

[0053]

[0054] where T base is the reference sending interval;

[0055] Monitoring and sending: After each batch is sent, monitor the state update and load condition of the child node in real time; if the gap between the load of the child node and the maximum load value allowed for the child node is less than the set load threshold or the state difference increases, then reduce the rhythm regulation parameter and slow down the sending rhythm of the subsequent instructions;

[0056] Step S4.3: During the process of sending instruction batches, the master node continuously monitors the status changes of the slave nodes, gradually updates the virtual status mapping, and adjusts the rhythm control parameters in real time; when the final state of the virtual status mapping gradually approaches the actual state and the state difference is reduced below the set difference threshold, the instruction sending rhythm is gradually accelerated until the instruction execution resumes the normal transfer rhythm.

[0057] This application also provides a remote management system for distributed device data transmission based on the FRPS technology, which is implemented based on the above method. The system includes:

[0058] Topology awareness module: Used to establish a topology awareness mechanism, collect the topology changes and connection status of network slave nodes in real time, and generate a self-organizing topology index of the slave node connection status; transmit the slave node disconnection status information to the instruction screening module as the basis for subsequent instruction screening.

[0059] Instruction screening module: Used to calculate the information entropy index of the instructions of the disconnected slave nodes based on the instruction information entropy model, screen the backlogged instructions, discard non-critical instructions and retain high-priority instructions; transmit the set of screened high-priority instructions to the shadow slave node module for simulation preprocessing on the shadow slave nodes.

[0060] Shadow slave node module: Used to establish shadow slave nodes for the disconnected slave nodes and generate a virtual status mapping, simulate the status changes and instruction response behaviors during the disconnection period of the slave nodes; transmit the virtual status mapping and simulation execution results of the shadow slave nodes to the rhythm control module to achieve smooth connection of instructions after the slave nodes resume connection; and

[0061] Rhythm control module: Used to dynamically adjust the instruction sending rhythm according to the virtual status mapping and rhythm control parameters after the slave nodes reconnect, ensuring smooth connection of instruction reception; gradually synchronize the slave node status with the master node to complete the instruction transfer and achieve smooth transition and seamless connection of the system.

[0062] Compared with the prior art, the advantages of this application are:

[0063] 1. The present invention addresses the remote management requirements for data transmission of distributed devices (especially large scientific devices), and realizes efficient multi-node collaboration and intelligent control through a node management mechanism, ensuring the stable operation of the device in a complex network environment.

[0064] 2. By using the self-organizing topology index to sense the connectivity status of nodes in real time, the master node can quickly identify disconnection situations and stop sending invalid instructions, avoiding resource waste and network redundant load during disconnection from the source.

[0065] 3. In terms of instruction management, the priority screening mechanism of the instruction information entropy index effectively filters out low-priority instructions and only retains the critical instructions that are crucial for the operation of the device, solving the problems of instruction accumulation and overload that may occur after disconnection recovery.

[0066] 4. The disconnection shadow node maps and simulates the state changes and instruction responses during node disconnection in a virtual state, ensuring seamless connection after reconnection and enabling the master node to avoid the delay and uncertainty of state synchronization after the node reconnects.

[0067] 5. The rhythm regulation mechanism flexibly adjusts the instruction sending rhythm based on the difference between the virtual state and the actual state, enabling the node to smoothly connect to the instructions and avoiding load fluctuations caused by sudden instructions.

[0068] 6. Through real-time topology awareness, intelligent instruction screening, virtual state mapping, and rhythm regulation, the present invention realizes the intelligence and efficiency of disconnection handling in the remote management of distributed device data transmission, enables the multi-node system to have extremely high response agility and precise instruction regulation, and significantly improves the operation coherence and collaborative management ability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 The figure shows a schematic flow diagram of a method for remote management of distributed device data transmission based on the FRPS technology;

[0070] Figure 2 The figure shows a schematic structural diagram of a remote management system for distributed device data transmission based on the FRPS technology. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings.

[0072] The present application provides a remote management method and system for distributed device data transmission based on FRPS technology. By using the self-organizing topological index to real-time sense the connectivity status of nodes, the master node can quickly identify disconnection situations and stop sending invalid instructions, thus avoiding resource waste and network redundant load during disconnection from the source. In terms of instruction management, the priority screening mechanism of the instruction information entropy index effectively filters out low-priority instructions and only retains critical instructions that are crucial for the operation of the data transmission device, solving the problem of instruction accumulation and overload that may occur after disconnection recovery. The disconnection shadow node maps and simulates the state changes and instruction responses during the disconnection period of the node in a virtual state, ensuring seamless connection after reconnection, so that the master node does not need to experience the delay and uncertainty of state synchronization after the node reconnects. In addition, the rhythm regulation mechanism flexibly adjusts the instruction sending rhythm based on the difference between the virtual state and the actual state, enabling the node to smoothly connect to instructions and avoiding load fluctuations caused by sudden instructions. Overall, the present invention realizes the intelligence and efficiency of disconnection handling in the remote management of distributed device data transmission through real-time topology sensing, intelligent instruction screening, virtual state mapping, and rhythm regulation, enabling the multi-node system to have extremely high response agility and precise instruction regulation, significantly improving the operation coherence and collaborative management ability of the system to solve the problems raised in the above background technology.

[0073] Embodiment 1

[0074] As Figure 1 shown, a remote management method for distributed device data transmission based on FRPS technology provided by the present application includes:

[0075] S1. Establish a topology sensing mechanism to real-time collect the topology changes and connection status of network sub-nodes, and generate a self-organizing topological index of the sub-node connection status.

[0076] S2. When it is found that a certain sub-node is disconnected, calculate the information entropy index of the instructions of this sub-node based on the instruction information entropy model, and automatically discard non-critical instructions and retain high-priority instructions according to the information entropy index.

[0077] S3. Establish a disconnection shadow sub-node for the disconnected sub-node, generate a virtual state mapping, and simulate the state changes and instruction response behaviors during the disconnection period of this sub-node.

[0078] S4. When the disconnected sub-node reconnects, control the instruction sending rhythm according to the virtual state mapping and rhythm regulation parameters to ensure the smooth connection of the sub-node instruction reception.

[0079] In the remote monitoring and management of distributed device data transmission, it is particularly crucial to timely grasp the connection status and topological structure changes of each sub-node to maintain the coordination of the system and the accuracy of instructions. Since these devices are usually located in complex geographical environments, the connection is vulnerable to various factors, and it is difficult for the unidirectional flow forwarding technology to achieve efficient perception and autonomous adjustment of node status. The present invention designs a topological perception mechanism based on self-organizing topological indices. By constructing a dynamic network topology graph, it realizes precise monitoring of node status and real-time response to connection conditions, thereby timely adjusting the sending strategy of the master control instruction when a node is interrupted, and ensuring the orderliness and intelligent control of the coordinated operation of the devices.

[0080] Step S1 includes the following contents:

[0081] S1.1. First, deploy a topological perception mechanism between the master node and each sub-node. Each sub-node transmits a node status data packet to the master node at a set interval (such as every second), which includes the device identifier of the sub-node, the sub-node location code, the current network signal strength S i , the communication traffic size F i and the connection duration T i . These parameters together constitute the real-time network status of the sub-node. This status information will enter the topological perception module of the master node as the basic data for generating a dynamic network topology graph.

[0082] S1.2. At the master node, use the connectivity and path optimization algorithms in graph theory to construct a network topology graph. Define the network topology graph G to represent the entire node network, G = (V, E), where V is the set of sub-nodes and E is the set of edges between sub-nodes, reflecting the connection status of sub-nodes. The weight w ij of each edge in the graph is calculated by the following formula:

[0083]

[0084] where, S i and S j respectively represent the current network signal strengths of sub-nodes i and j, used to evaluate the connection stability of sub-nodes. F i and F j are the current communication traffic sizes of sub-nodes i and j, to reflect the network load difference. T i and T j respectively represent the connection durations of sub-nodes i and j, used to measure the connection stability and historical reliability.

[0085] Weight the network connections by the product of node signal strengths, and combine the non-linear adjustment of traffic differences and durations, so that the weights can accurately reflect the quality of network connections in real-time topological changes. In this way, when the signal is weak or the traffic difference is large, the edge weight will be significantly reduced, indicating the unreliability of the connection.

[0086] S1.3, Based on the constructed network topology graph, calculate the self-organizing topology index STI as an overall evaluation index of the network state. Let the sum of the weights of the minimum spanning tree of the network topology graph be W MST , and jointly construct the self-organizing topology index using the network clustering coefficient and path centrality. The formula is as follows:

[0087]

[0088] where Clust i represents the clustering coefficient of sub-node i, which measures the connectivity between this node and its surrounding nodes, that is, the ability of sub-nodes to form a sub-network. A higher clustering coefficient indicates that the connections between nodes are tight, which is beneficial to improving the overall robustness of the network. W MST is the sum of the weights of the minimum spanning tree, which reflects the cost of the optimal path connection. A lower sum of the weights of the minimum spanning tree indicates that the network connection is efficient and highly reliable. Cen ij is the path centrality between sub-nodes i and j, which is used to measure the importance of nodes in the network topology and helps to evaluate the impact of sub-node disconnection on the overall topology.

[0089] The calculation formula of the self-organizing topology index evaluates the real-time state of the network through three levels: the clustering coefficient, the sum of the weights of the minimum spanning tree, and the path centrality, reflecting the stability and connection quality of the overall topology.

[0090] The self-organizing topology index comprehensively evaluates the stability and connectivity of the network topology structure through three key parameters: the clustering coefficient, the sum of the weights of the minimum spanning tree, and the path centrality. Therefore, it can sensitively reflect the changes in the topology structure when a sub-node is disconnected, thus identifying the disconnection state of the sub-node.

[0091] Specifically, the construction of the self-organizing topology index depends on the following three aspects:

[0092] 1. Sudden drop in the clustering coefficient: In the topology structure, the clustering coefficient measures the local connectivity between nodes. The clustering coefficient of the disconnected sub-node will decrease significantly, and the clustering coefficients of its surrounding sub-nodes will also decline synchronously. This change in local connectivity is directly reflected in the self-organizing topology index, causing an obvious fluctuation in the value of the self-organizing topology index, thus capturing the offline state of the sub-node.

[0093] 2. Significant increase in the weight of the minimum spanning tree: The sum of the weights of the minimum spanning tree represents the cost of maintaining an optimal connection state in the network topology. When a child node disconnects, the connection paths need to be reallocated, resulting in an increase in the weight of the minimum spanning tree. This increase is amplified by logarithmic adjustment in the self-organizing topology index, enabling the self-organizing topology index to quickly identify and mark significant changes in the network topology.

[0094] 3. Abnormal fluctuations in path centrality: Path centrality is used to measure the importance of a node in the topology. Once a key node disconnects, its path centrality will decrease, and the centrality distribution of other child nodes will also be adjusted accordingly. The self-organizing topology index comprehensively considers the path centrality of all child nodes in the calculation and can quickly reflect the change in the importance of the entire network topology caused by the offline of a child node.

[0095] Therefore, through the fluctuations and sudden drops of the self-organizing topology index, the master node can monitor and judge the disconnection of child nodes in the network in real time, ensuring that the management system promptly adjusts the instruction sending strategy to adapt to dynamic topology changes.

[0096] S1.4, if the self-organizing topology index drops below a preset threshold, the master node determines that there is a disconnection of a child node and immediately stops sending instructions to the disconnected child node. By means of the topology awareness mechanism, the network topology and the preset threshold are adjusted in real time to ensure the flexible responsiveness of the network connection to cope with unforeseen topology changes in a multi-child node environment.

[0097] By using the self-organizing topology index to sense the disconnection state of nodes in real time and thus immediately stop sending instructions to the disconnected child node, it has significant advantages compared with the traditional method of stopping instruction sending after detecting disconnection. The traditional method relies on the network layer to feedback disconnection notifications, usually with a response delay. And in the case of high-frequency instruction sending, it may lead to the backlog of instructions during the disconnection period, wasting transmission resources and increasing the network load. However, the topology awareness mechanism implemented by the self-organizing topology index takes topology changes as the trigger point, can instantly and automatically identify abnormal connections of child nodes and react quickly. This not only reduces the sending of invalid instructions but also improves resource utilization efficiency and avoids the "instruction burst" phenomenon that may occur after recovery due to instruction backlog. In addition, the real-time response ability of the self-organizing topology index ensures the rapid adaptability of the system to the state of child nodes, making remote management more flexible and intelligent, especially suitable for the high requirements for instruction timeliness and resource optimization in the remote management and operation scenarios of data transmission of complex distributed scientific devices.

[0098] In the remote management of device data transmission distributed across multiple locations, the master node's keen perception and real-time response to topological changes directly affect the system's instruction execution accuracy and the coordination among multiple nodes. The present invention constructs a dynamic network topology map through a topology awareness mechanism, achieving a comprehensive analysis of the collaboration capabilities between child nodes, ensuring that when a child node disconnects, the master node can quickly identify and optimize the instruction transfer logic. Through the quantitative evaluation of the self-organizing topology index, this method can accurately capture the details of topological structure changes, providing higher response sensitivity and intelligent instruction adaptation, effectively supporting the multi-child node collaborative operation requirements of distributed device data transmission in complex environments.

[0099] In step S1, the master node senses the disconnection state of a certain child node through the self-organizing topology index, thereby stopping the direct instruction sending to the disconnected child node. However, when the child node resumes connection, the master node may have accumulated a certain number of instructions to be sent. To ensure the avoidance of an instruction backlog explosion when reconnecting, in step S2, based on the instruction entropy control mechanism, the priority and necessity of instructions are screened through the instruction information entropy index, thereby optimizing the instruction transfer, retaining the most critical instructions, discarding low-value instructions, and ensuring the orderly transfer of instructions.

[0100] Step S2 includes the following content:

[0101] S2.1, first, parameterize the attributes of the backlogged instructions, extract the key attributes of each instruction, including the importance weight I k 、urgency weight U k 、historical execution success rate weight H k , where:

[0102] I k reflects the importance of the instruction for the current task of the child node, and numerically values the importance of the instruction according to the task type and priority (for example, the device calibration instruction may have a higher weight than the data query instruction).

[0103] U k represents the urgency weight of the instruction, which is determined according to the time sensitivity of the current instruction. For example, instructions close to real-time monitoring have a higher priority.

[0104] H k represents the weight of the success rate of this instruction in past executions, reflecting the execution reliability of the instruction according to historical records (for example, instructions with a high past execution failure rate will reduce the weight).

[0105] S2.2. After obtaining the attributes of each instruction, calculate the Instruction Information Entropy Index (CIE) of each instruction through the information entropy model to determine its value and necessity in the backlog queue. The Instruction Information Entropy Index is used to quantify the priority and necessity of each instruction to be sent, so as to ensure that when the child node reconnects after disconnection, key instructions can be retained and low-value instructions can be automatically discarded. Its calculation process comprehensively considers factors such as the importance, urgency, and historical execution success rate of the instruction, and evaluates the effectiveness of the instruction with a multi-level model. Specifically, the information entropy index first takes the importance and urgency of the instruction as the main weights to enhance the significance of high-priority and time-sensitive instructions in the model; secondly, uses the historical execution success rate to inversely adjust the feasibility of the instruction to reduce duplicate instructions with low execution success rate after disconnection recovery; finally, through non-linear balance adjustment, dynamically allocate weights to adapt to the needs of different scenarios. The overall calculation idea is to retain instructions that have a significant impact on the system operation through the trade-off of entropy values, so as to improve the instruction execution efficiency and system response quality after recovery. The formula is as follows:

[0106]

[0107] Among them, CIE k represents the Instruction Information Entropy Index of instruction k, reflecting its priority and necessity in the queue. α represents the non-linear adjustment index, which is used to control the sensitivity to the success rate and enhance the priority protection of instructions with high importance and low success rate. log(1 + U k ) significantly improves higher-urgency instructions in the entropy value calculation through logarithmic adjustment of the urgency. Weakens the priority of instructions with high past success rates through reciprocal non-linear control to prevent repeated transmission of useless instructions.

[0108] S2.3. Compare the calculated Instruction Information Entropy Index value with the screening threshold. When the Instruction Information Entropy Index value is lower than the screening threshold, the master node automatically discards the corresponding instruction and retains the instructions with an Instruction Information Entropy Index value higher than the screening threshold. This screening mechanism ensures that high-priority instructions are retained while low-priority instructions are automatically discarded, effectively reducing the instantaneous instruction traffic after reconnecting.

[0109] S2.4. After each execution of the instruction discard or retention decision, the instruction information entropy index model is updated in real time, and the weight distributions of the importance weight, the urgency weight, and the historical execution success rate weight are adjusted. The master node recalculates the screening threshold based on the latest task priorities and network status information to ensure the accuracy and adaptability of the instruction screening model, and to ensure that the instruction backlog is optimized when the connection is restored in the future. Specifically, the weight distributions of the importance weight, the urgency weight, and the historical execution success rate weight should be adjusted according to the following principles: First, if the execution success rate of an instruction is low (i.e., the urgency weight is small), the importance weight should be appropriately increased to ensure the effective execution of high-priority instructions; if the entropy value of an instruction is high (i.e., the historical execution success rate weight is large), the corresponding adjustment weight should be increased by 5% - 15% to enhance its response ability in a complex network environment; if the load of the node where the instruction is located is high, the weights of the importance weight and the historical execution success rate weight need to be reduced to avoid overload and reduce the execution pressure of non-critical instructions. Finally, the adjustment of the weights is optimized progressively based on real-time monitoring results and historical data feedback to ensure a high degree of matching between the instruction decision and the actual operation status, and to avoid over-backlog or missed instruction discovery.

[0110] In the remote management of data transmission in a multi-node distributed device, ensuring the orderly flow of instructions after the disconnection of the slave nodes, avoiding the backlog of invalid instructions and ensuring the efficient execution of instructions after the connection is restored, is a key requirement for system operation. The present invention establishes an information entropy index model through an instruction entropy control mechanism to dynamically evaluate the priority and necessity of instructions to be sent, automatically discard low-value instructions, and retain core instructions. The information entropy index model comprehensively considers the importance, urgency, and historical success rate of instructions to achieve intelligent screening of backlogged instructions, avoid the "explosion" of instructions or resource waste after the disconnection is restored, ensure the efficiency and continuity of system instruction processing, make the instruction regulation of the master node intelligent and self-adaptive in a complex environment, and meet the precise management requirements of multi-slave node cooperation.

[0111] In the previous steps, the master node has sensed the disconnection state of the slave nodes and retained high-priority instructions through instruction information entropy index screening. To ensure that the instruction sequence can be seamlessly connected after the slave nodes are reconnected and avoid device anomalies caused by out-of-sync states, in this step, a corresponding disconnected shadow slave node is established for each disconnected slave node in the master node to maintain the virtual state mapping (VSM). The shadow slave node ensures the continuity of the system and the correctness of instruction execution by simulating the state changes and possible instruction response behaviors of the slave nodes during the disconnection period.

[0112] Step S3 includes the following content:

[0113] S3.1. When a disconnection of a child node is detected, the master node immediately creates a shadow child node for the corresponding child node. The initial state of the shadow child node is obtained from the latest state data of the child node before disconnection and includes the following information:

[0114] Device status parameter set S 0 : including key physical parameters such as temperature, voltage, pressure, position, etc.

[0115] Task progress parameter set P 0 : Records the currently executing tasks and their progress, such as task numbers, completed steps, etc.

[0116] Historical instruction set C history : Contains the most recently executed instructions and their execution results.

[0117] The "most recently" in the most recently executed instructions and their execution results refers to a specific time window, usually a duration dynamically determined according to the system response requirements. This time period is usually set as the instruction execution record within the past one cycle, and the cycle length can be determined according to the node load, network status fluctuation frequency, and real-time requirement of instruction response. For example, in a relatively stable network state, the cycle length may be the instruction set within the past 10 minutes, while in a scenario of frequent network fluctuations or node handovers, the cycle can be shortened to a few seconds or dozens of seconds to capture more precise state changes. The historical instruction set should include the detailed information of all executed instructions within this cycle, including the priority, execution result, system load, and network status at the time of execution of each instruction, and based on this, analyze and predict the trend of instruction execution and possible adjustment requirements.

[0118] Through the above parameters, the virtual state of the shadow child node can accurately reflect the actual state at the time of child node disconnection.

[0119] S3.2. The shadow child node needs to simulate the state changes during the disconnection period. According to the physical characteristics and operating rules of the device, establish a state change model to predict the key physical parameters. Taking the temperature parameter as an example, the state change model can be expressed as:

[0120]

[0121] where, T(t) represents the temperature at time t. T 0 represents the initial temperature, that is, the temperature at the time of disconnection. P in (t′) represents the input power at time t', which is derived from the operation of the device or environmental impact. P out (t′) represents the heat dissipation power at time t′, which depends on the heat dissipation performance of the device and environmental conditions. C th represents the heat capacity of the device, indicating the response degree of temperature change to the absorption or release of energy.

[0122] For other physical parameters, such as pressure, position, etc., similar differential or integral equations can be established according to the corresponding physical laws for prediction.

[0123] S3.3. The shadow node, according to the high-priority instruction set C sent by the master node high , simulates the execution process of the instructions in the virtual state. The specific steps are as follows:

[0124] For each instruction c i ∈C high perform parsing to determine its operation target and expected effect.

[0125] Define a state update function f i (S) for each instruction to describe the impact of the instruction on the device state. For example, for the instruction to adjust the voltage V: V(t + Δt) = V(t) + ΔV i ; where ΔV i is the voltage change amount required by instruction c i .

[0126] Apply the state update function of the instruction to the current virtual state of the shadow sub-node to obtain the updated state S': S′ = f i (S); check whether the updated state meets the safe operating range and physical constraint conditions of the device. If not, generate an exception warning and record it in the log of the shadow sub-node.

[0127] Store the updated state as the new virtual state for use by the next instruction or state change model.

[0128] S3.4. The shadow sub-node continuously records the virtual state at each time point during the disconnection period to form a virtual state mapping VSM = {(t k , S k )}, where t k is the time node and S k is the corresponding virtual state. The virtual state mapping includes:

[0129] The time node sequence t k : Records the time points of each state update.

[0130] The virtual state sequence S k : The virtual state corresponding to the time point.

[0131] By maintaining the virtual state mapping, the shadow node can completely describe the state evolution process during the disconnection period.

[0132] S3.5. When the sub-node reconnects, the master node obtains the actual state S of the sub-node real, and compare it with the last virtual state S of the shadow child node virtual for comparison.

[0133] State difference calculation: ΔS = |S real - S virtual |; Determine whether the state difference is within the allowable tolerance range ∈.

[0134] If |ΔS| ≤ ∈, it is considered that the virtual state is consistent with the actual state, and the subsequent instructions can be directly continued to be executed.

[0135] If |ΔS| > ∈, state correction is required.

[0136] S3.6, for the case where the state difference exceeds the tolerance range, take the following measures:

[0137] Generate state correction instructions: According to the state difference, generate a series of state correction instructions to guide the child node to adjust the actual state to the expected state.

[0138] Adjust the instruction sequence: Re-plan the instruction sequence to be executed, insert the state correction instructions into the corresponding positions of the instruction sequence, and ensure that the subsequent instructions are executed in the correct state.

[0139] Among them, the corresponding position of the instruction sequence refers to the position where the state correction instruction is inserted in the instruction sequence. This position needs to be determined comprehensively according to the urgency of state correction, the degree of dependence of subsequent instructions on the corrected state, and the logic of the execution order. Specifically, the state correction instruction should be preferentially inserted before all subsequent instructions that need to depend on the corrected state, but it cannot affect the instructions that can be correctly executed in the actual state of the current node. If the urgency of state correction is high and most of the subsequent instructions depend on the corrected state, the correction instruction should be inserted at the front of the sequence to ensure that the system enters the correct state first. For the case where some instructions are executed independently of the corrected state, the state correction instruction can be inserted after the independent instructions to ensure the logic and efficiency of the overall sequence execution. This insertion process needs to combine the instruction dependency graph and the time constraints of task execution, and achieve position optimization through a dynamic scheduling algorithm.

[0140] Synchronous execution: Send the state correction instructions and the adjusted instruction sequence to the child node, monitor the instruction execution situation, and ensure the success of state synchronization.

[0141] The state correction instruction and the adjusted instruction sequence are designed to address the problem of state deviation during the disconnection of the target node, and there is no contradiction or duplication in their execution. The role of the state correction instruction is to quickly adjust the current state of the target node to a reference state that is mapped consistently with the virtual state of the master node, ensuring that subsequent instructions can continue to be executed in the correct state. In the adjusted instruction sequence, only the set of simplified instructions that accumulated during the disconnection and are still meaningful for task completion is retained. These instructions are the result of re-planning and screening based on the corrected state. Since the accumulated instructions have been screened and optimized, low-priority instructions that are repeated or have been simulated and executed in the virtual state have been removed. Therefore, the adjusted instruction sequence will not cause the target node to repeatedly execute the content already included in the state correction. The cooperation of the two ensures state consistency and the integrity of task execution, avoiding both instruction redundancy and logical conflicts in execution.

[0142] S3.7, after the state synchronization is completed, the virtual state of the shadow child node is updated to the actual state of the child node, the virtual state mapping is cleared, and the shadow child node enters the standby state, waiting for the next disconnection event. The master node continues to perform normal instruction interaction and status monitoring with the child node according to the new instruction sequence.

[0143] In the remote management of distributed device data transmission, the disconnection of the child node may lead to the interruption of instruction flow and the deviation of device status. Especially in a high-precision experimental environment, the state desynchronization will affect the coherence of the device data transmission operation and the integrity of the data. The present invention realizes the dynamic simulation of the state change during the disconnection of the child node and the preprocessing of instructions by establishing a disconnection shadow child node at the master node and creating a virtual state mapping. The shadow child node executes high-priority instructions in the virtual state and records the simulated state changes, enabling the master node to quickly restore the system state after the child node reconnects. Through the seamless connection between the virtual state mapping and the actual state of the shadow child node, the system realizes the continuity and synchronization of the instruction flow, effectively improving the management intelligence and control accuracy in the scenario of node disconnection, and ensuring the coordinated operation of the remote management of device data transmission in a complex environment.

[0144] In the previous steps, the master node has detected the disconnection state of the child node through the self-organizing topology index, screened out the priority instructions, established a virtual state mapping on the disconnection shadow child node, and preprocessed and simulated the execution of the instructions. This step continues these results. When the child node reconnects, to ensure the smooth reception and execution of the instructions, based on the difference between the virtual state mapping and the actual state, the rhythm of instruction sending is controlled to avoid problems such as load overload and state fluctuations caused by sudden instructions. The rhythm regulation mechanism ensures the gradual connection between the master instructions and the child node state through dynamic rhythm allocation.

[0145] Step S4 includes the following content:

[0146] S4.1, based on the state difference ΔS and the current load level L of the child node current , determine the rhythm control parameter R rate , the rhythm control parameter controls the interval and batch size of command sending to ensure that the node receives the command smoothly. The calculation formula of the rhythm control parameter is:

[0147]

[0148] Among them, R rate Indicates the rhythm control parameter, which is used to control the rhythm of command sending. The smaller the value, the slower the rhythm of command sending. ΔS indicates the difference between the actual state of the child node and the virtual state of the shadow node. A large difference will reduce the command sending rate. L current Indicates the current load level of the child node, such as CPU usage, memory usage, etc. max It represents the maximum load value allowed by the child node. κ represents the response sensitivity parameter, which reflects the response strength of the system to the load level.

[0149] The state difference and current load level are controlled through nonlinear functions so that the instruction sending rhythm can dynamically adapt to the actual conditions of the child nodes.

[0150] S4.2, using the rhythm control parameters, the master control node sends instructions rhythmically according to the following steps to ensure smooth connection:

[0151] Determine by batch: Group the set of instructions to be sent into batches B, each batch size B size Regulated by the rhythmic control parameters, namely:

[0152]

[0153] Among them, N is the total number of instructions to be sent, B size Indicates the number of instructions per batch, Indicates rounding up.

[0154] Interval sending: Set the interval time T for each batch of sending interval , according to R rate The calculation results are:

[0155]

[0156] Among them, T base It is the benchmark sending interval, which is used to control the basic rhythm interval to ensure a reasonable instruction sending interval under different load and state difference conditions.

[0157] Transmission monitoring: After each batch is sent, the status updates and load conditions of the child nodes are monitored in real time. If the load of a child node approaches the maximum load value allowed for the child node or the status difference increases, the rhythm regulation parameter is decreased to slow down the sending rhythm of subsequent instructions.

[0158] S4.3. During the process of sending instruction batches, the master node continuously monitors the status changes of the child nodes, gradually updates the virtual status mapping, and adjusts the rhythm regulation parameter in real time. When the final state of the virtual status mapping gradually approaches the actual state and the status difference decreases below the set threshold, the instruction sending rhythm is gradually accelerated until the instruction execution resumes the normal flow rhythm. Ensure the flexible control of the instruction sending rhythm by the master node, so that the instruction execution is synchronized with the node status.

[0159] When the child node reconnects after disconnection, the smooth connection of instructions is crucial. Instructions that come in too quickly or instantaneously may cause the device load to overload or the status to fluctuate. The present invention realizes the precise control of the dynamic instruction sending rhythm through the rhythm regulation mechanism, and flexibly adjusts the batch size and sending interval of instructions according to the virtual status mapping of the shadow child node and the status difference of the actual child node. This mechanism gradually speeds up or slows down the instruction sending rhythm according to the current load condition of the child node, ensuring the effective diversion of instructions during the process of gradual synchronization of the status. Through the rhythmic instruction sending strategy, the system effectively avoids the adverse effects brought by instruction impact during recovery, and ensures the continuity of instruction execution and the smooth operation of device data transmission in the remote management environment.

[0160] Embodiment 2

[0161] As Figure 2 shown, the distributed device data transmission remote management system based on the FRPS technology provided by the present application includes: a topology awareness module, an instruction screening module, a shadow child node module, and a rhythm regulation module.

[0162] Topology awareness module: Establish a topology awareness mechanism, collect the topology changes and connection status of network nodes in real time, and generate a self-organizing topology index of the child node connection status; transmit the child node disconnection status information to the instruction screening module as the basis for subsequent instruction screening.

[0163] Instruction screening module: Calculate the information entropy index based on the instruction information entropy model, screen the backlogged instructions, automatically discard non-critical instructions and retain high-priority instructions; transmit the set of screened high-priority instructions to the shadow child node module for simulation preprocessing on the shadow child node.

[0164] Shadow child node module: Establish shadow child nodes for each disconnected child node and generate virtual state mappings to simulate the state changes and instruction response behaviors during the disconnection of the child nodes; transfer the virtual state mappings and simulated execution results of the shadow child nodes to the rhythm regulation module to achieve smooth connection of instructions after the child nodes resume connection.

[0165] Rhythm regulation module: After the child nodes reconnect, dynamically adjust the instruction sending rhythm according to the virtual state mappings and rhythm regulation parameters to ensure smooth connection of received instructions; gradually synchronize the child node states with the master node to complete the instruction flow and achieve smooth transition and seamless connection of the system.

[0166] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them. Although the present application has been described in detail with reference to the embodiments, those of ordinary skill in the art should understand that any modification or equivalent replacement of the technical solutions of the present application does not depart from the spirit and scope of the technical solutions of the present application, and they should all be covered within the scope of the claims of the present application.

Claims

1. A distributed device data transmission remote management method based on FRPS technology, comprising: Step S1, establishing a topology perception mechanism, collecting topology changes and connection status of network sub-nodes in real time, generating a self-organizing topology index of the sub-node connection status, and determining whether there is a sub-node disconnection; Step S2, when a child node is disconnected, the information entropy index of the instruction of the disconnected child node is calculated based on the instruction information entropy model, non-critical instructions are discarded according to the information entropy index, and high-priority instructions are retained; Step S3, establishing a disconnected shadow child node for the disconnected child node, generating a virtual state mapping, and simulating the state change and instruction response behavior of the child node during the disconnection period; Step S4, when the child node is reconnected, the instruction sending rhythm is controlled according to the virtual state mapping and rhythm control parameters to ensure smooth connection of the child node instruction reception.

2. The distributed device data transmission remote management method based on FRPS technology according to claim 1 is characterized in that: The step S1 comprises: Step S1.1, each child node transmits a child node status data packet to the master node at a set interval; the master node constructs a network topology graph using the connectivity and path optimization algorithm in graph theory; the network topology graph G, G = (V, E) represents the entire node network; where V is the child node set, and E is the edge set between the child nodes, reflecting the child node connection status; Step S1.2, based on the constructed network topology graph, calculate the self-organizing topology index STI as the overall evaluation index of the network status: Among them, Clust i represents the clustering coefficient of child node i; W MST is the minimum spanning tree weight sum; Cen ij is the path centrality of child nodes i and j; Step S1.3: If the self-organizing topology index drops below the set topology index threshold, the master control node determines that a child node is disconnected and stops sending instructions to the disconnected child node.

3. The distributed device data transmission remote management method based on FRPS technology according to claim 2 is characterized in that: The step S2 comprises: Step S2.1, extracting the key attributes of each backlog instruction; the key attributes include importance weight I k , Urgency weight U k and the historical execution success rate weight H k ; Step S2.2, calculate the instruction information entropy index of each instruction through the information entropy model: Among them, CIE k represents the information entropy index of the kth instruction; α represents the nonlinear adjustment index; Step S2.3: When the instruction information entropy index value is lower than the set screening threshold, the master control node discards the corresponding instruction and retains the instructions whose instruction information entropy index value is higher than the set screening threshold to form a high priority instruction set C. high ; Step S2.4, after each instruction discard or retain decision is made, the importance weight, urgency weight and historical execution success rate weight are adjusted, and the instruction information entropy index is recalculated.

4. The distributed device data transmission remote management method based on FRPS technology according to claim 3 is characterized in that: The adjustment of importance weight, urgency weight and historical execution success rate weight includes: If the execution success rate of the instruction is lower than the set execution success rate threshold, the importance weight is increased; If the instruction information entropy index is greater than the set information entropy index threshold, the corresponding adjustment weight is increased by 5% to 15%; If the load of the subnode where the instruction is located is higher than the set load threshold, the importance weight and the historical execution success rate weight are reduced.

5. The distributed device data transmission remote management method based on FRPS technology according to claim 3 is characterized in that: The step S3 comprises: Step S3.1, when a child node is detected to be disconnected, the master node creates a shadow child node for the disconnected child node; the initial state of the shadow child node is set to the last state of the child node before the disconnection; the state includes: device state parameter set S0, task progress parameter set P0 and historical instruction set C history ; Step S3.2, the shadow subnode simulates the state change of the disconnected subnode during the disconnection period, establishes a state change model according to the physical characteristics and operation rules of the disconnected subnode, and predicts the key physical parameters; Step S3.3: The shadow node receives a high priority instruction set C from the master node. high , simulating the execution process of instructions in a virtual state, including: For each instruction c i ∈C high Analyze to determine its operation objectives and expected effects; define a state update function f for each instruction i (S), the update function f i (S) is used to describe the effect of the instruction on the device status; Apply the state update function of the instruction to the current virtual state of the shadow child node to obtain the updated state S': S' = f i (S); check whether the updated state meets the safe operation range and physical constraints of the device; if not, generate an abnormal warning; if the conditions are met, store the updated state as a new virtual state for use by the next instruction or state change model; Step S3.4: The shadow child node continuously records the virtual state at each time point during the disconnection period to form a virtual state map VSM={(t k ,S k )}, where t k is the time node, S k is the corresponding virtual state.

6. The distributed device data transmission remote management method based on FRPS technology according to claim 5 is characterized in that: The step S3 further comprises: Step S3.5: When the disconnected child node is reconnected, the master node obtains the actual status S of the child node. real , and the last virtual state S of the shadow child node virtual Compare; calculate the state difference ΔS: ΔS=|S real -S virtual | Determine whether the state difference is within the set tolerance range ∈; if |ΔS|≤∈, the virtual state is considered to be consistent with the actual state, and step S3.7 is executed; if |ΔS|>∈, step S3.6 is executed; Step S3.6, generating a state correction instruction according to the state difference, inserting the state correction instruction into the corresponding position of the instruction sequence, sending the adjusted instruction sequence to the child node, monitoring the execution of the instruction, and ensuring successful state synchronization; Step S3.7, after the state synchronization is completed, the virtual state of the shadow child node is updated to the actual state of the child node, the virtual state mapping is cleared, and the shadow child node is set to enter the standby state, waiting for the next disconnection event; the master control node continues to perform normal command interaction and status monitoring with the child node according to the new command sequence.

7. The distributed device data transmission remote management method based on FRPS technology according to claim 6 is characterized in that: The inserting of the state correction instruction into the corresponding position of the instruction sequence includes: The state correction instruction is inserted before all subsequent instructions that need to rely on the correction state, but it cannot affect the instructions that can be correctly executed under the actual state of the current child node; If the urgency of state correction is higher than the set urgency threshold, and the subsequent instructions depend on the corrected state, the state correction instruction is inserted at the front of the sequence; If some instructions can be executed independently of the state correction state, the state correction instructions are inserted after the independent instructions.

8. The distributed device data transmission remote management method based on FRPS technology according to claim 6 is characterized in that: The step S4 comprises: Step S4.1: Based on the state difference ΔS and the current load level L of the child node current , determine the rhythm control parameter R rate : Among them, L max represents the maximum load value allowed by the child node; k represents the response sensitivity parameter. Step S4.2, using the rhythm control parameters, the master control node sends instructions rhythmically according to the following steps: Determine batching: Divide the set of instructions to be sent into B batches, each batch size is B size Determined by rhythm control parameters: Where N is the total number of instructions to be sent, Indicates rounding up; Interval sending: According to R rate Calculate the interval time T for each batch of sending interval : Among them, T base is the benchmark sending interval; Monitoring and sending: After each batch of sending is completed, the status update and load of the sub-nodes are monitored in real time; if the difference between the sub-node load and the maximum load value allowed by the sub-node is less than the set load threshold or the status difference increases, the rhythm control parameters are reduced to slow down the sending rhythm of subsequent instructions; Step S4.3, during the instruction batch sending process, the master control node continuously monitors the state changes of the child nodes, gradually updates the virtual state mapping and adjusts the rhythm control parameters in real time; when the final state of the virtual state mapping gradually approaches the actual state and the state difference is reduced to below the set difference threshold, the instruction sending rhythm is gradually accelerated until the instruction execution resumes the normal flow rhythm.

9. A remote management system for distributed device data transmission based on FRPS technology, implemented based on any method described in claims 1-8, characterized in that: The system comprises: Topology perception module: used to establish a topology perception mechanism, collect the topology changes and connection status of network sub-nodes in real time, generate a self-organizing topology index of the sub-node connection status; pass the sub-node disconnection status information to the instruction screening module as the basis for subsequent instruction screening; Instruction screening module: used to calculate the information entropy index of the instructions of the disconnected sub-node based on the instruction information entropy model, screen the backlog instructions, discard non-critical instructions and retain high-priority instructions; pass the screened high-priority instruction set to the shadow sub-node module for simulation preprocessing on the shadow sub-node; Shadow subnode module: used to establish shadow subnodes for disconnected subnodes and generate virtual state mappings to simulate state changes and instruction response behaviors of subnodes during disconnection; pass the virtual state mappings and simulation execution results of shadow subnodes to the rhythm control module to achieve smooth connection of instructions after the subnodes are restored; and Rhythm control module: It is used to dynamically adjust the rhythm of command sending according to the virtual state mapping and rhythm control parameters after the sub-node is reconnected to ensure the smooth connection of command reception; gradually synchronize the sub-node state with the main control node to complete the command flow and achieve smooth transition and seamless connection of the system.

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