Intelligent emergency response method and system

Abstract

The invention relates to the technical field of data transmission, in particular to an intelligent emergency calling method and system. The method comprises the following steps: when an emergency event occurs, detecting potential bottleneck and congestion in a network, analyzing a current available transmission path based on real-time network data, evaluating the quality and priority of the path, and collecting real-time emergency data at the same time; distributing the emergency data to corresponding network slices through a predefined slicing strategy; and after the network slices are distributed, detecting the demand of emergency data traffic, evaluating the real-time slice capacity, judging whether the resources in the slices meet the demand of the emergency data traffic, and if the resources in the slices do not meet the demand of the data traffic, performing adjustment according to the real-time condition of the slices. According to the intelligent emergency response method and system, it can be ensured that the key task data can preferentially obtain necessary resources under the emergency condition, interference of low-priority data is avoided, and it is ensured that the key task data can be reliably transmitted.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to an intelligent emergency response method and system. Background Art

[0002] Information and communication technology (ICT) plays an increasingly important role in modern society. Especially when responding to emergencies and disasters, an effective communication network is a key factor in ensuring the efficiency of emergency response, the success of rescue operations, and reducing the loss of life and property. In recent years, the deployment of 5G networks and the popularization of Internet of Things (IoT) devices have brought new possibilities for emergency communications, making large-scale, high-speed data transmission possible. However, with the continuous development of technology and the increasing complexity of emergency scenarios, the traditional network resource allocation method has been difficult to meet the needs of real-time and efficient data transmission. The allocation of resources for emergency data is often evaluated based on overall needs, without refinement for specific traffic priorities. Even if some low-priority data does not require a large amount of resources, it may occupy resources in the slice, thereby affecting the transmission of more urgent data. Therefore, an intelligent emergency response method and system are provided. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent emergency response method and system to solve the problem that the allocation of resources for emergency data proposed in the above background technology is often evaluated based on overall needs, without refinement for specific traffic priorities. Even if some low-priority data does not require a large amount of resources, it may occupy resources in the slice.

[0004] To achieve the above object, the present invention provides an intelligent emergency response method, comprising the following steps: S1. When an emergency occurs, the emergency system starts real-time monitoring and evaluation of network resources, detects potential bottlenecks and congestion in the network, and analyzes the current available transmission paths based on real-time network data, and evaluates the quality and priority of the paths. At the same time, it collects real-time emergency data and introduces a dynamic adjustment mechanism for optimization in the process of evaluating the quality and priority of the paths. S2. Divide the network into multiple independent virtual network slices in advance through the 5G network slicing method. Each network slice is configured according to performance requirements. When an emergency occurs, the emergency data is allocated to the corresponding network slice through the predefined slicing strategy. The priority indication function is introduced in the process of network slice allocation for optimization; S3. After the network slice is allocated, the demand for emergency data traffic is detected and the real-time slice capacity is evaluated to determine whether the resources in the slice meet the demand for emergency data traffic. If the resources in the slice meet the demand for emergency data traffic, emergency data is transmitted; if the resources in the slice do not meet the demand for data traffic, adjustments are made according to the real-time status of the slice; S4. After the emergency data is transmitted to the target terminal, the target terminal activates the emergency plan that has been pre-set based on historical data and simulation exercise results. The emergency plan can adjust the response measures according to the incident situation and send a call instruction to the relevant responsible personnel.

[0005] As a further improvement of the technical solution, in S1, the emergency system starts real-time monitoring and evaluation of network resources to detect potential bottlenecks and congestion in the network, as follows: The emergency system continuously collects data related to network performance through monitoring tools deployed on various nodes of the network. The emergency system regularly scans the network infrastructure and checks the status of key components. The performance related to network performance includes bandwidth utilization, latency, and packet loss rate. If the network nodes in a certain area show congestion risks, after eliminating the influence of external factors, the emergency system marks the area as a key focus and increases the data sampling frequency of the node.

[0006] As a further improvement of the technical solution, in S1, based on real-time network data, the currently available transmission paths are analyzed, and the quality and priority of the paths are evaluated, as follows: Evaluate path priority: ; in, is the path priority value; is the actual available bandwidth value; is the total available bandwidth value of the path; is the weight of bandwidth availability; is the transmission delay of the path; is the weight of path delay; is the packet loss rate of the path; is the weight of the packet loss rate; is the reliability of the path; is the weight of path reliability; In the process of evaluating the quality and priority of the paths, a dynamic adjustment mechanism is introduced for optimization. The optimization is as follows: ; in, The optimized path The priority value of After optimization The actual available bandwidth value on the path; After optimization The transmission delay of each path; After optimization Packet loss rate of each path; is the dynamic correction factor.

[0007] As a further improvement of the present technical solution, the emergency data in S1 includes real-time command and dispatch, on-site video monitoring, location tracking data and alarm notification data.

[0008] As a further improvement of the technical solution, in S2, emergency data is allocated to corresponding network slices through a predefined slice strategy, specifically: Slice allocation: ; in, It is emergency data Assign to slice score; For data bandwidth requirements; For data Delay requirements; For data Packet loss rate requirement; For Slices Available bandwidth; For Slices Delay For Slices Packet loss rate; The importance of bandwidth; The importance of latency; The importance of packet loss rate; Final transmission path: ; in, For data The final transmission path.

[0009] As a further improvement of the technical solution, in S2, a priority indication function is introduced in the process of network slice allocation for optimization, and the optimization is as follows: ; in, is the priority indicator function; Resource enhancement factor for high priority data; Resource enhancement factor for low priority data; Resource enhancement factor for medium priority data; Indicates that the data is of high priority; Indicates that the data is of low priority; Indicates that the data is of medium priority; ; in, Optimized emergency data Assign to slice score; is the weight of the priority indicator function; ; in, For optimized data The final transmission path.

[0010] As a further improvement of the technical solution, in S3, the specific steps of detecting the demand for emergency data traffic are as follows: Initial emergency data traffic requirements: ; in, To meet emergency data traffic needs; For the Benchmark coefficients for data types; For the The urgency of the data type; For the Network status adaptation coefficient of data types; For the Size coefficient of the data type; ; is the number of data types; Dynamically adjust data traffic demand: ; in, The data traffic demand is dynamically adjusted; is the current network load or delay; is the maximum load of the network; is the adjustment factor; The total traffic demand is obtained by taking the weighted sum of all emergency data traffic demands: ; in, For the final data traffic demand; For the The weight of the data type.

[0011] As a further improvement of the technical solution, in S3, the real-time slice capacity is evaluated as follows: ; in, is the total capacity of the current slice; is the available bandwidth of the current slice; is the processing capacity of the current slice; is the weighted value of the latency and packet loss rate of the current slice; The resource utilization efficiency of the current slice; The service quality level of the slice; Determine whether the resources in the slice meet the requirements of emergency data traffic: ; in, is the judgment coefficient, if If it is greater than or equal to 1, it means that the slice resources meet the demand; if it is less than 1, it is adjusted according to the real-time status of the slice.

[0012] As a further improvement of the technical solution, in S3, adjustments are made according to the real-time status of the slices, as follows: If other slice resources are not tight, according to the optimized emergency data Assign to slice Score Emergency data is allocated to slices with high scores; if other slices have limited resources, the slice capacity is adjusted according to the load balancing and traffic optimization strategies: ; in, is the adjusted bandwidth requirement; For bandwidth requirements; is the regulating factor; The current bandwidth utilization of the slice.

[0013] On the other hand, the present invention provides an intelligent emergency response system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the intelligent emergency response methods described above.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The intelligent emergency response method and system introduces a priority indication function. When data is of high priority, it will obtain more resources, while low-priority data will be restricted. This adjustment can ensure that in an emergency, critical mission data can obtain necessary resources first, avoid interference from low-priority data, and ensure that critical mission data can be transmitted quickly and reliably.

[0015] 2. In the intelligent emergency response method and system, slice capacity evaluation is performed after network slice allocation, and whether resources need to be reallocated or adjusted can be determined based on the actual priority and resource capacity of the slice, and excessive pre-allocation of resources can be avoided; and after slice allocation, the system can evaluate whether the capacity of the slice meets the needs based on the actual needs of emergency data. If it is found that the resources of a slice are insufficient, the system can dynamically adjust, reallocate data or adjust the path to ensure that high-priority emergency data can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1: Please refer to Figure 1 As shown, this embodiment provides an intelligent emergency response method, comprising the following steps: S1. When an emergency occurs, the emergency system starts real-time monitoring and evaluation of network resources, detects potential bottlenecks and congestion in the network, and analyzes the current available transmission paths based on real-time network data, and evaluates the quality and priority of the paths. At the same time, it collects real-time emergency data and introduces a dynamic adjustment mechanism for optimization in the process of evaluating the quality and priority of the paths. In S1, the emergency system starts real-time monitoring and evaluation of network resources to detect potential bottlenecks and congestion in the network, as follows: The emergency system continuously collects data related to network performance through monitoring tools deployed on various nodes of the network (such as traffic analyzers, intrusion detection systems (IDS), security information and event management systems (SIEM), etc.). The emergency system regularly scans the network infrastructure and checks the status of key components (routers, switches) to ensure that they are in optimal working condition. Among them, the performance related to network performance includes bandwidth utilization, latency, and packet loss rate; if the network nodes in a certain area show congestion risks, after excluding the influence of external factors, the emergency system marks the area as a key focus and increases the data sampling frequency of the node to obtain more accurate information. For example, the original sampling once a minute may become once a second, so that instantaneous changes can be captured more quickly to help confirm whether there is real congestion. At the same time, the system takes into account the influence of external factors, such as whether weather conditions or other unforeseen factors will have an adverse effect on the communication link.

[0019] In S1, based on real-time network data, the currently available transmission paths are analyzed and the quality and priority of the paths are evaluated as follows: Calculate bandwidth availability: ; in, for bandwidth availability; is the total bandwidth of the path; The bandwidth that has been used; is the number of data types; Calculate path delay: ; in, is the path delay; is the delay of data packet transmission; is the propagation delay; is the queuing delay, which can be estimated based on the load and flow of the path; Calculate the packet loss rate: ; in, is the packet loss rate; is the total number of packets sent; is the number of packets lost; Calculate path reliability: ; in, is the reliability of the path, which is proportional to the inverse of the packet loss rate and delay; Evaluate path priority: ; in, is the path priority value; is the actual available bandwidth value; is the total available bandwidth value of the path; is the weight of bandwidth availability; is the transmission delay of the path; is the weight of path delay; is the packet loss rate of the path; is the weight of the packet loss rate; is the reliability of the path; is the weight of path reliability; In the process of evaluating the quality and priority of the paths, a dynamic adjustment mechanism is introduced for optimization. The optimization is as follows: ; in, The optimized path The priority value indicates the overall quality of a path. The higher the value, the better the path quality and the more suitable it is for data transmission. This score is used to select the best path among multiple paths to ensure the reliable transmission of high-priority emergency data. After optimization The actual available bandwidth value on the path. Bandwidth is a key factor affecting data transmission rate and reliability. In an emergency, the bandwidth of the path must be sufficient to carry real-time data (such as video, command and dispatch information, etc.); After optimization The transmission delay of a path usually refers to the data transmission time from the source to the destination. The lower the delay, the faster the data transmission speed. Especially in emergency communications, the lower the delay can improve the response speed. After optimization The packet loss rate of a path indicates the proportion of data packets lost during data transmission. The lower the packet loss rate, the better the network quality, which is suitable for data with real-time and high reliability requirements; It is a dynamic correction factor that adjusts the path quality score according to the real-time status of the network (such as network load, congestion, priority of emergency data, etc.); certain time points (such as network congestion, node failure, etc.) will be dynamically adjusted to affect the final evaluation score of each path. It can make weighted adjustments to the evaluation of each path based on factors such as real-time traffic, bandwidth requirements, and latency, thereby reflecting changes in network status; The emergency data in S1 includes real-time command and dispatch, on-site video surveillance, location tracking data, and alarm notification data. Real-time command and dispatch data is usually very sensitive to latency, requiring low latency and high reliability; on-site video surveillance data has high bandwidth requirements, especially in high-definition and panoramic surveillance, which may require a larger bandwidth, but has low latency requirements (can tolerate a few seconds of latency); location tracking data has requirements for frequency and transmission interval, and is usually small-volume data, but may require high-frequency updates; alarm notification data is small in size, but must ensure fast and reliable transmission.

[0020] S2. Divide the network into multiple independent virtual network slices in advance through the 5G network slicing method. Each network slice is configured according to performance requirements (divided into high-priority slices and low-priority slices). When an emergency occurs, the emergency data is allocated to the corresponding network slice through the predefined slicing strategy. The priority indication function is introduced in the process of network slice allocation for optimization; In S2, emergency data is allocated to the corresponding network slices through the predefined slice strategy, specifically: Each slice is matched according to its resource configuration, and emergency data is allocated to the appropriate slice according to its needs. If the data demand exceeds the resource capacity of a slice, you need to choose another slice or adjust the resource allocation. To determine which slice should be assigned, you can use the following formula to match the slice: Slice allocation: ; in, It is emergency data Assign to slice score; For data bandwidth requirements; For data Delay requirements; For data Packet loss rate requirement; For Slices Available bandwidth; For Slices Delay For Slices Packet loss rate; The importance of bandwidth; The importance of latency; The importance of packet loss rate; According to the score Select the slice with the highest score as the data Assigned target slices; Once emergency data is assigned to a specific slice, the next step is to select an appropriate transmission path based on the slice priority and path priority. The ultimate goal is to optimize resource allocation and ensure that high-priority data obtains the required resources in the network.

[0021] Final transmission path: ; in, For data The final transmission path; In S2, a priority indication function is introduced to optimize the network slice allocation process. After optimization, it is as follows: ; in, is the priority indicator function; The resource enhancement coefficient for high-priority data. A higher coefficient means that high-priority data will obtain more bandwidth, processing power and other resources. is the resource enhancement factor for low-priority data. A low factor may indicate that low-priority data will face bandwidth limitations or higher latency. The resource enhancement coefficient for medium priority data. The value of this coefficient is usually between high priority and low priority. Indicates that the data is of high priority; Indicates that the data is of low priority; Indicates that the data is of medium priority; ; in, Optimized emergency data Assign to slice score; is the weight of the priority indicator function; ; in, For optimized data The final transmission path; The optimized formula introduces , which is a mechanism to dynamically adjust resource allocation based on the priority of data. Through the priority indication function, when data belongs to high priority, it will get more resources (such as bandwidth, processing power, etc.), while low priority data will be restricted. This adjustment can ensure that in an emergency, mission-critical data can get the necessary resources first and avoid interference from low priority data streams; Through weighted priority scoring and path optimization, the final transmission path not only takes into account the resource matching of the slice, but also the dynamic adjustment of the priority. High-priority data will choose the path with the best transmission quality to ensure that mission-critical data can be transmitted quickly and reliably.

[0022] S3. After the network slice is allocated, the demand for emergency data traffic is detected and the real-time slice capacity is evaluated to determine whether the resources in the slice meet the demand for emergency data traffic. If the resources in the slice meet the demand for emergency data traffic, emergency data is transmitted; if the resources in the slice do not meet the demand for data traffic, adjustments are made according to the real-time status of the slice; Slice capacity assessment after network slice allocation can dynamically adapt to real-time changes in network resources, determine whether resources need to be reallocated or adjusted based on actual priorities and slice resource capacity, and avoid over-preallocation of resources; The network environment is usually highly dynamic, especially when an emergency occurs, the network load, bandwidth, path quality, etc. may change dramatically. If capacity assessment is performed before slice allocation, inaccurate judgments may be made based on outdated network status; After the slices are allocated, the system can evaluate whether the capacity of the slices meets the needs based on the actual needs of the emergency data traffic (such as bandwidth, latency requirements, etc.). If it is found that a slice has insufficient resources, the system can dynamically adjust, reallocate data or adjust the path to ensure that high-priority emergency data can be guaranteed; pre-assessment of resources may lead to premature prediction of certain slice resources, which cannot reflect the actual needs of emergency data traffic and changes in the network environment in real time; If capacity assessment is performed before slice allocation, it may lead to over-reservation of network resources, especially in the face of uncertain emergency events. Excessive pre-allocation may cause the resources of some low-priority slices to be wasted, thus affecting the data transmission of high-priority slices.

[0023] In S3, the specific steps for detecting the demand for emergency data traffic are as follows: Initial emergency data traffic requirements: ; in, To meet emergency data traffic needs; For the Different types of emergency data have different benchmark values ​​(e.g., video surveillance data has a higher coefficient, while command and dispatch data has a lower coefficient); For the The urgency of this data type reflects the priority of the emergency event, which is usually derived from the previous urgency evaluation formula; For the The network status adaptation coefficient of the data type takes into account factors such as the current network bandwidth, delay, and packet loss rate; For the The size coefficient of each data type indicates the size of each data type. It is usually estimated based on the data sampling rate, frequency, and amount of data per unit time; ; is the number of data types; Detailed parameters and calculation methods: Urgency reflects the severity of the emergency event and is usually predicted by a predefined policy or machine learning model. For each data type, we can adjust the urgency based on the event type and time sensitivity. It can be expressed as: ; in, The importance of the event to the data type (e.g., command and dispatch is more important, while video surveillance is less important); Time sensitivity, which reflects the sensitivity of the data to delay (e.g., alarm notifications are very sensitive to latency, while video surveillance is relatively tolerant); The complexity of the data reflects the difficulty of data processing and transmission; The weight of the event's importance to the data type; is the weight of time sensitivity; is the weight of the complexity of the data; The network state adaptation coefficient reflects the current network status, mainly including factors such as bandwidth, delay, packet loss rate, etc. When the network load is high or congestion occurs, the network state adaptation coefficient may be low. The specific formula is: ; in, is the available bandwidth of the current path; The bandwidth required for the current data; is the packet loss rate of the current path; is the maximum allowed packet loss rate (usually set as a threshold); this coefficient reflects the impact of network status on data traffic. When the network is congested or bandwidth is insufficient, the adaptation coefficient will decrease, affecting the estimation of data traffic demand.

[0024] During an emergency, the system needs to dynamically adjust traffic estimates based on real-time changes in network status and data. For example, when the network load is high, the system may need to prioritize the needs of high-priority data and temporarily reduce the transmission frequency or quality of low-priority traffic; Therefore, it is necessary to dynamically adjust the data traffic demand: ; in, The data traffic demand is dynamically adjusted; is the current network load or delay; is the maximum load of the network; is the adjustment factor, which indicates the sensitivity to load changes; The total traffic demand is obtained by taking the weighted sum of all emergency data traffic demands: ; in, For the final data traffic demand; For the The weight of each data type reflects its importance in the emergency; In S3, the real-time slice capacity is evaluated as follows: ; in, is the total capacity of the current slice; is the available bandwidth of the current slice. The total bandwidth requirement of emergency data can be determined by the weighted sum of different data, such as video stream, sensor data stream, alarm notification stream, etc. Each data stream has different bandwidth requirements; The processing capacity of the current slice. The processing capacity here refers to the processing capacity or resource consumption rate of network resources (such as computing units, storage, routing, etc.) in the slice, which is often linked to CPU, GPU, storage resources, etc. This coefficient takes into account the utilization of the processing capacity of the slice in emergency data flow. For example, in the case of high-priority emergency data flow, the processing capacity requirement may be greater; It is the weighted value of the latency and packet loss rate of the current slice. Latency refers to the transmission delay of data from source to destination, usually measured in milliseconds (ms). In emergency situations, data streams with higher latency requirements (such as real-time command and dispatch) require low-latency slices. Packet loss rate refers to the proportion of data packets lost during transmission, usually expressed as a percentage. A high packet loss rate will lead to data loss and affect the reliability of emergency communications, especially critical data streams such as alarm notifications. The weighting of latency and packet loss rate is to comprehensively consider latency and packet loss rate. The weighting coefficient can be used to adjust the weight of latency and packet loss rate in slice quality; The current slice resource utilization efficiency is used to measure the slice resource utilization efficiency. If the utilization of a slice's bandwidth, processing power and other resources is very low, A value lower than 1 indicates that resources are not fully utilized. On the contrary, when resources are used efficiently, The value is close to 1. For example, if a slice is almost fully occupied when a high priority task is assigned, It can be close to 1; conversely, if the load is low, it may be close to 0; The service quality level of the slice. This score can be calculated based on whether the slice meets the service level agreement (SLA). It can usually be scored based on multiple factors such as latency, bandwidth, and reliability. The service quality level can set different service quality requirements. For example, the service quality level of video streams and sensor data streams is lower, but the critical mission command and dispatch streams and alarm notification streams may require higher service quality levels. The score is usually a value between 0 and 1, with 1 indicating the best quality; Determine whether the resources in the slice meet the needs of emergency data: ; in, is the judgment coefficient, if If it is greater than or equal to 1, it means that the slice resources meet the demand; if it is less than 1, it is adjusted according to the real-time status of the slice; In S3, adjustments are made based on the real-time status of the slice, as follows: If other slice resources are not tight, according to the optimized emergency data Assign to slice Score Emergency data is allocated to slices with high scores; if other slices have limited resources, the slice capacity is adjusted according to the load balancing and traffic optimization strategies: ; in, is the adjusted bandwidth requirement; For bandwidth requirements; It is a regulation factor used to adjust bandwidth requirements according to network load conditions; The current bandwidth utilization of the slice, ranging from 0 to 1, indicates the utilization of slice resources.

[0025] Through this mechanism, the network can flexibly respond to different data flow requirements in emergency events, while balancing the load of slices to ensure that high-priority data flows are transmitted first, while low-priority data flows are reasonably scheduled without affecting critical tasks. This adaptive bandwidth adjustment not only improves the efficiency of network resource utilization, but also enhances the network's robustness in responding to emergencies.

[0026] Embodiment 2: This embodiment provides an intelligent emergency response system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned intelligent emergency response method.

[0027] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. An intelligent emergency response method, characterized in that: The following steps are involved: S1. When an emergency occurs, the emergency system starts real-time monitoring and evaluation of network resources, detects potential bottlenecks and congestion in the network, and analyzes the current available transmission paths based on real-time network data, and evaluates the quality and priority of the paths. At the same time, it collects real-time emergency data and introduces a dynamic adjustment mechanism for optimization in the process of evaluating the quality and priority of the paths. S2. Divide the network into multiple independent virtual network slices in advance through the 5G network slicing method. Each network slice is configured according to performance requirements. When an emergency occurs, the emergency data is allocated to the corresponding network slice through the predefined slicing strategy. The priority indication function is introduced in the process of network slice allocation for optimization; S3. After the network slice is allocated, the demand for emergency data traffic is detected and the real-time slice capacity is evaluated to determine whether the resources in the slice meet the demand for emergency data traffic. If the resources in the slice meet the demand for emergency data traffic, emergency data is transmitted; if the resources in the slice do not meet the demand for data traffic, adjustments are made according to the real-time status of the slice; S4. After the emergency data is transmitted to the target terminal, the target terminal activates the emergency plan that has been pre-set based on historical data and simulation exercise results. The emergency plan can adjust the response measures according to the incident situation and send a call instruction to the relevant responsible personnel.

2. The intelligent emergency response method according to claim 1, characterized in that: In S1, the emergency system starts real-time monitoring and evaluation of network resources to detect potential bottlenecks and congestion in the network, as follows: The emergency system continuously collects data related to network performance through monitoring tools deployed on various nodes of the network. The emergency system regularly scans the network infrastructure and checks the status of key components. The performance related to network performance includes bandwidth utilization, latency, and packet loss rate. If the network nodes in a certain area show congestion risks, after eliminating the influence of external factors, the emergency system marks the area as a key focus and increases the data sampling frequency of the node.

3. The intelligent emergency response method according to claim 2, characterized in that: In S1, based on real-time network data, the currently available transmission paths are analyzed, and the quality and priority of the paths are evaluated, as follows: Evaluate path priority: ； in, is the path priority value; is the actual available bandwidth value; is the total available bandwidth value of the path; is the weight of bandwidth availability; is the transmission delay of the path; is the weight of path delay; is the packet loss rate of the path; is the weight of the packet loss rate; is the reliability of the path; is the weight of path reliability; In the process of evaluating the quality and priority of the paths, a dynamic adjustment mechanism is introduced for optimization. The optimization is as follows: ； in, The optimized path The priority value of After optimization The actual available bandwidth value on the path; After optimization The transmission delay of each path; After optimization Packet loss rate of each path; is the dynamic correction factor.

4. The intelligent emergency response method according to claim 3, characterized in that: The emergency data in S1 includes real-time command and dispatch, on-site video monitoring, location tracking data and alarm notification data.

5. The intelligent emergency response method according to claim 4, characterized in that: In S2, emergency data is allocated to corresponding network slices through a predefined slice strategy, specifically: Slice allocation: ； in, It is emergency data Assign to slice score; For data bandwidth requirements; For data Delay requirements; For data Packet loss rate requirement; For Slices Available bandwidth; For Slices Delay For Slices Packet loss rate; The importance of bandwidth; The importance of delay; The importance of packet loss rate; Final transmission path: ； in, For data The final transmission path.

6. The intelligent emergency response method according to claim 5, characterized in that: In S2, a priority indication function is introduced to optimize the network slice allocation process, and the optimization is as follows: ； in, is the priority indicator function; Resource enhancement factor for high priority data; Resource enhancement factor for low priority data; Resource enhancement factor for medium priority data; Indicates that the data is of high priority; Indicates that the data is of low priority; Indicates that the data is of medium priority; ； in, Optimized emergency data Assign to slice score; is the weight of the priority indicator function; ； in, For optimized data The final transmission path.

7. The intelligent emergency response method according to claim 6, characterized in that: In S3, the specific steps of detecting the demand for emergency data traffic are as follows: Initial emergency data traffic requirements: ; in, To meet emergency data traffic needs; For the Benchmark coefficients for data types; For the The urgency of the data type; For the Network status adaptation coefficient of data types; For the Size coefficient of the data type; ; is the number of data types; Dynamically adjust data traffic demand: ； in, The data traffic demand is dynamically adjusted; is the current network load or delay; is the maximum load of the network; is the adjustment factor; The total traffic demand is obtained by taking the weighted sum of all emergency data traffic demands: ； in, For the final data traffic demand; For the The weight of the data type.

8. The intelligent emergency response method according to claim 7, characterized in that: In S3, the real-time slice capacity is evaluated as follows: ； in, is the total capacity of the current slice; is the available bandwidth of the current slice; is the processing capacity of the current slice; is the weighted value of the latency and packet loss rate of the current slice; The resource utilization efficiency of the current slice; The service quality level of the slice; Determine whether the resources in the slice meet the requirements of emergency data traffic: ； in, is the judgment coefficient, if If it is greater than or equal to 1, it means that the slice resources meet the demand; if it is less than 1, it is adjusted according to the real-time status of the slice.

9. The intelligent emergency response method according to claim 8, characterized in that: In S3, adjustments are made according to the real-time status of the slice, as follows: If other slice resources are not tight, according to the optimized emergency data Assign to slice Score Assign emergency data to slices with high scores; If other slice resources are insufficient, adjust the slice capacity based on load balancing and traffic optimization strategies: ； in, is the adjusted bandwidth requirement; For bandwidth requirements; is the regulating factor; The current bandwidth utilization of the slice.

10. An intelligent emergency response system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the intelligent emergency response method as described in any one of claims 1-9.

Images