A control system for a portable rescue communication terminal

Abstract

The present invention relates to the field of wireless communication technology, and specifically to a control system for a portable rescue communication terminal. The system includes: an environmental impact analysis module that collects environmental parameters in a rescue scene, as well as interference factors such as multipath effects and signal attenuation, dynamically analyzes the parameters, and calculates the impact of the parameters on signal transmission, synthesizes the calculation results into an environmental impact index, and obtains rescue data analysis results. In the present invention, by dynamically analyzing environmental parameters in a rescue scene and their impact on signal transmission, the reliability and stability of rescue communications can be effectively enhanced. Combined with historical environmental data, communication delay predictions are made more accurate, thereby reducing the risk of communication failures due to delays. At the same time, monitoring the operating status of the entire communication network and analyzing abnormal patterns in data streams in combination with real-time information can help identify and respond to potential faults in advance and ensure the continuity and effectiveness of rescue operations.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to a control system of a portable rescue communication terminal. Background Art

[0002] The field of wireless communication technology involves the transmission of data and information in a wireless environment. The field mainly includes different wireless communication standards and protocols, such as cellular communication networks, satellite communications, wireless local area networks, and short-range communication technologies such as Bluetooth and NFC. Wireless communication technology enables devices to communicate without physical connection, greatly increasing the flexibility and mobility of communication. With the advancement of technology, the speed, security and reliability of wireless communication have been significantly improved, and it is widely used in various civil and commercial occasions, including mobile communications, the Internet of Things, remote monitoring, and emergency rescue operations.

[0003] Among them, the control system of the rescue communication terminal is a communication equipment control system designed for rescue missions, which is used to manage and optimize communication activities during the rescue process. The system includes hardware equipment and software interfaces to ensure effective communication between rescuers when a disaster occurs. Its main purpose is to provide stable and reliable communication support in emergency situations, such as natural disasters, accident sites, or other occasions that require a quick response. Through this system, rescue teams can exchange information in real time and coordinate rescue operations.

[0004] When dealing with emergency rescue, existing technologies have difficulty in real-time analyzing and predicting the impact of the environment on signal transmission, which can easily lead to communication interruptions in extreme situations such as natural disasters, thus affecting the rescue effect and the safety of rescuers. At the same time, it is difficult to diagnose and handle sudden communication failures in a timely manner, making it inconvenient to effectively coordinate rescue resources at critical moments. In addition, when the network status or external environment changes, the data transmission path is difficult to adjust in real time, resulting in low data transmission efficiency and delayed rescue opportunities. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a control system for a portable rescue communication terminal.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A control system of a portable rescue communication terminal includes:

[0007] The environmental impact analysis module collects environmental parameters in the rescue scene, as well as interference factors such as multipath effects and signal attenuation, dynamically analyzes the parameters, and calculates the impact of the parameters on signal transmission. The calculation results are synthesized into an environmental impact index to obtain the rescue data analysis results.

[0008] The communication delay prediction module uses historical environmental data to set initial parameters, initializes the delay differential equation model, adds real-time information of the rescue data analysis results to dynamically adjust the parameters, predicts future communication delays, and generates real-time delay prediction results;

[0009] The fault diagnosis and response module monitors the operation status and traffic conditions of the entire communication network, analyzes abnormal patterns and sudden changes in delays in the data stream with reference to the real-time delay prediction results and the current communication network activity, calculates the future failure probability of the communication network nodes through the Markov decision process, and obtains the communication network failure analysis results;

[0010] The dynamic routing management module calculates and adjusts the communication network path in real time according to the communication network fault analysis result, optimizes the transmission route of the data packet, matches the fault status and the changing traffic demand, and obtains the optimized routing configuration.

[0011] As a further solution of the present invention, the step of obtaining the influence of the calculation parameters on signal transmission is specifically:

[0012] Collect environmental parameters in the rescue scene and use the formula:

[0013]

[0014] Calculates the weighted average of the parameters ;

[0015] in, It is The measured environmental parameter value, is corresponding to The weight of the multipath effect is quantitatively determined through historical data analysis and the characteristics of the current rescue scene. It is The signal attenuation interference parameter value measured for this time, is corresponding to The signal attenuation weight is adjusted according to the electromagnetic interference intensity in the rescue environment. is the total number of measurements;

[0016] Based on the weighted average of the parameters , using the formula:

[0017]

[0018] Calculate the overall impact of multipath effects and signal attenuation parameters on signal transmission ;

[0019] in, is the reference environmental parameter value, determined according to the preset environmental standard of the rescue communication terminal. is the adjustment coefficient, which is used to adjust the sensitivity of environmental parameters to signal transmission. It is obtained based on past rescue operation experience and experimental data. is the tuning parameter, set based on minimizing the error, is the natural logarithm function, used to convert and The linear relationship of the ratio becomes nonlinear.

[0020] As a further solution of the present invention, the steps of obtaining the rescue data analysis results are specifically as follows:

[0021] According to the overall impact of the signal transmission , using the formula:

[0022]

[0023] Calculating the Environmental Impact Index ;

[0024] in, is the preset minimum impact threshold, which is set according to the minimum interference in the rescue scenario. is the preset maximum impact threshold, which is set according to the maximum interference occurring in the rescue scenario;

[0025] According to the environmental impact index , using the formula:

[0026]

[0027] Calculate the percentage intensity of the impact of the environment on the rescue operation , and obtain the rescue data analysis results.

[0028] As a further solution of the present invention, the step of obtaining the initialized delay differential equation model is specifically as follows:

[0029] Extract key information from historical environmental data using the formula:

[0030]

[0031] Calculate the weighted average based on historical data to generate initial parameters ;

[0032] in, It is The weight of each data point is obtained through historical performance and correlation analysis. It is The measured values ​​of environmental parameters for data points, is the total number of historical data points;

[0033] According to the initial parameters , using the formula:

[0034]

[0035] Calculate in time Predicted communication delay ;

[0036] in, is the attenuation coefficient, which is obtained by dynamic adjustment based on the trend of historical data changes and rescue scenarios. is the time variable, is a constant used to realize the exponential decay characteristic of delay over time. is an exponential function that describes the decay behavior of the delay over time. Represents a negative value of the exponent.

[0037] As a further solution of the present invention, the step of obtaining the real-time delay prediction result is specifically:

[0038] Percentage intensity of the impact of the environment on the rescue operation , using the formula:

[0039]

[0040] Adjust time Predicted communication delay , and get the adjusted predicted communication delay ;

[0041] in, is the adjustment factor used to quantify right The extent of the impact is based on data analysis and simulations of past rescue missions;

[0042] According to the adjusted predicted communication delay , using the formula:

[0043]

[0044] Calculate the communication delay at the target time , get the real-time delay prediction result;

[0045] in, is the attenuation coefficient of environmental changes, which is dynamically adjusted according to real-time environmental monitoring data and obtained through historical data and environmental sensitivity analysis. is an exponential decay function, which is used to simulate the impact of environmental changes on latency. Represents a negative exponent.

[0046] As a further solution of the present invention, the step of obtaining the abnormal pattern and sudden change of delay in the analysis data stream is specifically:

[0047] Communication delay according to the target time and the current time of real-time communication network activity , using the formula:

[0048]

[0049] Evaluate the real-time impact of latency on communication network activity and obtain the adjusted communication network activity status ;

[0050] in, is the adjustment coefficient, which reflects the degree of influence of delay on the communication network status, obtained through the analysis of historical delay data and communication network sensitivity test;

[0051] According to the adjusted communication network activity state , using the formula:

[0052]

[0053] Quantify the abnormal behavior of the communication network status and calculate the anomaly detection index of the communication network ;

[0054] in, Yes means The time derivative of , that is, the speed at which the communication network state changes over time, is the weight coefficient of anomaly detection, which is used to adjust the sensitivity of abnormal behavior based on historical data of communication network performance and pre-defined thresholds. is the average value of the communication network status obtained by monitoring, used for standardization The current reading of

[0055] As a further solution of the present invention, the step of obtaining the communication network fault analysis result is specifically:

[0056] According to the anomaly detection index of the communication network , using the formula:

[0057]

[0058] Calculate the communication network node from the state To status The transition probability ;

[0059] in, is the attenuation coefficient, which adjusts the sensitivity of the transition probability based on historical data and node characteristics. is the state index in the Markov model, is an exponential decay function, which is used to calculate the state transition probability. is the total number of states in the Markov model, including all conditions the communication network is in;

[0060] According to the communication network node from the state To status The transition probability , using the formula:

[0061]

[0062] Calculate the failure probability of communication network nodes in the future state ;

[0063] in, Is the node in state The future failure probability, It indicates the status No direct transfer to state The probability of It is to calculate the total probability of at least one fault state transition.

[0064] As a further solution of the present invention, the step of obtaining the optimized routing configuration is specifically:

[0065] According to the failure probability of the communication network node in the future state , using the formula:

[0066]

[0067] Calculate the routing adjustment demand index of communication network nodes ;

[0068] in, is a conversion factor used to convert the probability of failure into routing adjustment requirements based on the average response time of the network and the service level agreement parameter settings. is an exponential function used to smooth the impact of failure probability;

[0069] Adjusting the demand index according to the routing of the communication network node , using the formula:

[0070]

[0071] Evaluate the current node To Node Optimized transmission path ;

[0072] in, Is the current node To Node The original network traffic, It is a regulation factor, which is used to adjust the traffic weight according to the routing demand index and the performance index and traffic distribution of the network;

[0073] According to the current node To Node Optimized transmission path , using the formula:

[0074]

[0075] Summarize all path weights to get the optimized routing configuration .

[0076] Compared with the prior art, the advantages and positive effects of the present invention are:

[0077] In the present invention, by dynamically analyzing the environmental parameters in the rescue scene and their impact on signal transmission, the reliability and stability of rescue communications can be effectively enhanced. Combined with historical environmental data, communication delay predictions are made more accurate, thereby reducing the risk of communication failures caused by delays. At the same time, monitoring the operating status of the entire communication network and analyzing abnormal patterns in the data stream in combination with real-time information can help identify and respond to potential failures in advance and ensure the continuity and effectiveness of rescue operations. By calculating and adjusting the communication network path in real time and optimizing the transmission route of the data packet, it can be flexibly adjusted according to actual needs to adapt to the changing situations that may occur in the rescue scene, thereby improving rescue efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 is a system flow chart of the present invention;

[0079] Figure 2 A flow chart of the steps for obtaining the influence of the calculation parameters on signal transmission according to the present invention;

[0080] Figure 3 A flowchart of the steps for obtaining rescue data analysis results of the present invention;

[0081] Figure 4 A flowchart of the steps for initializing the delay differential equation model of the present invention;

[0082] Figure 5 A flowchart of the steps for obtaining the real-time delay prediction result of the present invention;

[0083] Figure 6A flowchart of the acquisition steps for analyzing abnormal patterns and sudden changes in delays in a data stream according to the present invention;

[0084] Figure 7 A flowchart of the steps for obtaining the communication network fault analysis results of the present invention;

[0085] Figure 8 This is a flow chart of the steps for obtaining the optimized routing configuration of the present invention. DETAILED DESCRIPTION

[0086] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0087] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0088] See also Figure 1 , a control system of a portable rescue communication terminal includes:

[0089] The environmental impact analysis module collects environmental parameters in the rescue scene, as well as interference factors such as multipath effects and signal attenuation, dynamically analyzes the parameters, and calculates the impact of the parameters on signal transmission. The calculation results are synthesized into an environmental impact index to obtain the rescue data analysis results.

[0090] The communication delay prediction module uses historical environmental data to set initial parameters, initializes the delay differential equation model, adds real-time information of rescue data analysis results to dynamically adjust parameters, predicts future communication delays, and generates real-time delay prediction results;

[0091] The fault diagnosis and response module monitors the operation status and traffic of the entire communication network, analyzes abnormal patterns and sudden changes in delays in the data stream with reference to the real-time delay prediction results and the current communication network activity, calculates the future failure probability of the communication network nodes through the Markov decision process, and obtains the communication network fault analysis results;

[0092] The dynamic routing management module calculates and adjusts the communication network path in real time according to the communication network fault analysis results, optimizes the data packet transmission route, matches the fault status and the changing traffic demand, and obtains the optimized routing configuration;

[0093] The rescue data analysis results include signal transmission attenuation indicators, multipath effect impact indicators and environmental noise interference levels. The real-time delay prediction results include the expected shortest delay, the expected longest delay and the delay fluctuation range. The communication network fault analysis results include risk fault node identification, potential fault cause analysis and fault response priority sorting. The optimized routing configuration includes selected backup routes, active obstacle avoidance routes and traffic redistribution plans.

[0094] See also Figure 2 , the steps for obtaining the influence of the calculation parameters on signal transmission are as follows:

[0095] Collect environmental parameters in the rescue scene and use the formula:

[0096]

[0097] Calculates the weighted average of the parameters ;

[0098] in, It is The measured environmental parameter value, is corresponding to The weight of the multipath effect is quantitatively determined through historical data analysis and the characteristics of the current rescue scene. It is The signal attenuation interference parameter value measured for this time, is corresponding to The signal attenuation weight is adjusted according to the electromagnetic interference intensity in the rescue environment. is the total number of measurements, representing the number of data points collected;

[0099] Weighted average of the parameters , using the formula:

[0100]

[0101] Calculate the overall impact of multipath effects and signal attenuation parameters on signal transmission ;

[0102] in, is the reference environmental parameter value, used for normalization , determined according to the preset environmental standards of the rescue communication terminal, is the adjustment coefficient, which is used to adjust the sensitivity of environmental parameters to signal transmission. It is obtained based on past rescue operation experience and experimental data. is to adjust the parameters slightly to ensure that the formula near The stability of , based on the minimization error setting, is the natural logarithm function, used to convert and The linear relationship of the ratio becomes nonlinear, enhancing the sensitivity of the model to small changes;

[0103] The calculation process is as follows:

[0104] For the formula:

[0105]

[0106] Setting parameter temperature are (22°C, 23°C, 24°C);

[0107] Temperature weight is (0.5, 0.5, 0.5);

[0108] Electromagnetic Interference are (30dB, 28dB, 32dB);

[0109] Electromagnetic interference weight is (0.2, 0.2, 0.2);

[0110] Total times is 3;

[0111] Calculate the weighted value for each data point:

[0112]

[0113]

[0114]

[0115] Calculate weighted average :

[0116]

[0117] result , indicating the average environmental parameter value after considering multipath effects and signal attenuation.

[0118] For the formula:

[0119]

[0120] Setting parameters , , , ;

[0121] Compute the logarithmic part:

[0122]

[0123] Calculating the overall impact :

[0124]

[0125] If the baseline value is 0.1, which is used to indicate the threshold of significant impact on signal transmission, the result is If it is lower than this benchmark, it indicates that the impact on signal transmission is small in the current environment, which means that the signal quality is within an acceptable range and maintains good communication effects.

[0126] See also Figure 3 ,The specific steps for obtaining the rescue data analysis results are:

[0127] According to the overall impact of signal transmission , using the formula:

[0128]

[0129] Calculating the Environmental Impact Index ;

[0130] in, is the preset minimum impact threshold, which is set according to the minimum interference in the rescue scenario. is the preset maximum impact threshold, which is set according to the maximum interference occurring in the rescue scenario;

[0131] According to the environmental impact index , using the formula:

[0132]

[0133] Calculate the percentage intensity of the impact of the environment on the rescue operation , obtain the rescue data analysis results;

[0134] The calculation process is as follows:

[0135] For the formula:

[0136]

[0137] Setting parameters , minimum interference , maximum interference ;

[0138] Standardized signal transmission impact :

[0139]

[0140] Represents the environmental impact index, that is, the relative position of the signal transmission impact within the preset interference range. This value is in the range of 0 to 1, which is closer to the median, indicating a moderate degree of environmental impact.

[0141] For the formula:

[0142]

[0143] Convert the environmental impact index into a percentage:

[0144]

[0145] Result interpretation: The percentage intensity of the impact of the environment on the rescue operation is about 28.41%,

[0146] See also Figure 4 , the specific steps for obtaining the initialization delay differential equation model are:

[0147] Extract key information from historical environmental data using the formula:

[0148]

[0149] Calculate the weighted average based on historical data to generate initial parameters ;

[0150] in, It is The weight of each data point is automatically adjusted according to the timeliness and relevance of the data, obtained through historical performance and correlation analysis. It is The measured values ​​of environmental parameters for data points, is the total number of historical data points;

[0151] According to the initial parameters , using the formula:

[0152]

[0153] Calculate in time Predicted communication delay ;

[0154] in, is the attenuation coefficient, which is obtained by dynamic adjustment based on the trend of historical data changes and rescue scenarios. is a time variable, indicating the time distance from the current moment to the predicted moment. is a constant representing the base of the natural logarithm, used to implement the exponential decay characteristics of delay over time. is an exponential function that describes the decay behavior of the delay over time. Represents a negative value of the exponent, ensuring that the delay decreases over time;

[0155] The calculation process is as follows:

[0156] For the formula:

[0157]

[0158] The weights are set according to the temporal proximity of the data: , The measured temperature value Spend, is 3 data points;

[0159] Compute the weighted sum:

[0160]

[0161]

[0162] Calculate weighted average :

[0163]

[0164] Represents the average value of historical environmental parameters weighted by temporal proximity, which is used as the initial parameter for initializing the model.

[0165] For the formula:

[0166]

[0167] Setting parameters is 23.1, is 0.05, The predicted delay after 5 minutes is ;

[0168] Calculate the exponential part:

[0169]

[0170] Calculating Prediction Latency :

[0171]

[0172] In a rescue communication environment, based on experience or industry standards, a delay of more than 30 units is considered high delay. Compare with the baseline value of 30. Since 18.0 is significantly lower than 30, it indicates that the communication delay is maintained at a low level after the scheduled 5 minutes.

[0173] See also Figure 5 , the specific steps for obtaining the real-time delay prediction results are:

[0174] Percentage intensity of the impact of the environment on rescue operations , using the formula:

[0175]

[0176] Adjust time Predicted communication delay , and get the adjusted predicted communication delay ;

[0177] in, is the adjustment factor used to quantify right The impact of the rescue missions is analyzed and simulated based on past rescue missions to optimize the accuracy of delay prediction.

[0178] Based on the adjusted predicted communication delay , using the formula:

[0179]

[0180] Calculate the communication delay at the target time , get the real-time delay prediction result;

[0181] in, It is the attenuation coefficient of environmental changes, which is dynamically adjusted according to real-time environmental monitoring data. It reflects the additional impact of environmental changes on communication delays and is obtained through historical data and environmental sensitivity analysis. is an exponential decay function, which is used to simulate the impact of environmental changes on latency. Represents a negative exponent, ensuring that the delay value decreases as time increases;

[0182] The calculation process is as follows:

[0183] For the formula:

[0184]

[0185] Setting parameters unit, , is 0.05;

[0186] Adjustment factor calculation:

[0187]

[0188] Calculate adjusted latency predictions :

[0189]

[0190] result It indicates that the predicted communication delay increases slightly after taking into account the real-time rescue data. This slightly higher delay value reflects the actual impact of the rescue scenario data on the delay prediction.

[0191] For the formula:

[0192]

[0193] Setting parameters for , is 0.03, Set it to 5 minutes later. ;

[0194] Calculate the exponential decay part:

[0195]

[0196] Calculate the final real-time predicted communication delay :

[0197]

[0198] result

[0199] This means that after 5 minutes, the expected communication delay is reduced to about 15.72 units after considering the impact of environmental changes. This lower delay value indicates that the communication system is expected to be more efficient after considering the impact of environmental changes.

[0200] See also Figure 6 ,The acquisition steps for analyzing abnormal patterns and sudden changes in latency in data streams are as follows:

[0201] Communication delay according to target time and the current time of real-time communication network activity , using the formula:

[0202]

[0203] Evaluate the real-time impact of latency on communication network activity and obtain the adjusted communication network activity status ;

[0204] in, is the adjustment coefficient, which reflects the degree of influence of delay on the communication network status, obtained through the analysis of historical delay data and communication network sensitivity test;

[0205] According to the adjusted communication network activity status , using the formula:

[0206]

[0207] Quantify the abnormal behavior of the communication network status and calculate the anomaly detection index of the communication network ;

[0208] in, Yes means The time derivative of , that is, the speed at which the communication network state changes over time, is the weight coefficient of anomaly detection, which is used to adjust the sensitivity of abnormal behavior based on historical data of communication network performance and pre-defined thresholds. It is the average value of the communication network status obtained by long-term monitoring and is used for standardization The current reading of

[0209] The calculation process is as follows:

[0210] For the formula:

[0211]

[0212] Set a specific time , the following data is obtained through network monitoring:

[0213] The data transfer rate is unit, for unit, for ;

[0214] Calculate the adjustment factor:

[0215]

[0216] Calculate adjusted network activity status :

[0217]

[0218] result Indicates that the adjusted network activity status shows a slight increase, reflecting the slight impact of real-time latency.

[0219] For the formula:

[0220]

[0221] Setting parameters , obtained through continuous monitoring Units / minute (indicates a slight drop in network activity). for , for unit;

[0222] calculate Deviation from the mean:

[0223]

[0224] Calculating network anomaly detection metrics :

[0225]

[0226] result Negative values ​​indicate that network activity has decreased slightly relative to the average, but has not yet reached a significant abnormality. Values ​​close to zero suggest that there are currently no major network anomalies.

[0227] See also Figure 7 , the specific steps for obtaining the communication network fault analysis results are:

[0228] Based on the anomaly detection indicators of the communication network , using the formula:

[0229]

[0230] Calculate the communication network node from the state To status The transition probability ;

[0231] in, is the attenuation coefficient, which adjusts the sensitivity of the transition probability based on historical data and node characteristics. is the state index in the Markov model, is an exponential decay function, which is used to calculate the state transition probability. is the total number of states in the Markov model, including all conditions the communication network is in;

[0232] According to the communication network node from the state To status The transition probability , using the formula:

[0233]

[0234] Calculate the failure probability of communication network nodes in the future state , obtain the communication network fault analysis results;

[0235] in, Is the node in state The future failure probability, is a product operation, considering all state transitions that may cause failures, It indicates the status No direct transfer to state The probability of It is to calculate the total probability of at least one fault state transition;

[0236] The calculation process is as follows:

[0237] For the formula:

[0238]

[0239] Setting parameters for , Set the network to three states (1, 2, 3), is 3;

[0240] Assumptions , calculate the transition probability from state 1 to other states:

[0241] Calculate the state transition probability:

[0242]

[0243]

[0244] Normalized state transition probability:

[0245]

[0246]

[0247]

[0248] For the formula:

[0249]

[0250] Setting parameters and use , :

[0251] Calculate the probability that no failure occurs:

[0252]

[0253]

[0254] Calculate the total probability of at least one failure occurring:

[0255]

[0256] result : In state 1, the probability that the node will fail at least once in the future is about 53.3%. This is a significant failure probability, indicating that in this state, the node has a high risk of failure.

[0257] See also Figure 8 , the steps to obtain the optimized routing configuration are as follows:

[0258] According to the failure probability of the communication network node in the future state , using the formula:

[0259]

[0260] Calculate the routing adjustment demand index of communication network nodes ;

[0261] in, is a conversion factor used to convert the probability of failure into routing adjustment requirements based on the average response time of the network and the service level agreement parameter settings. It is an exponential function, which is used to smooth the impact of failure probability and ensure the sensitivity of routing adjustment to actual operation;

[0262] Adjust the demand index according to the routing of communication network nodes , using the formula:

[0263]

[0264] Evaluate the current node To Node Optimized transmission path ;

[0265] in, Is the current node To Node The original network traffic, It is a regulation factor, which is used to adjust the traffic weight according to the routing demand index and the performance index and traffic distribution of the network;

[0266] According to the current node To Node Optimized transmission path , using the formula:

[0267]

[0268] Summarize all path weights to get the optimized routing configuration ;

[0269] The calculation process is as follows:

[0270] For the formula:

[0271]

[0272] Setting parameters is 0.2, is 5;

[0273] Calculate the routing adjustment demand index :

[0274]

[0275] For the formula:

[0276]

[0277] Setting parameters is 1000 units, is 0.632121, is 0.1;

[0278] Calculate optimized transmission path weights :

[0279]

[0280] For the formula:

[0281]

[0282] Setting parameters is 1063.2121;

[0283] When there is only one pair of nodes in the network arrive hour:

[0284] Calculate the final routing configuration for the entire network :

[0285]

[0286] result Indicates the total weight of the routing configuration of the entire network. If the baseline value is 1000, the efficiency of the routing configuration is improved through dynamic adjustment, reflecting the optimization of the network's response to fault prediction.

[0287] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A control system for a portable rescue communication terminal, characterized in that: The system comprises: The environmental impact analysis module collects environmental parameters in the rescue scene, as well as interference factors such as multipath effects and signal attenuation, dynamically analyzes the parameters, and calculates the impact of the parameters on signal transmission. The calculation results are synthesized into an environmental impact index to obtain the rescue data analysis results. The communication delay prediction module uses historical environmental data to set initial parameters, initializes the delay differential equation model, adds real-time information of the rescue data analysis results to dynamically adjust the parameters, predicts future communication delays, and generates real-time delay prediction results; The fault diagnosis and response module monitors the operation status and traffic conditions of the entire communication network, analyzes abnormal patterns and sudden changes in delays in the data stream with reference to the real-time delay prediction results and the current communication network activity, calculates the future failure probability of the communication network nodes through the Markov decision process, and obtains the communication network failure analysis results; The dynamic routing management module calculates and adjusts the communication network path in real time according to the communication network fault analysis result, optimizes the transmission route of the data packet, matches the fault status and the changing traffic demand, and obtains the optimized routing configuration.

2. The control system of the portable rescue communication terminal according to claim 1, characterized in that: The steps for obtaining the influence of the calculation parameters on signal transmission are specifically as follows: Collect environmental parameters in the rescue scene and use the formula: Calculates the weighted average of the parameters ; in, It is The environmental parameter values ​​measured is corresponding to The weight of the multipath effect is quantitatively determined through historical data analysis and the characteristics of the current rescue scene. It is The signal attenuation interference parameter value measured for this time, is corresponding to The signal attenuation weight is adjusted according to the electromagnetic interference intensity in the rescue environment. is the total number of measurements; Based on the weighted average of the parameters , using the formula: Calculate the overall impact of multipath effects and signal attenuation parameters on signal transmission ; in, is the reference environmental parameter value, determined according to the preset environmental standard of the rescue communication terminal. is the adjustment coefficient, which is used to adjust the sensitivity of environmental parameters to signal transmission. It is obtained based on past rescue operation experience and experimental data. is the tuning parameter, set based on minimizing the error, is the natural logarithm function, used to convert and The linear relationship of the ratio becomes nonlinear.

3. The control system of the portable rescue communication terminal according to claim 2, characterized in that: The steps for obtaining the rescue data analysis results are specifically as follows: According to the overall impact of the signal transmission , using the formula: Calculating the Environmental Impact Index ; in, is the preset minimum impact threshold, which is set according to the minimum interference in the rescue scenario. is the preset maximum impact threshold, which is set according to the maximum interference occurring in the rescue scenario; According to the environmental impact index , using the formula: Calculate the percentage intensity of the impact of the environment on the rescue operation , and obtain the rescue data analysis results.

4. The control system of the portable rescue communication terminal according to claim 3, characterized in that: The steps of obtaining the initialized delay differential equation model are specifically as follows: Extract key information from historical environmental data using the formula: Calculate the weighted average based on historical data to generate initial parameters ; in, It is The weight of each data point is obtained through historical performance and correlation analysis. It is The measured values ​​of environmental parameters for data points, is the total number of historical data points; According to the initial parameters , using the formula: Calculate in time Predicted communication delay ; in, is the attenuation coefficient, which is obtained by dynamic adjustment based on the trend of historical data changes and rescue scenarios. is the time variable, is a constant used to realize the exponential decay characteristic of delay over time. is an exponential function that describes the decay behavior of the delay over time. Represents a negative value of the exponent.

5. The control system of the portable rescue communication terminal according to claim 4, characterized in that: The steps for obtaining the real-time delay prediction result are specifically as follows: Percentage intensity of the impact of the environment on the rescue operation , using the formula: Adjust time Predicted communication delay , and get the adjusted predicted communication delay ; in, is the adjustment factor used to quantify right The extent of the impact is based on data analysis and simulations of past rescue missions; According to the adjusted predicted communication delay , using the formula: Calculate the communication delay at the target time , get the real-time delay prediction result; in, is the attenuation coefficient of environmental changes, which is dynamically adjusted according to real-time environmental monitoring data and obtained through historical data and environmental sensitivity analysis. is an exponential decay function, which is used to simulate the impact of environmental changes on latency. Represents a negative exponent.

6. The control system of the portable rescue communication terminal according to claim 5, characterized in that: The acquisition steps of abnormal patterns and sudden changes in delays in the analysis data stream are specifically as follows: Communication delay according to the target time and the current time of real-time communication network activity , using the formula: Evaluate the real-time impact of latency on communication network activity and obtain the adjusted communication network activity status ; in, is the adjustment coefficient, which reflects the degree of influence of delay on the communication network status, obtained through the analysis of historical delay data and communication network sensitivity test; According to the adjusted communication network activity state , using the formula: Quantify the abnormal behavior of the communication network status and calculate the anomaly detection index of the communication network ; in, Yes means The time derivative of , that is, the speed at which the communication network state changes over time, is the weight coefficient of anomaly detection, which is used to adjust the sensitivity of abnormal behavior based on historical data of communication network performance and pre-defined thresholds. is the average value of the communication network status obtained by monitoring, used for standardization The current reading of 7. The control system of the portable rescue communication terminal according to claim 6, characterized in that: The steps for obtaining the communication network fault analysis result are specifically as follows: According to the anomaly detection index of the communication network , using the formula: Calculate the communication network node from the state To status The transition probability ; in, is the attenuation coefficient, which adjusts the sensitivity of the transition probability based on historical data and node characteristics. is the state index in the Markov model, is an exponential decay function, which is used to calculate the state transition probability. is the total number of states in the Markov model, including all conditions the communication network is in; According to the communication network node from the state To status The transition probability , using the formula: Calculate the failure probability of communication network nodes in the future state , obtain the communication network fault analysis results; in, Is the node in state The future failure probability, It indicates the status No direct transfer to state The probability of It is to calculate the total probability of at least one fault state transition.

8. The control system of the portable rescue communication terminal according to claim 7, characterized in that: The steps for obtaining the optimized routing configuration are specifically as follows: According to the failure probability of the communication network node in the future state , using the formula: Calculate the routing adjustment demand index of communication network nodes ; in, is a conversion factor used to convert the probability of failure into routing adjustment requirements based on the average response time of the network and the service level agreement parameter settings. is an exponential function used to smooth the impact of failure probability; Adjusting the demand index according to the routing of the communication network node , using the formula: Evaluate the current node To Node Optimized transmission path ; in, Is the current node To Node The original network traffic, It is a regulation factor, which is used to adjust the traffic weight according to the routing demand index and the performance index and traffic distribution of the network; According to the current node To Node Optimized transmission path , using the formula: Summarize all path weights to get the optimized routing configuration .

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