Intelligent emergency command system and method based on artificial intelligence

Abstract

The invention provides an intelligent emergency command system and method based on artificial intelligence. The method comprises the following steps: acquiring an emergency data stream containing a first event feature set; determining a first risk analysis cluster according to risk assessment modules corresponding to the features, wherein each module is associated with at least one initial response strategy set; performing an emergency decision on each module in the cluster; screening target response strategies which cope with the time efficiency index reaching the standard from the response strategies corresponding to the modules, and performing dynamic decomposition processing on each target strategy to generate a dynamic response sub-strategy set; and finally, generating an emergency response instruction for the module based on the dynamic response sub-strategy set. According to the dynamic decomposition, strategy decomposition granularity is calculated through environmental interference factors, and then emergency disposal data is mapped to generate a sub-strategy set. According to the invention, the timeliness and accuracy of emergency decision making can be improved, and the resource allocation efficiency is optimized.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence and emergency management technology, and more specifically, to an intelligent emergency command system and method based on artificial intelligence. Background Art

[0002] In today's society, with the acceleration of urbanization and the intertwining of various complex factors, the frequency and complexity of emergencies, such as natural disasters, public health incidents, and safety accidents, are increasing. These incidents are often characterized by suddenness, wide-ranging impacts, and high levels of damage, posing a serious threat to the normal operation of society and the safety of people's lives and property. To effectively respond to these emergencies, emergency command systems have emerged. Their primary purpose is to quickly and accurately collect and analyze relevant information, formulate appropriate emergency response strategies, and effectively dispatch resources and coordinate command to minimize the resulting losses.

[0003] Existing emergency command systems mostly rely on traditional information processing and decision-making methods. While these systems can meet basic emergency command needs to a certain extent, they suffer from numerous limitations. First, these systems often lack comprehensive and timely data collection, failing to obtain dynamic information about emergencies in real time, resulting in insufficient basis for decision-making. Second, in terms of risk assessment, existing systems typically employ relatively simple assessment models, making it difficult to accurately identify and quantify the risk characteristics of complex events, thus impacting the scientific nature and effectiveness of emergency response strategies. Furthermore, in terms of resource scheduling, existing systems often lack flexibility and intelligence, unable to adjust resource allocation plans in a timely manner based on dynamic changes in events, resulting in frequent resource waste or shortages. Finally, the emergency response command generation process in existing systems is relatively fixed and rigid, unable to adapt to complex and changing emergency scenarios and unable to meet the requirements of rapid and accurate emergency command.

[0004] In the process of implementing the embodiments of the present invention, there are at least the following problems or defects in the existing technology: the existing emergency command system has deficiencies in the real-time and comprehensiveness of data collection, the accuracy of risk assessment, the flexibility of resource scheduling, and the intelligent generation of emergency response instructions, and it is difficult to meet the complex and changeable needs of responding to emergencies. A more efficient, intelligent and flexible emergency command system and method are urgently needed to solve these problems. Summary of the Invention

[0005] The present invention provides an intelligent emergency command system and method based on artificial intelligence.

[0006] In a first aspect of the present invention, a smart emergency command method based on artificial intelligence is provided, comprising:

[0007] Obtain the data stream of the emergency event, where the data stream of the emergency event includes a first event feature set;

[0008] According to the risk assessment modules corresponding to each first event feature in the first event feature set, determine a risk analysis cluster as the first risk analysis cluster, where each risk assessment module has at least one corresponding initial response strategy set;

[0009] For each first risk assessment module in the first risk analysis cluster, perform the following emergency decision steps: Screen out the target response strategies from at least one response strategy corresponding to the first risk assessment module to obtain at least one target response strategy, where the target response strategy is a response strategy whose response time index meets the preset threshold condition, and the at least one response strategy is evolved from a first event feature subset to the at least one initial response strategy set, and the first event feature subset corresponds to the first risk assessment module;

[0010] Perform dynamic decomposition processing on each target response strategy in the at least one target response strategy to generate a dynamic response sub-strategy set, and obtain at least one dynamic response sub-strategy set;

[0011] Generate an emergency response instruction for the first risk assessment module according to the at least one dynamic response sub-strategy set.

[0012] Further, the method further includes:

[0013] In response to receiving a resource scheduling request, determine the target resource scheduling module corresponding to the resource scheduling request;

[0014] In response to determining that there is a corresponding target scheduling strategy for the target resource scheduling module, determine the difference set between at least one scheduling strategy and at least one target scheduling strategy as a non-target scheduling strategy group, where the target scheduling strategy is a target response strategy corresponding to the target resource scheduling module, and the scheduling strategy is a response strategy corresponding to the target resource scheduling module;

[0015] Perform a response resource allocation operation for the resource scheduling request according to the non-target scheduling strategy group to obtain a resource scheduling result.

[0016] Further, the performing a response resource allocation operation for the resource scheduling request according to the non-target scheduling strategy group to obtain a resource scheduling result includes:

[0017] In response to determining that the dynamic decomposition processing of the at least one target scheduling strategy is not completed, select the target scheduling strategies that have completed dynamic decomposition from the at least one target scheduling strategy as decomposed strategy units to obtain a set of decomposed strategy units;

[0018] According to the non-target scheduling policy group and the decomposed policy unit set, a resource allocation operation in response to the resource scheduling request is performed to obtain a resource scheduling result.

[0019] Furthermore, performing a resource allocation operation in response to the resource scheduling request according to the non-target scheduling policy group and the decomposed policy unit set to obtain a resource scheduling result includes:

[0020] Selecting a target scheduling strategy that has not completed dynamic decomposition from the at least one target scheduling strategy as an uncompleted decomposition strategy unit to obtain an uncompleted decomposition strategy unit set;

[0021] A resource allocation operation in response to the resource scheduling request is performed according to the non-target scheduling policy group, the decomposed policy unit set, and the uncompleted decomposition policy unit set to obtain a resource scheduling result.

[0022] Furthermore, the dynamically decomposing each target response strategy in the at least one target response strategy to generate a dynamic response sub-strategy set includes:

[0023] For each target response strategy, perform the following decomposition processing steps:

[0024] According to the environmental interference factor corresponding to the target response strategy, the strategy decomposition granularity is calculated; by formula

[0025]

[0026] Determine the policy decomposition granularity, where is the real-time response capability value, is the risk complexity coefficient, t is the duration of the event, , , Adapt parameters to the environment;

[0027] Creating an initial response sub-strategy cluster according to the policy decomposition granularity;

[0028] The emergency disposal data corresponding to the target response strategy is mapped to the initial response sub-strategy cluster to obtain a dynamic response sub-strategy set.

[0029] Furthermore, after mapping the emergency handling data corresponding to the target response strategy to the initial response sub-strategy cluster to obtain a dynamic response sub-strategy set, the method further includes:

[0030] Bind the response parameter set corresponding to the dynamic response sub-policy set to the risk assessment parameters corresponding to the first risk assessment module, and disable the target response policy.

[0031] Further, before generating the emergency response instruction for the first risk assessment module according to the at least one dynamic response sub-policy set, the method further includes:

[0032] In response to receiving the secondary event data stream, determine the risk analysis cluster corresponding to the secondary event data stream as the second risk analysis cluster, where the secondary event data stream represents the event data that occurs after the first event feature set;

[0033] In response to determining that there is a target initial response policy in the second initial response policy set, select the target initial response policy from the second initial response policy set to obtain at least one target initial response policy, where each second initial response policy is the initial response policy for the second event feature subset to be processed corresponding to the second risk analysis cluster, the second event feature subset is a subset of the second event feature set corresponding to the secondary event data stream, and the target initial response policy is the target response policy corresponding to the first risk analysis cluster that has not completed dynamic decomposition;

[0034] Determine the difference set between the second initial response policy set and the at least one target initial response policy as the non-target initial response policy set;

[0035] For each target initial response policy, input the second event feature subset corresponding to the target initial response policy into the target initial response policy and the corresponding initial response sub-policy cluster;

[0036] For each non-target initial response policy, input the second event feature subset corresponding to the non-target initial response policy into the non-target initial response policy.

[0037] Further, after screening out the target response policy from the at least one response policy corresponding to the first risk assessment module to obtain at least one target response policy, the method further includes:

[0038] In response to detecting that there is no target response policy in the at least one response policy, determine the at least one response policy and the corresponding at least one emergency response plan as the emergency response instruction.

[0039] Further, generating the emergency response instruction for the first risk assessment module according to the at least one dynamic response sub-policy set includes:

[0040] In response to determining that there is a critical unresolved response strategy among the at least one response strategy, select the critical unresolved response strategy from the at least one response strategy to obtain at least one critical unresolved response strategy;

[0041] Generate an emergency response instruction for the first risk assessment module according to the at least one critical unresolved response strategy and the at least one dynamic response sub-strategy set.

[0042] In a second aspect of the present invention, there is provided an intelligent emergency command system based on artificial intelligence, including:

[0043] A data acquisition module for real-time collecting an emergency event data stream, where the emergency event data stream includes a first event feature set;

[0044] A risk assessment cluster module for determining a first risk analysis cluster according to a risk assessment module corresponding to each first event feature in the first event feature set;

[0045] A strategy screening module for performing the following operations on each first risk assessment module in the first risk analysis cluster:

[0046] Screen a target response strategy from at least one response strategy corresponding to the first risk assessment module, where a response timeliness index of the target response strategy meets a preset threshold condition;

[0047] A dynamic decomposition module for performing dynamic decomposition processing on each target response strategy to generate a dynamic response sub-strategy set;

[0048] An instruction generation module for generating an emergency response instruction for the first risk assessment module according to the dynamic response sub-strategy set.

[0049] According to the above embodiments of the present invention, there are at least the following beneficial effects:

[0050] 1. By establishing an intelligent matching mechanism between the risk assessment module and the dynamic response strategy, the problem of disconnection between the strategy and the event characteristics in traditional emergency response is solved. This method can automatically generate a risk analysis cluster according to the first event feature set, and screen the optimal target response strategy based on timeliness, significantly improving the adaptability of emergency decision-making to the event characteristics and avoiding the lag and subjective error of manual matching.

[0051] 2. Adopting a dynamic decomposition processing mechanism combined with environmental interference factors to calculate the strategy granularity overcomes the technical defect of poor adaptability of fixed strategies in complex scenarios. By formulaically calculating the decomposition granularity and generating a dynamic response sub-strategy set, the real-time refinement and dynamic adjustment of emergency strategies are realized, ensuring that the response plan can be continuously optimized with the evolution of events and environmental changes, and improving the accuracy and execution efficiency of response measures.

[0052] 3. Through the difference calculation of resource scheduling and non-target strategies and the linkage processing of secondary events, resource conflicts and response blind spots during multi-tasking are resolved. The system can automatically identify undecomposed strategies, completed decomposed strategies, and secondary event-related strategies. Through differentiated resource allocation and strategy binding mechanisms, it achieves efficient resource scheduling and multi-level risk coordination, avoiding missed responses and wasted resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily apparent by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present invention are shown by way of example and not limitation, in which:

[0054] Figure 1 A flowchart of an artificial intelligence-based intelligent emergency command method provided by one embodiment of the present invention;

[0055] Figure 2 A schematic diagram of the structure of an artificial intelligence-based smart emergency command system provided by one embodiment of the present invention;

[0056] Figure 3 The figure schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0058] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.

[0059] It should be noted that any number of elements in the drawings is for illustration only and not for limitation, and any naming is only for distinction and does not have any limiting meaning.

[0060] Reference below Figure 1 , Figure 1 The following is a flow chart of an intelligent emergency command method based on artificial intelligence provided by one embodiment of the present invention. Figure 1As shown, an intelligent emergency command method based on artificial intelligence includes:

[0061] S1. Acquire an emergency event data stream, wherein the emergency event data stream includes a first event feature set;

[0062] S2. Determine a risk analysis cluster as a first risk analysis cluster based on the risk assessment module corresponding to each first event feature in the first event feature set, wherein each risk assessment module has at least one corresponding initial response strategy set;

[0063] S3. For each first risk assessment module in the first risk analysis cluster, perform the following emergency decision-making steps: screen a target response strategy from at least one response strategy corresponding to the first risk assessment module to obtain at least one target response strategy, wherein the target response strategy is a response strategy for a timeliness index that satisfies a preset threshold condition, and the at least one response strategy is obtained by evolving from a first event feature subset to the at least one initial response strategy set, the first event feature subset corresponding to the first risk assessment module;

[0064] S4. Dynamically decompose each target response strategy in the at least one target response strategy to generate a dynamic response sub-strategy set, thereby obtaining at least one dynamic response sub-strategy set;

[0065] S5. Generate an emergency response instruction for the first risk assessment module based on the at least one dynamic response sub-strategy set.

[0066] It should be noted that the emergency data stream mentioned here refers to the various information about the event collected in real time during the emergency through various channels such as sensors, monitoring equipment, and personnel reports. This information exists in the form of a data stream and can reflect the occurrence, development, and changes of the event. The first event feature set refers to the set of representative and critical features extracted from these data streams. These features can help the system quickly identify and understand important information such as the nature, scale, and scope of impact of the event. For example, in a fire incident, the first event feature set includes key features such as the location of the fire source, the size of the fire, the smoke concentration, and the distribution of people.

[0067] Specifically, emergency event data streams can be acquired through a variety of methods. For example, in urban environments, real-time data can be collected using cameras, smoke sensors, temperature sensors, and other devices installed at key locations. In the transportation sector, data related to traffic accidents or congestion can be obtained through vehicle GPS data and traffic cameras. Each first event feature in the first event feature set refers to a specific information point extracted from this data, such as the characteristics of a flame image captured by a camera or the characteristics of temperature changes detected by a sensor. These features can be extracted using preset algorithms, such as image recognition algorithms for identifying flame images or temperature sensor threshold triggering algorithms for detecting abnormal temperature changes. This feature set constitutes the foundational data for the system's subsequent risk assessment and emergency decision-making.

[0068] Preferably, the operation step of obtaining the emergency event data stream can be further refined as follows: real-time data collection through a variety of sensor networks deployed in different areas, and the use of data preprocessing algorithms to clean and filter the collected raw data to remove noise data and irrelevant information to improve data quality and availability. For example, for image data, an image denoising algorithm can be used to remove noise caused by factors such as ambient light; for sensor data, abnormal fluctuations can be removed by a filtering algorithm. At the same time, for the extraction of the first event feature set, a machine learning model can be used for feature extraction, such as training a deep learning model with the raw data as input and the extracted key features as output. The training data of the model can be historical event data and its corresponding feature labels. In this way, valuable information for emergency decision-making can be extracted more accurately.

[0069] In some embodiments, the method further comprises:

[0070] In response to receiving a resource scheduling request, determining a target resource scheduling module corresponding to the resource scheduling request;

[0071] In response to determining that the target resource scheduling module has a corresponding target scheduling policy, determining a difference set of at least one scheduling policy and at least one target scheduling policy as a non-target scheduling policy group, wherein the target scheduling policy is a target response policy corresponding to the target resource scheduling module, and the scheduling policy is a response policy corresponding to the target resource scheduling module;

[0072] According to the non-target scheduling policy group, a resource allocation operation in response to the resource scheduling request is performed to obtain a resource scheduling result.

[0073] It should be noted that when the system receives a resource scheduling request, it will determine the target resource scheduling module corresponding to the request. Here, the resource scheduling request refers to a request for reallocating or adjusting resources due to reasons such as resource shortage or demand change during the emergency response process. The target resource scheduling module refers to the module in the system responsible for handling specific types of resource scheduling. It can formulate corresponding scheduling strategies based on information such as the type, quantity, and location of resources. After determining the target resource scheduling module, the system will further analyze the scheduling strategy corresponding to the module to ensure that resources can be reasonably and efficiently allocated to where they are needed.

[0074] Specifically, the resource scheduling request may come from the commanders, rescue teams, or other relevant departments at the emergency site. The content of the request includes the types of resources required, such as rescue vehicles, medical equipment, personnel, etc., the quantity, and the expected arrival time, etc. The target resource scheduling module is a preset functional module in the system, and it has different scheduling algorithms and strategies for different types of resources. For example, for the scheduling of rescue vehicles, the module will consider factors such as the current location, status, and driving speed of the vehicles; for the scheduling of medical equipment, it will consider the types, quantities, storage locations, and transportation times of the equipment. These parameters and information constitute the basic data for resource scheduling, and the system determines the optimal scheduling plan by analyzing these data.

[0075] Preferably, for the operation step of determining the target resource scheduling module, it can be further refined as follows: The system parses the key information in the resource scheduling request, such as the resource type and quantity, etc., and calls the predefined resource classification algorithm to map the request to the corresponding resource scheduling module. For example, if the request is to schedule fire trucks, the system will call the fire truck scheduling module, which stores the real-time locations, statuses, etc. of all fire trucks internally, and determines the optimal scheduling plan according to the preset scheduling algorithms, such as the shortest path algorithm or the priority algorithm. During the processing, the system will also update the allocation status of resources in real time to ensure the accuracy and timeliness of scheduling. At the same time, for the determination of the target scheduling strategy, the system will predict the resource demand and scheduling effect through a machine learning model based on historical scheduling data and real-time resource status, so as to dynamically adjust the scheduling strategy to adapt to the constantly changing emergency scenarios.

[0076] In some embodiments, the performing, according to the non-target scheduling policy group, a response resource allocation operation for the resource scheduling request to obtain a resource scheduling result includes:

[0077] In response to determining that the dynamic decomposition processing of the at least one target scheduling strategy is not completed, selecting, from the at least one target scheduling strategy, the target scheduling strategy that has completed the dynamic decomposition as the decomposed policy unit to obtain a set of decomposed policy units;

[0078] According to the non-target scheduling policy group and the decomposed policy unit set, a resource allocation operation in response to the resource scheduling request is performed to obtain a resource scheduling result.

[0079] It should be noted that after determining that a target resource scheduling module has a corresponding target scheduling policy, the system will determine the difference between at least one scheduling policy and at least one target scheduling policy as a non-target scheduling policy group. The target scheduling policy here refers to the policy that is directly related to the target resource scheduling module and is used to meet the current resource scheduling request, while the non-target scheduling policy group refers to those scheduling policies that are not directly related to the current scheduling request but may be used in other situations. In this way, the system can distinguish between the key policies and non-key policies required for the current scheduling request, thereby allocating resources more efficiently.

[0080] Specifically, the target scheduling strategy refers to the specific strategy formulated in the target resource scheduling module for a specific resource scheduling request, which includes key parameters such as the resource allocation quantity, allocation path, and allocation time. For example, in rescue vehicle scheduling, the target scheduling strategy includes the vehicle's departure time, driving route, expected arrival time, etc. The non-target scheduling strategy group refers to those strategies that are not directly related to the current scheduling request, but may be used in other situations. For example, when the scheduling request is for a rescue vehicle at a fire scene, the scheduling strategy related to medical rescue vehicles belongs to the non-target scheduling strategy group. The distinction between these strategies helps the system give priority to meeting the most urgent scheduling needs when resources are limited.

[0081] Preferably, the determination of the non-target scheduling policy group can be further refined as follows: the system matches all available scheduling policies with the current resource scheduling request through a policy matching algorithm, and calculates the similarity or correlation between each policy and the request. For example, a feature vector-based matching algorithm can be used, in which each scheduling policy and scheduling request is represented as a feature vector, including features such as resource type, quantity, and demand time. By calculating the distance or similarity between these feature vectors, the system can identify the target scheduling policy that best matches the current request and classify the remaining policies as non-target scheduling policy groups. In actual operation, the system will dynamically adjust the allocation of these policies based on the real-time resource status and the priority of the scheduling request to ensure that resources can be efficiently allocated to where they are most needed.

[0082] In some embodiments, performing a resource allocation operation in response to the resource scheduling request based on the non-target scheduling policy group and the decomposed policy unit set to obtain a resource scheduling result includes:

[0083] Select, from the at least one target scheduling policy, the target scheduling policy for which the dynamic decomposition is not completed, as the uncompleted decomposition policy unit, to obtain a set of uncompleted decomposition policy units;

[0084] Perform a response resource allocation operation for the resource scheduling request according to the non-target scheduling policy group, the set of decomposed policy units, and the set of uncompleted decomposition policy units, to obtain a resource scheduling result.

[0085] It should be noted that when the system determines that the dynamic decomposition process of the target scheduling policy is not completed, it will select the target scheduling policies for which the dynamic decomposition has been completed from the target scheduling policies, as the decomposed policy units, to obtain a set of decomposed policy units. The dynamic decomposition process of the target scheduling policy here refers to the process of decomposing a complex scheduling policy into multiple smaller and executable sub-policies, so as to more flexibly respond to the dynamic changes of emergencies. The decomposed policy unit refers to the sub-policy obtained after the decomposition process. These sub-policies can be executed independently and are more in line with the requirements of actual operations. In this way, the system can more efficiently process complex scheduling tasks and ensure the accuracy and timeliness of resource allocation.

[0086] Specifically, the target scheduling policy refers to the specific policy formulated for a specific resource scheduling request during the resource scheduling process. It may contain multiple steps or conditions and needs to be further decomposed to adapt to the actual situation. The dynamic decomposition process refers to decomposing these policies into smaller and operable sub-policies according to the real-time changes of events and the availability of resources. For example, a complex rescue vehicle scheduling policy includes multiple stages of scheduling, such as vehicle assembly, allocation, and arrival at the scene. Through dynamic decomposition, these stages can be decomposed into independent sub-policies, and each sub-policy corresponds to a specific action step. The set of decomposed policy units is the collection of these decomposed sub-policies, and they can be executed and managed independently.

[0087] Preferably, for the specific implementation of the dynamic decomposition process of the target scheduling policy, it can be further refined as follows: The system uses a dynamic decomposition algorithm to decompose the target scheduling policy into multiple sub-policies according to real-time event data and resource status. For example, a decomposition algorithm based on event characteristics and resource constraints can be used, and the input parameters include the urgency of the event, the availability and location of resources, etc. The algorithm will decompose the complex scheduling policy into a series of smaller and executable sub-policies according to these parameters. Each sub-policy will be assigned a priority and execution conditions, and the system will dynamically adjust the execution order and content of the sub-policies according to these conditions. For example, in the rescue vehicle scheduling, if the fire situation in a certain area suddenly intensifies, the system will give priority to adjusting the vehicle scheduling sub-policy in that area to ensure that resources can reach the most needed place in time.

[0088] In some embodiments, dynamically decomposing each of the at least one target response policy to generate a set of dynamic response sub-policies includes:

[0089] For each target response policy, perform the following decomposition steps:

[0090] According to the environmental interference factor corresponding to the target response policy, calculate the policy decomposition granularity; through the formula

[0091]

[0092] Determine the policy decomposition granularity, where is the real-time response capability value, is the risk complexity coefficient, t is the event duration, , , is the environmental adaptation parameter;

[0093] According to the policy decomposition granularity, create an initial cluster of response sub-policies;

[0094] Map the emergency disposal data corresponding to the target response policy to the initial cluster of response sub-policies to obtain a set of dynamic response sub-policies.

[0095] It should be noted that for each target response policy, the system calculates the policy decomposition granularity according to the environmental interference factor corresponding to the target response policy, creates an initial cluster of response sub-policies based on this, and then obtains a set of dynamic response sub-policies. Here, the environmental interference factor refers to various external factors that may affect the execution of the response policy during the emergency response process, such as weather conditions, traffic conditions, personnel distribution, etc. These factors will affect the implementation effect and execution efficiency of the response policy. The policy decomposition granularity refers to the degree of refinement when decomposing the target response policy into multiple sub-policies, which determines the size and quantity of the sub-policies. In this way, the system can flexibly adjust the response policy according to the actual situation, improving the adaptability and effectiveness of the emergency response.

[0096] Specifically, the target response strategy refers to the response strategy formulated for a specific risk assessment module in the emergency command system, which includes specific measures and steps for dealing with emergencies. The environmental interference factors refer to various external influencing factors that may be encountered when implementing these strategies. For example, in fire rescue, weather conditions such as wind direction, wind speed, and traffic conditions such as road congestion may all affect the rescue operation. The strategy decomposition granularity refers to the degree of refinement when decomposing the target response strategy into multiple sub-strategies, which determines the size and quantity of the sub-strategies. For example, if the environmental interference factors indicate complex traffic conditions, the system will choose a finer decomposition granularity to more flexibly adjust the driving routes of rescue vehicles. The initial response sub-strategy cluster refers to the set of initial sub-strategies created according to the decomposition granularity. These sub-strategies will be further adjusted and optimized according to the actual situation, and finally form a dynamic response sub-strategy set.

[0097] Preferably, the operation step of calculating the strategy decomposition granularity according to the environmental interference factors corresponding to the target response strategy can be further refined as follows: The system calculates the strategy decomposition granularity through an environmental interference factor evaluation model. The input parameters of this model include real-time environmental data such as weather conditions and traffic flow, and basic parameters of the target response strategy such as response time requirements and resource types. The model evaluates the degree of influence of environmental interference on strategy execution through preset algorithms such as rule-based algorithms or machine learning algorithms, and calculates an appropriate decomposition granularity accordingly. For example, if the model evaluation result shows that traffic conditions seriously interfere with the arrival time of rescue vehicles, the system will increase the decomposition granularity and decompose the strategy into smaller sub-strategies to more flexibly adjust the driving routes and times of the vehicles. At the same time, for the step of creating the initial response sub-strategy cluster, the system will decompose the target response strategy into multiple sub-strategies according to the calculated decomposition granularity and organize these sub-strategies into a cluster. Each sub-strategy includes specific execution steps and conditions, and the system will dynamically adjust these sub-strategies according to real-time data to ensure the efficiency and adaptability of emergency response.

[0098] In some embodiments, after mapping the emergency disposal data corresponding to the target response strategy to the initial response sub-strategy cluster to obtain the dynamic response sub-strategy set, the method further includes:

[0099] Performing strategy binding on the response parameter set corresponding to the dynamic response sub-strategy set and the risk assessment parameters corresponding to the first risk assessment module, and disabling the target response strategy.

[0100] It should be noted that after the generation of the dynamic response sub - policy set is completed, the system will perform policy binding on the response parameter set corresponding to these sub - policy sets and the risk assessment parameters corresponding to the first risk assessment module, and disable the target response policy. Here, the response parameter set refers to the specific parameter set included in each sub - policy in the dynamic response sub - policy set. These parameters define the execution details of the sub - policy, such as the quantity of resource allocation, execution time, etc. The risk assessment parameters refer to the parameters associated with the first risk assessment module, which are used to evaluate the risk level and impact scope of an event. Through policy binding, the system ensures that the execution of each sub - policy matches the risk assessment result, thereby improving the pertinence and effectiveness of emergency response. Disabling the target response policy is to avoid the reuse or misuse of these policies in the subsequent process and ensure the orderly progress of emergency response.

[0101] Specifically, the dynamic response sub - policy set refers to a set of sub - policies obtained after policy decomposition, and each sub - policy contains specific execution steps and parameters. The response parameter set refers to the set of all parameters involved in these sub - policies. For example, in a rescue operation, the parameters include the departure time of the rescue team, the estimated arrival time, the rescue equipment carried, etc. The first risk assessment module refers to the module in the system used to evaluate the risk level of emergencies, which generates risk assessment results based on the characteristics of the event and real - time data. The risk assessment parameters refer to the specific parameters used to measure the risk in these assessment results, such as the risk level, impact scope, estimated loss, etc. By performing policy binding on the response parameter set and the risk assessment parameters, the system ensures that the execution of each sub - policy matches the current risk situation, thereby improving the scientificity and effectiveness of emergency response.

[0102] Preferably, for the operation step of policy binding, it can be further refined as follows: The system associates the response parameters of each sub - policy in the dynamic response sub - policy set with the risk assessment parameters of the first risk assessment module through a policy - binding algorithm. For example, the algorithm can adjust the resource allocation parameters in the sub - policy based on the risk level. If the risk level is high, the quantity of resource allocation is increased or the resource type is adjusted. At the same time, for the step of disabling the target response policy, after the policy binding is completed, the system marks the target response policy as disabled through a status - marking mechanism. In this way, the system will no longer consider these decomposed and bound policies during the subsequent emergency response process, thus avoiding repeated scheduling or conflicts. In addition, the system will record the relevant information of these disabled policies for auditing and review when needed to ensure the transparency and traceability of the emergency response process.

[0103] In some embodiments, before generating the emergency response instruction for the first risk assessment module according to the at least one dynamic response sub - policy set, the method further includes:

[0104] In response to receiving the secondary event data stream, determine the risk analysis cluster corresponding to the secondary event data stream as the second risk analysis cluster, where the secondary event data stream represents event data that occurs after the first event feature set;

[0105] In response to determining that there is a target initial response strategy in the second initial response strategy set, select the target initial response strategy from the second initial response strategy set to obtain at least one target initial response strategy, where each second initial response strategy is the initial response strategy for the second event feature subset to be processed corresponding to the second risk analysis cluster, the second event feature subset is a subset of the second event feature set corresponding to the secondary event data stream, and the target initial response strategy is the target response strategy corresponding to the first risk analysis cluster that has not been dynamically decomposed;

[0106] Determine the difference set between the second initial response strategy set and the at least one target initial response strategy as the non-target initial response strategy set;

[0107] For each target initial response strategy, input the second event feature subset corresponding to the target initial response strategy into the target initial response strategy and the corresponding initial response sub-strategy cluster;

[0108] For each non-target initial response strategy, input the second event feature subset corresponding to the non-target initial response strategy into the non-target initial response strategy.

[0109] It should be noted that before generating the emergency response instruction for the first risk assessment module, the system will receive the secondary event data stream and determine the corresponding second risk analysis cluster. Here, the secondary event data stream refers to the data stream of other related events triggered by the initial event after the initial event occurs, such as the data of secondary disasters such as fires or tsunamis that may be triggered after an earthquake. The second risk analysis cluster refers to a set of modules for risk assessment of these secondary event data streams, which can analyze the risk characteristics of secondary events and generate corresponding initial response strategies. In this way, the system can comprehensively consider the initial event and the secondary events it triggers, so as to formulate a more comprehensive emergency response strategy.

[0110] Specifically, the secondary event data stream refers to the new event data stream generated after the initial event occurs due to its impact. These data streams contain the characteristics and relevant information of the secondary events. For example, in a fire incident, the secondary events are the collapse of buildings or the entrapment of people caused by the fire. The second risk analysis cluster refers to the set of modules in the system used to analyze the risks of these secondary events. It contains multiple risk assessment modules, each of which performs risk assessment on a specific type of secondary event. The second initial response strategy set refers to the set of initial response strategies generated for secondary events. These strategies are formulated based on the characteristics of secondary events and risk assessment results. The target initial response strategy refers to the target response strategy among these initial response strategies that corresponds to the first risk analysis cluster and has not completed dynamic decomposition. By inputting the feature subset of the secondary event into the corresponding response strategy and initial response sub-strategy cluster, the system can dynamically adjust the emergency response strategy to deal with complex event scenarios.

[0111] Preferably, the step of determining the second risk analysis cluster corresponding to a secondary event data stream in response to receiving the secondary event data stream can be further refined as follows: the system uses a secondary event detection module to monitor secondary events triggered by the initial event in real time and collect relevant data streams. This module uses a preset secondary event recognition algorithm, such as a threshold detection algorithm or a pattern matching algorithm based on event characteristics, to identify the occurrence of a secondary event. Once a secondary event data stream is detected, the system invokes the corresponding second risk analysis cluster, which contains multiple risk assessment modules for different types of secondary events. Each module performs a risk assessment based on the secondary event's characteristics, such as event type, impact range, and risk level, and generates a corresponding initial response strategy. For example, for a building collapse secondary event caused by a fire, the system invokes a building collapse risk assessment module. This module considers factors such as the building's structure and occupant distribution to generate an initial response strategy for rescue and evacuation. The system also compares and integrates these initial response strategies with the target response strategies of the first risk analysis cluster to ensure a comprehensive and coordinated emergency response.

[0112] In some embodiments, after filtering out a target response strategy from the at least one response strategy corresponding to the first risk assessment module to obtain at least one target response strategy, the method further includes:

[0113] In response to detecting that the target response strategy does not exist in the at least one response strategy, the at least one response strategy and the corresponding at least one emergency handling plan are determined as an emergency response instruction.

[0114] It should be noted that after screening out the target response strategy from at least one response strategy corresponding to the first risk assessment module, if it is detected that there is no target response strategy that meets the preset threshold conditions among these response strategies, the system will determine all response strategies and corresponding emergency disposal plans as emergency response instructions. The preset threshold conditions here refer to the standards pre-set by the system for evaluating whether the response strategy is effective enough, such as whether the response timeliness index reaches a certain specific value. The emergency disposal plan refers to a set of detailed disposal measures and processes prepared in advance for a specific type of event, which includes the action steps to be taken when there is no suitable target response strategy. In this way, the system can ensure that effective emergency response instructions can be generated in any case, even if there is no target response strategy that fully meets the conditions.

[0115] Specifically, the target response strategy refers to the strategy that meets the preset threshold conditions after screening among the response strategies corresponding to the first risk assessment module. These strategies are usually based on the characteristics of the event and the risk assessment results, and can effectively respond to emergencies. The preset threshold conditions refer to the standards pre-set by the system to evaluate whether the response strategy is effective enough, such as whether the response time index reaches a certain value, or whether the resource allocation meets the minimum requirements. The emergency response plan refers to a set of detailed handling measures and processes prepared in advance for a specific type of event. It includes action steps to be taken when there is no suitable target response strategy, such as personnel evacuation, emergency rescue, etc. These plans are usually formulated by emergency experts based on historical data and experience, and stored in the system for easy access at any time.

[0116] Preferably, the operation step of detecting that the target response strategy does not exist in at least one of the response strategies can be further refined as follows: the system evaluates each response strategy through a response strategy evaluation module to check whether it meets the preset threshold conditions. The module calculates the response time efficiency index of each response strategy, for example, by simulating the execution time of the response strategy and comparing it with the preset time efficiency threshold. If the response time efficiency index of no response strategy reaches the preset threshold, the system will trigger a backup mechanism to combine all response strategies with the corresponding emergency response plans to generate a comprehensive emergency response instruction. For example, in a complex chemical leak incident, if the system finds after evaluation that no single response strategy can effectively control the leak within the specified time, it will combine multiple response strategies, such as blocking the area, evacuating personnel, cleaning up the leak, etc., with pre-established emergency response plans, such as medical rescue and environmental monitoring, to generate a comprehensive emergency response instruction to ensure that the incident can be handled promptly and effectively.

[0117] In some embodiments, generating an emergency response instruction for the first risk assessment module based on the at least one dynamic response sub-strategy set includes:

[0118] In response to determining that there are critical undecomposed response strategies among the at least one response strategy, select the critical undecomposed response strategies from the at least one response strategy to obtain at least one critical undecomposed response strategy;

[0119] Generate an emergency response instruction for the first risk assessment module according to the at least one critical undecomposed response strategy and the at least one set of dynamic response sub-strategies.

[0120] It should be noted that when generating an emergency response instruction for the first risk assessment module, the system will check whether there are critical undecomposed response strategies. If there are, the system will select these critical undecomposed response strategies from the response strategies and generate a final emergency response instruction in combination with the set of dynamic response sub-strategies. The critical undecomposed response strategies here refer to those strategies that have not been decomposed into sub-strategies during the dynamic decomposition process but are crucial for emergency response. These strategies need to be processed separately due to their complexity or importance to ensure the comprehensiveness and effectiveness of emergency response.

[0121] Specifically, the critical undecomposed response strategy refers to a response strategy that, for some reasons such as complexity, importance, or particularity, has not been decomposed into smaller sub-strategies during the dynamic decomposition process. These strategies usually include critical emergency measures such as specific rescue operations or resource allocation. The set of dynamic response sub-strategies refers to the set of sub-strategies obtained after dynamic decomposition processing, and these sub-strategies are more flexible and more adaptable to the real-time changing emergency needs. For example, in a large-scale fire incident, the critical undecomposed response strategy is to call heavy fire-fighting equipment, and the set of dynamic response sub-strategies includes specific measures such as evacuating the crowd and setting up isolation zones. By combining these two strategies, the system can generate a comprehensive emergency response instruction to ensure that critical measures are implemented while other relevant measures can also be flexibly adjusted to adapt to the development of the incident.

[0122] Preferably, the operation step of determining that there is a critical undecomposed response strategy in the at least one response strategy can be further refined as follows: The system uses a critical strategy identification module to evaluate all response strategies and identify those strategies that have not been decomposed and are crucial for emergency response. This module analyzes the characteristics and importance of each response strategy according to preset rules or models. For example, the system can use a rule-based model, where the rules define which types of strategies are regarded as critical strategies, such as strategies involving critical infrastructure protection or mass evacuation of people. For the step of generating an emergency response instruction for the first risk assessment module, the system integrates the identified critical undecomposed response strategies with the dynamic response sub-strategy set. The integration process can be achieved through an instruction generation algorithm, which adjusts the execution order and parameters of the strategies according to the real-time data of the event and the risk assessment results to generate the final emergency response instruction. For example, in a natural disaster event, the system combines critical undecomposed response strategies, such as calling large rescue equipment, with the dynamic response sub-strategy set, such as evacuating people in phases, to generate an orderly and comprehensive emergency response instruction to ensure that the emergency response can be carried out efficiently and orderly in a complex and changeable event scenario.

[0123] The above embodiments of the present invention have the following beneficial effects:

[0124] 1. By establishing an intelligent matching mechanism between the risk assessment module and the dynamic response strategy, the problem of the disconnection between the strategy and the event characteristics in traditional emergency response is solved. This method can automatically generate a risk analysis cluster according to the first event feature set and screen the optimal target response strategy based on timeliness, significantly improving the adaptability of emergency decision-making to the characteristics of emergencies and avoiding the lag and subjective errors of manual matching.

[0125] 2. The dynamic decomposition processing mechanism is adopted to calculate the strategy granularity in combination with the environmental interference factor, overcoming the technical defect of poor adaptability of fixed strategies in complex scenarios. By calculating the decomposition granularity formulaically and generating a dynamic response sub-strategy set, the real-time refinement and dynamic adjustment of emergency strategies are realized, ensuring that the response plan can be continuously optimized with the evolution of the event and the change of the environment, and improving the accuracy and execution efficiency of response measures.

[0126] 3. Through the difference set operation of resource scheduling and non-target strategies and the linkage processing of secondary events, the problems of resource conflicts and response blind spots in multi-task concurrency are solved. The system can automatically identify undecomposed strategies, completed decomposed strategies, and secondary event-related strategies, and through a differential resource allocation and strategy binding mechanism, achieve efficient resource scheduling and multi-level risk coordinated disposal, avoiding response omissions or resource waste.

[0127] Such as Figure 2As shown in the figure, an AI-based intelligent emergency command system according to some embodiments includes:

[0128] A data acquisition module 201 for collecting an emergency event data stream in real time, where the emergency event data stream includes a first event feature set;

[0129] A risk assessment cluster module 202 for determining a first risk analysis cluster according to the risk assessment modules corresponding to each first event feature in the first event feature set;

[0130] A policy screening module 203 for performing the following operations on each first risk assessment module in the first risk analysis cluster:

[0131] Screening a target response policy from at least one response policy corresponding to the first risk assessment module, where the response timeliness index of the target response policy meets a preset threshold condition;

[0132] A dynamic decomposition module 204 for performing dynamic decomposition processing on each target response policy to generate a dynamic response sub-policy set;

[0133] An instruction generation module 205 for generating an emergency response instruction for the first risk assessment module according to the dynamic response sub-policy set.

[0134] It can be understood that the modules described in the AI-based intelligent emergency command system correspond to the steps in the AI-based intelligent emergency command method described in the reference. Figure 1 Therefore, the operations, features, and beneficial effects described above for the AI-based intelligent emergency command method also apply to the AI-based intelligent emergency command system and the modules included therein, and will not be elaborated here.

[0135] Next, refer to Figure 3 , which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic device in some embodiments of the present invention may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0136] As Figure 3As shown, electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage device 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for the operation of electronic device 300. Processing device 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0137] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0138] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a terminal device such as a computer, server, mobile phone, or tablet.

[0139] The above description is only some preferred embodiments of the present invention and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) disclosed in the embodiments of the present invention that have similar functions.

Claims

1. An intelligent emergency command method based on artificial intelligence, comprising: Obtaining an emergency event data stream, wherein the emergency event data stream includes a first event feature set; Determining a risk analysis cluster as a first risk analysis cluster according to the risk assessment modules corresponding to each first event feature in the first event feature set, wherein there is at least one corresponding initial response strategy set for each risk assessment module; For each first risk assessment module in the first risk analysis cluster, perform the following emergency decision-making steps: Screen out target response strategies from at least one response strategy corresponding to the first risk assessment module to obtain at least one target response strategy, wherein the target response strategy is a response strategy whose response time limit index meets the preset threshold condition, and the at least one response strategy is evolved from a first event feature subset to the at least one initial response strategy set, and the first event feature subset corresponds to the first risk assessment module; Perform dynamic decomposition processing on each target response strategy in the at least one target response strategy to generate a dynamic response sub-strategy set, and obtain at least one dynamic response sub-strategy set; Generate an emergency response instruction for the first risk assessment module according to the at least one dynamic response sub-strategy set.

2. The method according to claim 1, wherein The method further includes: In response to receiving a resource scheduling request, determining a target resource scheduling module corresponding to the resource scheduling request; In response to determining that there is a corresponding target scheduling strategy for the target resource scheduling module, determining the difference set between at least one scheduling strategy and at least one target scheduling strategy as a non-target scheduling strategy group, wherein the target scheduling strategy is a target response strategy corresponding to the target resource scheduling module, and the scheduling strategy is a response strategy corresponding to the target resource scheduling module; Perform a response resource allocation operation for the resource scheduling request according to the non-target scheduling strategy group to obtain a resource scheduling result.

3. The method according to claim 2, wherein The performing a response resource allocation operation for the resource scheduling request according to the non-target scheduling strategy group to obtain a resource scheduling result includes: In response to determining that the dynamic decomposition processing of the at least one target scheduling strategy is not completed, selecting the target scheduling strategies that have completed dynamic decomposition from the at least one target scheduling strategy as decomposed strategy units to obtain a decomposed strategy unit set; Perform a response resource allocation operation for the resource scheduling request according to the non-target scheduling strategy group and the decomposed strategy unit set to obtain a resource scheduling result.

4. The method according to claim 3, wherein, The performing a response resource allocation operation for the resource scheduling request according to the non-target scheduling strategy group and the decomposed strategy unit set to obtain a resource scheduling result includes: Selecting the target scheduling strategies that have not completed dynamic decomposition from the at least one target scheduling strategy as uncompleted decomposed strategy units to obtain an uncompleted decomposed strategy unit set; Perform a response resource allocation operation for the resource scheduling request according to the non-target scheduling strategy group, the decomposed strategy unit set and the uncompleted decomposed strategy unit set to obtain a resource scheduling result.

5. The method according to claim 1, wherein Performing dynamic decomposition processing on each of the at least one target response policy to generate a dynamic response sub-policy set, including: For each target response policy, perform the following decomposition processing steps: Calculate the policy decomposition granularity according to the environmental interference factor corresponding to the target response policy; through the formula Determine the policy decomposition granularity, where is the real-time response capability value, is the risk complexity coefficient, t is the event duration, , , is the environment adaptation parameter; Create an initial response sub-policy cluster according to the policy decomposition granularity; Map the emergency response data corresponding to the target response policy to the initial response sub-policy cluster to obtain a dynamic response sub-policy set.

6. The method according to claim 5, wherein, After mapping the emergency response data corresponding to the target response policy to the initial response sub-policy cluster to obtain a dynamic response sub-policy set, the method further includes: Performing policy binding on the response parameter set corresponding to the dynamic response sub-policy set and the risk assessment parameters corresponding to the first risk assessment module, and disabling the target response policy.

7. The method according to claim 1, wherein, Before generating an emergency response instruction for the first risk assessment module according to the at least one dynamic response sub-policy set, the method further includes: In response to receiving a secondary event data stream, determining a risk analysis cluster corresponding to the secondary event data stream as a second risk analysis cluster, where the secondary event data stream represents event data that occurs after the first event feature set; In response to determining that there is a target initial response policy in the second initial response policy set, selecting the target initial response policy from the second initial response policy set to obtain at least one target initial response policy, where each second initial response policy is an initial response policy for a second event feature subset to be processed corresponding to the second risk analysis cluster, the second event feature subset is a subset of the second event feature set corresponding to the secondary event data stream, and the target initial response policy is a target response policy that has not been dynamically decomposed corresponding to the first risk analysis cluster; Determining the difference set between the second initial response policy set and the at least one target initial response policy as a non-target initial response policy set; For each target initial response policy, input the second event feature subset corresponding to the target initial response policy into the target initial response policy and the corresponding initial response sub-policy cluster; For each non-target initial response policy, input the second event feature subset corresponding to the non-target initial response policy into the non-target initial response policy.

8. The method according to claim 1, wherein, After screening out target response policies from at least one response policy corresponding to the first risk assessment module to obtain at least one target response policy, the method further includes: In response to detecting that there is no target response policy in the at least one response policy, determining the at least one response policy and the corresponding at least one emergency response plan as an emergency response instruction.

9. The method according to claim 1, wherein Generating an emergency response instruction for the first risk assessment module according to the at least one dynamic response sub-policy set includes: In response to determining that there is a critical undecomposed response policy in the at least one response policy, selecting the critical undecomposed response policy from the at least one response policy to obtain at least one critical undecomposed response policy; Generate an emergency response instruction for the first risk assessment module according to the at least one key undissolved response strategy and the at least one set of dynamic response sub-strategies.

10. An emergency command system based on artificial intelligence, characterized in that, Comprising a data acquisition module for collecting an emergency event data stream in real time, the emergency event data stream including a first event feature set; a risk assessment cluster module for determining a first risk analysis cluster according to the risk assessment modules corresponding to each first event feature in the first event feature set; a policy screening module for performing the following operations on each first risk assessment module in the first risk analysis cluster: screen a target response policy from at least one response policy corresponding to the first risk assessment module, the response time limit index of the target response policy satisfying a preset threshold condition; a dynamic decomposition module for performing dynamic decomposition processing on each target response policy to generate a set of dynamic response sub-strategies; an instruction generation module for generating an emergency response instruction for the first risk assessment module according to the set of dynamic response sub-strategies.

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