Panoramic situation intelligent emergency management and control system based on visual AI large model

Abstract

The invention relates to the technical field of environment monitoring, and discloses a panoramic situation intelligent emergency management and control system based on a visual AI large model. The system obtains multi-dimensional data information of a global emergency scene through a data sensing module by using a multi-source sensing device, wherein the multi-dimensional data information contains images, videos, environmental parameters and the like; the model processing module pre-processes the multi-dimensional data, and inputs the pre-processed multi-dimensional data into a visual AI large model to complete feature extraction and situation analysis; the situation deduction module constructs a dynamic situation map of the emergency scene based on an analysis result, and visually presents an event development situation; the instruction generation module generates an emergency management and control instruction according to the map deduction conclusion; and the execution feedback module issues the instruction to an execution terminal and collects feedback data in the execution process in real time, so that closed-loop optimization of the control effect is realized. According to the system, multi-source data can be efficiently integrated, accurate situation analysis and intelligent management and control are realized through a visual AI large model, the emergency response efficiency and the management and control accuracy are remarkably improved, and the system is suitable for various complex emergency scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental monitoring, in particular to a panoramic situation intelligent emergency management and control system based on a visual AI large model. BACKGROUND

[0002] In the field of environmental monitoring, sensors are the core components for obtaining environmental data, and their performance directly affects the accuracy and reliability of the monitoring results. Traditional environmental monitoring sensors have many problems to be solved in practical applications. The existing sensors have poor environmental adaptability. In complex working conditions such as high temperature, high humidity, and strong corrosion, the sensitive elements of the sensor are prone to performance degradation, resulting in measurement data deviation. For example, in the waste gas monitoring scene of a chemical enterprise, the environment containing acidic gases can corrode the electrode material of the sensor, prolong the response time of the sensor, and reduce the measurement accuracy, making it impossible to accurately reflect the changes in waste gas composition in real time.

[0003] The power consumption problem of traditional sensors is more prominent. Most of them use wired power supply or large-capacity battery power supply. In the scene of field monitoring without fixed power supply, it is necessary to frequently replace the battery or maintain the cable, which not only increases the monitoring cost, but also may cause data interruption due to power interruption. For example, in the monitoring of farmland soil moisture, the high power consumption of traditional sensors makes it difficult for them to work stably for a long time in remote farmland, and it is necessary to replace the battery regularly by manual work, which is particularly inconvenient in large-area farmland monitoring.

[0004] The data transmission stability of existing sensors also has deficiencies. When some sensors use wireless transmission, they are greatly affected by environmental electromagnetic interference, and packet loss and bit error may occur during data transmission, resulting in inaccurate information received by the monitoring system. In the city noise monitoring network, due to the complex electromagnetic environment of the city, the wireless transmission module of the traditional sensor often experiences data transmission interruption due to electromagnetic interference, affecting the real-time monitoring and early warning of noise pollution. At the same time, the integration of traditional sensors is low, and each functional module is relatively independent, resulting in a large volume of the sensor, which requires a lot of space for installation and deployment. In some scenes with strict space limitations (such as small weather stations and unmanned aerial vehicle environmental monitoring equipment), it is difficult to meet the actual application requirements. Moreover, the calibration process of existing sensors is complicated, and professional personnel need to carry calibration equipment for on-site calibration regularly, which not only increases labor costs, but also may cause gradual accumulation of measurement errors due to untimely calibration, affecting the reliability of long-term monitoring data. SUMMARY

[0005] The present application relates to the technical field of environmental monitoring, in particular to a panoramic situation intelligent emergency management and control system based on a visual AI large model.

[0006] In order to achieve the above object, the application provides a panoramic situation intelligent emergency management and control system based on a visual AI large model, which comprises: A data perception module acquires multi-dimensional data information of a global emergency scene through a multi-source perception device; A model processing module pre-processes the acquired multi-dimensional data information and inputs the visual AI large model to perform feature extraction and situation analysis; A situation deduction module constructs a dynamic situation atlas of the emergency scene based on the analysis result output by the visual AI large model; An instruction generation module generates corresponding emergency management and control instructions according to the deduction conclusion of the dynamic situation atlas; An execution feedback module issues the emergency management and control instructions to an execution terminal and collects feedback data of the execution process.

[0007] Preferably, the data perception module adopts a distributed sensor network structure, and each sensor node can adjust the perception parameters to adapt to the monitoring requirements of different emergency scenes. The multi-dimensional data information of the global emergency scene acquired through the multi-source perception device is specifically as follows: each sensor node collects image, audio and environmental parameter data according to a preset period, and transmits the data to a central processing unit after preliminary screening by an edge computing device.

[0008] Preferably, the model processing module comprises a data preprocessing unit and a large model analysis unit; the data preprocessing unit performs format unification and noise filtering on the multi-dimensional data information to obtain standardized input data; and the large model analysis unit processes the standardized input data through an end-to-end processing method to extract target feature information containing scene elements and event association relationships. The end-to-end processing method specifically comprises: inputting the standardized input data; converting the standardized input data into a tensor form recognizable by the large model; enhancing the features of key areas through an attention mechanism and performing multi-scale feature fusion; and suppressing redundant information interference through a dynamic mask technology to obtain the final feature analysis result.

[0009] Preferably, the model processing module comprises a feature extraction unit; the feature extraction unit is used to extract multi-dimensional features from the target feature information. The extracted multi-dimensional features include spatio-temporal features, semantic features, context association features, event evolution features and key element saliency features. The spatio-temporal features include the spatial distribution range and the time series change rate of each element in the emergency scene. The semantic features are used to describe the category attributes and mutual relationships of entity objects in the scene. The context association feature is to identify the development context of the event through front and back frame data association analysis, and is used to reflect the dynamic association of the scene. The event evolution feature is used to reflect the development trend and potential impact of different emergency events. The key element significance feature is a prominent representation of key objects in a complex scene, with scene adaptability and multi-modal compatibility, and is used to reflect the importance of core elements.

[0010] Preferably, the model processing module further comprises a model optimization unit and a situation index acquisition unit; the model optimization unit optimizes the parameters of the visual AI large model using a transfer learning method, and classifies the extracted target feature information using the optimized and trained visual AI large model to identify the specific situation type of the emergency scene; the situation index acquisition unit quantitatively analyzes the key situation elements of each scene area, and acquires the situation index of the emergency scene according to the quantitative analysis result and the scene coverage range.

[0011] Preferably, the transfer learning method is used to optimize the parameters of the visual AI large model, and the specific process is as follows: According to the preset source domain data set size, a pre-trained model highly related to the target emergency scene is selected as an initial model; The initial model is fine-tuned using a target domain small sample data set, the classification accuracy of the model is calculated through a cross-entropy loss function, and the classification accuracy is used as an evaluation basis for model optimization; According to the evaluation basis, the adjustment direction and step of the model parameters are updated; Repeat the parameter adjustment and evaluation process until the convergence condition is met, and output the optimized visual AI large model parameter combination, which includes the weight coefficient of the feature extraction layer and the bias parameter of the classification layer.

[0012] Preferably, the visual AI large model takes historical emergency scene data as input and scene situation type labels as output during training; During the training process, the contrast learning enhancement algorithm is used to enhance the ability to distinguish similar scenes, and the adversarial training is used to improve the robustness of the algorithm to noise data; The inference process of the visual AI large model is as follows: Input the target feature information to be analyzed; generate a high-dimensional feature vector through the feature extraction layer; perform linear transformation and activation function processing on the high-dimensional feature vector through the classification layer; output the situation type probability distribution of the emergency scene, and the class with the highest probability is the final identified situation type.

[0013] Preferably, the situation deduction module is used to construct a dynamic situation map of the emergency scene based on the analysis result output by the visual AI large model, and the specific process is as follows: set a primary situation threshold range, a medium situation threshold range and a high situation threshold range; the primary situation threshold range is within the medium situation threshold range; the medium situation threshold range is within the high situation threshold range; if the situation index of the current scene is within the primary situation threshold range, the emergency scene is in a controllable state; if the situation index of the current scene exceeds the upper limit value of the primary situation threshold range, the emergency scene has a risk expansion trend; if the situation index of the current scene is lower than the lower limit value of the primary situation threshold range, the emergency scene has information missing or monitoring blind area; if the situation index of the current scene is outside the primary situation threshold range and within the medium situation threshold range, the risk level of the emergency scene is a pre-warning level; if the situation index of the current scene is outside the medium situation threshold range and within the high situation threshold range, the risk level is an alert level, and if the situation index of the current scene is outside the high situation threshold range, the risk level is an emergency level.

[0014] Preferably, the data perception module is used to acquire multi-dimensional data information of the global emergency scene through multi-source perception equipment, and the deployment position of the perception equipment is located at a key node, a key protection area and an event high incidence area of the emergency scene, and each deployment position is assigned a unique geographic identification code.

[0015] Preferably, the execution feedback module is used to issue an emergency control instruction to an execution terminal and collect feedback data of an execution process, and the collection position of the feedback data is a starting point, an ending point, a key operation node and an effect verification area of an instruction execution area.

[0016] Compared with the prior art, the present application has the following advantages: The present application acquires multi-dimensional data information of the global emergency scene through multi-source perception equipment, and uses a visual AI large model for feature extraction and situation analysis, thereby improving the monitoring coverage and data integration efficiency of complex scenes. The distributed sensor network structure can dynamically adjust the perception parameters according to different emergency scenes, thereby ensuring the flexibility and adaptability of data acquisition. The edge computing equipment performs preliminary screening on the data, thereby reducing the load of the central processing unit and improving the system response speed.

[0017] The data preprocessing unit generates standardized input data through format unification and noise filtering, thereby providing high-quality input for subsequent model analysis. The large model analysis unit adopts an end-to-end processing method, combines an attention mechanism and a multi-scale feature fusion technology, and can accurately extract scene elements and event correlation. The application of dynamic mask technology effectively suppresses redundant information interference and enhances the feature representation ability of key areas.

[0018] The feature extraction unit extracts multi-dimensional features, including spatiotemporal, semantic, and contextual features, from target feature information to comprehensively characterize the dynamic changes in emergency scenarios. The introduction of event evolution features and key element salience features further enhances the system's analytical depth and adaptability to complex scenarios. The model optimization unit improves the classification accuracy and generalization capabilities of the large-scale visual AI model in target emergency scenarios through transfer learning and small-sample fine-tuning.

[0019] The situational deduction module implements multi-level risk warnings based on dynamic situation maps. It can assess scenario status in real time based on situation indicators, providing intuitive support for emergency decision-making. The instruction generation module rapidly generates control instructions that match the deduction conclusions, and through the execution feedback module, forms a closed-loop optimization mechanism. Feedback data collection covers the entire instruction execution process, providing a reliable data foundation for dynamic system adjustments and continuous optimization.

[0020] This invention intelligently integrates the entire emergency management process, from data collection to command execution, addressing issues such as delayed response and single-minded decision-making in traditional systems, thereby improving the efficiency and accuracy of emergency management. The system's adaptive capabilities and multimodal compatibility make it widely applicable to emergency scenarios in areas such as natural disasters, public safety, and urban management. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a working principle diagram of the panoramic situation intelligent emergency management and control system based on the visual AI large model described in the present invention; Figure 2 Flowchart of end-to-end processing of the model processing module; Figure 3 Flowchart for model optimization and situation indicator acquisition; Figure 4 Flowchart for training and reasoning large visual AI models. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0023] See also Figures 1-4 The present invention provides a panoramic situation intelligent emergency management and control system based on a large visual AI model. The system includes: a data perception module, a model processing module, a situation deduction module, an instruction generation module, and an execution feedback module. The specific implementation steps are as follows: The data perception module obtains multi-dimensional data information of the global emergency scene through multi-source perception devices. The model processing module pre-processes the obtained multi-dimensional data information and inputs it into a visual AI large model for feature extraction and situation analysis. The situation deduction module constructs a dynamic situation atlas of the emergency scene based on the analysis results output by the visual AI large model. The instruction generation module generates corresponding emergency control instructions according to the deduction conclusions of the dynamic situation atlas. The execution feedback module issues the emergency control instructions to the execution terminal and collects feedback data of the execution process.

[0024] Embodiment 1: The data perception module adopts a distributed sensor network structure, in which each sensor node has the function of adjusting perception parameters to adapt to the monitoring needs of different emergency scenes. In actual application, each sensor node will work according to a pre-set period to collect data such as images, audio and environmental parameters in the emergency scene. The pre-set period is not fixed but can be flexibly adjusted according to the specific characteristics and monitoring needs of the emergency scene, so as to ensure that the collected data can meet the requirements of subsequent processing and analysis.

[0025] After the data is collected, it will be preliminarily screened by the edge computing device. This operation of the edge computing device plays an important role, as it can filter out some data that is obviously not required or redundant before data transmission, reducing the amount of data transmission and thus improving the efficiency of data transmission and the working efficiency of the subsequent central processing unit. The data that has been preliminarily screened will be transmitted to the central processing unit for further processing and analysis.

[0026] In the emergency scene, the deployment positions of the perception devices have clear regulations, and they are deployed at key nodes, key protection areas and high-event areas. The selection of these positions is based on a comprehensive analysis and judgment of the emergency scene, which can ensure that the perception devices can maximize the acquisition of valuable information. Each deployment position is assigned a unique geographic identification code, which makes the position of each perception device accurately identifiable and locatable, providing convenience for subsequent data processing, analysis, and generation and execution of emergency control instructions.

[0027] Through the design of this distributed sensor network structure, as well as reasonable perception device deployment and data collection and processing methods, comprehensive and real-time monitoring of the global emergency scene can be achieved. Whether it is a large public place, a complex industrial production area, or other types of emergency scenes, this data perception module can adjust the perception parameters of the sensor nodes according to the different characteristics of the scene to obtain accurate and effective multi-dimensional data information.

[0028] During data collection, image data can intuitively reflect the positions and states of personnel and objects in the emergency scene; audio data can capture abnormal sounds in the scene, such as distress signals and explosion sounds; environmental parameter data includes temperature, humidity, air pressure, etc. The comprehensive collection of these data provides rich raw data for subsequent analysis and judgment of the situation of the emergency scene.

[0029] The preliminary screening of data by the edge computing device is performed according to preset screening rules. These rules can be set according to different emergency scenes and data types, for example, for image data, images that are unclear or have too low resolution can be filtered out; for environmental parameter data, data that is outside the normal range or obviously incorrect can be filtered out. Through this preliminary screening, the data transmitted to the central processing unit can have high quality and usability.

[0030] After receiving the data that has undergone preliminary screening, the central processing unit further integrates and processes the data to provide reliable input data for the subsequent model processing module. The workflow of the entire data perception module is closely linked, and each link cooperates with each other to ensure that data collection, transmission and preliminary processing can be efficiently and accurately performed.

[0031] In addition, the distributed sensor network structure also has strong scalability. When the scope of the emergency scene expands or the monitoring demand increases, new demands can be met by increasing sensor nodes without the need for large-scale changes to the structure of the entire system. At the same time, the independent working ability of each sensor node also enables the system to maintain a certain monitoring ability when some nodes fail, improving the reliability and stability of the system.

[0032] The uniqueness of the geographic identification code enables accurate knowledge of the location corresponding to each data when processing and analyzing the data, thereby better understanding the situation in different areas of the emergency scene. For example, in the event of a sudden event, the geographic identification code can be used to quickly locate the specific location of the event and retrieve the data collected by the perception devices near the location, providing timely and accurate information support for emergency decision-making.

[0033] The data perception module realizes efficient acquisition of multi-dimensional data information of the global emergency scene through the distributed sensor network structure, reasonable deployment of perception devices, flexible adjustment of perception parameters, standardized data collection and preliminary processing procedures, and unique geographic identification code settings, providing a solid data foundation for the normal operation of the entire panoramic situation intelligent emergency management and control system based on the visual AI large model.

[0034] Embodiment 2: The model processing module includes a data preprocessing unit and a large model analysis unit. The data preprocessing unit performs format unification and noise filtering operations on multi-dimensional data information to obtain standardized input data. In actual processing, the data collected by multi-source perception devices may have different formats, such as different resolutions and format types of image data, different sampling rates of audio data, etc. The data preprocessing unit needs to uniformly convert these different format data to make them conform to the standard format that can be recognized and processed by the visual AI large model. At the same time, the collected data may contain various noises, such as image noise caused by electromagnetic interference in the environment, noise in audio, etc. Noise filtering operation is to remove these interference factors and improve the quality of data.

[0035] The large model analysis unit processes the standardized input data through an end-to-end processing method to extract target feature information containing scene element and event association relationship. The specific process of the end-to-end processing method is: input the standardized input data, and convert these standardized input data into a tensor form that can be recognized by the large model. Tensor is a multi-dimensional array that can well represent the characteristics and structure of data. Converting data into tensor form can make the visual AI large model better process and analyze it.

[0036] The attention mechanism is used to enhance the features of key regions and perform multi-scale feature fusion. The attention mechanism is like human visual attention, which can make the model focus on the key parts of the data and ignore unimportant information. In emergency scene data, only part of the area may contain key scene elements and event information. The attention mechanism can automatically identify these key areas and enhance their feature representation. Multi-scale feature fusion considers that data at different scales may contain different useful information. For example, in image data, large scales can see the overall scene structure, and small scales can see detailed features. By fusing features at different scales, the scene can be described more comprehensively.

[0037] Dynamic mask technology is used to suppress redundant information interference and obtain the final feature analysis result. Dynamic mask technology can automatically generate a mask to mask redundant and unimportant information according to the characteristics of the data, thereby reducing the interference of these information on feature analysis, and making the final feature analysis result more accurate.

[0038] The model processing module also includes a feature extraction unit for extracting multi-dimensional features from the target feature information. These multi-dimensional features include spatio-temporal features, semantic features, context association features, event evolution features, and key element saliency features.

[0039] The spatio-temporal features include the spatial distribution range and the time series change rate of each element in the emergency scene. The spatial distribution range can clearly indicate the specific location and occupied area of each element in the emergency scene, for example, the location distribution of fire, personnel, and fire facilities in a fire emergency scene; the time series change rate can reflect the changes of these elements over time, such as the spread speed of fire and the moving speed of personnel.

[0040] The semantic features are used to describe the category attributes and mutual relationships of entity objects in the scene. The category attributes of entity objects can determine what the objects are, such as personnel, vehicles, or buildings, etc.; the mutual relationships can indicate the connections between these objects, such as personnel beside vehicles, and personnel in buildings, etc.

[0041] The context association features are identified by analyzing the correlation between the front and rear frame data to reflect the dynamic association of the scene. In an emergency scene, the development of an event is a continuous process, and there is a close relationship between the front and rear frame data. By analyzing the correlation between these data, we can understand how the event develops from one state to another, and thus better grasp the dynamic changes of the scene.

[0042] The event evolution features are used to reflect the development trend and potential impact of different emergency events. Different emergency events have different development laws and potential impacts, for example, a fire may spread to the surrounding area and produce toxic gases; a flood may cause water levels to rise and submerge surrounding areas, etc. The event evolution features can help the model predict the development direction of the event and the possible consequences.

[0043] The key element saliency features are the prominent representation of key objects in complex scenes, with scene adaptability and multi-modal compatibility, which are used to reflect the importance of core elements. In a complex emergency scene, there may be multiple objects, but some of them play a key role in the development and handling of the event. The key element saliency features can highlight these key objects, making the model pay more attention to them.

[0044] The scene adaptability allows this feature to automatically adjust according to different emergency scenes to adapt to the characteristics of the scene; the multi-modal compatibility allows this feature to handle multiple types of data from different perception devices, such as images, audio, environmental parameters, etc.

[0045] Through the collaborative work of the data preprocessing unit, the large model parsing unit, and the feature extraction unit, the model processing module is able to conduct in-depth processing and analysis of the collected multi-dimensional data information. The data preprocessing unit provides high-quality standardized input data for subsequent processing. The large model parsing unit extracts target feature information containing scene elements and event correlations through end-to-end processing methods. The feature extraction unit further extracts multi-dimensional features from this target feature information. These features can comprehensively and accurately describe the status and development trends of the emergency scenario, providing rich and reliable information support for the subsequent situation deduction module and instruction generation module.

[0046] Example 3: The model processing module also includes a model optimization unit and a situation indicator acquisition unit. The model optimization unit uses transfer learning methods to optimize the parameters of the visual AI large model. It then uses the optimized and trained visual AI large model to classify the extracted target feature information and identify the specific situation type of the emergency scenario.

[0047] Transfer learning is used to optimize the parameters of the large visual AI model. The specific process is as follows: Based on the preset source domain dataset size, a pre-trained model highly relevant to the target emergency scenario is selected as the initial model. The source domain dataset here refers to a trained dataset that has a certain degree of relevance to the target emergency scenario. The preset source domain dataset size can be set according to actual needs.

[0048] The fully connected layer parameters of the initial model are fine-tuned using a small sample dataset from the target domain. A small sample dataset from the target domain refers to a smaller dataset collected for the target emergency scenario. During the fine-tuning process, the classification accuracy of the model is calculated using the cross-entropy loss function, which serves as the evaluation basis for model optimization. The cross-entropy loss function is expressed as: in, represents the cross entropy loss value, represents the number of samples, Indicates the samples, Indicates category, represents the total number of categories, Representation sample Belong to category The true label (usually 0 or 1), Represents the model prediction sample Belong to category probability.

[0049] The adjustment direction and step of the model parameter are updated according to the evaluation basis. The adjustment direction and step here determine the path and speed of the optimization of the model parameters, and need to be determined according to the calculation results of the cross-entropy loss function and the classification accuracy.

[0050] The parameter adjustment and evaluation process is repeated until the convergence condition is met, and the optimized visual AI large model parameter combination is output. The optimized parameter combination includes the weight coefficients of the feature extraction layer and the bias parameters of the classification layer. The convergence condition can be that the cross-entropy loss value is less than a certain preset threshold, or the classification accuracy reaches a certain preset value, etc.

[0051] The situation index acquisition unit quantitatively analyzes the key situation elements of each scene area, and acquires the situation index of the emergency scene according to the quantitative analysis results and the scene coverage range. The key situation elements refer to the factors that have a significant impact on the situation judgment in the emergency scene, such as personnel density, fire spread speed, environmental parameters, etc. Quantitative analysis of these elements can convert them into numerical form, which is convenient for subsequent analysis and processing.

[0052] The visual AI large model takes historical emergency scene data as input and scene situation type label as output during training. Historical emergency scene data refers to the data of past collected and already occurred emergency scenes, and the scene situation type label is the annotation of the emergency scene situation type corresponding to these data.

[0053] During the training process, the contrast learning enhancement algorithm enhances the ability to distinguish similar scenes, and the adversarial training improves the robustness of the algorithm to noise data. Contrast learning is a method that improves model performance by learning the differences between similar samples and different samples; adversarial training is to improve the resistance of the model to noise and interference by making the model against the generative adversarial network.

[0054] The inference process of the visual AI large model is as follows: input the target feature information to be analyzed, generate a high-dimensional feature vector through the feature extraction layer, perform linear transformation and activation function processing on the high-dimensional feature vector through the classification layer, and output the situation type probability distribution of the emergency scene. The class with the highest probability is the final recognized situation type.

[0055] The role of the feature extraction layer is to extract more abstract and representative features from the input target feature information and generate a high-dimensional feature vector. The classification layer performs linear transformation on the high-dimensional feature vector, maps it to different class spaces, and then processes it through the activation function to convert the linear transformation result into a probability distribution.

[0056] The activation function can be a softmax function, whose expression is: where, probability of the first category, output value of the classification layer for the first category, total number of categories.

[0057] Through the work of the model optimization unit and the situation index acquisition unit, the performance of the visual AI large model can be continuously optimized, the identification accuracy of the emergency scene situation type a is improved, and accurate situation indexes are obtained, providing reliable basis for situation deduction. The model optimization unit optimizes the model parameters by using the pre-training model and the target domain small sample dataset through the transfer learning method, reducing the amount of data and time required for model training, and improving the training efficiency of the model. The situation index acquisition unit can comprehensively and accurately reflect the situation of the emergency scene by quantitatively analyzing the key situation elements and considering the coverage range of the scene.

[0058] In the model optimization process, the cross-entropy loss function can effectively measure the difference between the model prediction results and the true labels, guiding the update direction of the model parameters. By continuously adjusting the model parameters, the cross-entropy loss value gradually decreases, and the classification accuracy gradually improves, thereby realizing the optimization of the model.

[0059] In the inference process, the high-dimensional feature vector generated by the feature extraction layer contains the key feature information of the emergency scene, and the classification layer processes these feature information through linear transformation and activation function to convert them into probability distribution of situation type, thereby realizing the identification of the emergency scene situation type.

[0060] The model optimization unit and the situation index acquisition unit in the model processing module realize the optimization of the visual AI large model and the acquisition of the situation index through a series of processing steps and methods, providing important support for the situation deduction and emergency management and control of the entire system.

[0061] Embodiment 4: The situation deduction module constructs a dynamic situation atlas of the emergency scene based on the analysis results output by the visual AI large model. The specific process needs to first set the three-level situation threshold range, i.e. the primary, intermediate and advanced situation threshold range, and the primary range is completely contained in the intermediate range, and the intermediate range is completely contained in the advanced range. Taking the forest fire emergency scene as an example, the primary situation threshold range can be set as the fire spread speed of 0.5 meters / minute to 1 meter / minute and the smoke concentration of 100 ppm to 200 ppm; the intermediate situation threshold range is set as the fire spread speed of 1 meter / minute to 3 meters / minute and the smoke concentration of 200 ppm to 500 ppm; the advanced situation threshold range is set as the fire spread speed exceeding 3 meters / minute and the smoke concentration exceeding 500 ppm. This grading setting can judge different risk levels according to different intensity of situation indexes.

[0062] When the current scenario's situation indicators are within the primary situation threshold range, such as a forest fire scenario with a fire spread rate of 0.8 meters per minute and a smoke concentration of 150 ppm, the emergency scenario is controllable. This means the fire's growth rate and smoke impact range are relatively low, and existing emergency resources and measures can effectively control it, preventing it from rapidly escalating into a more serious situation.

[0063] If the current scenario's situation indicator exceeds the upper limit of the primary situation threshold range, for example, if the fire spread rate in a forest fire reaches 1.2 meters per minute, exceeding the primary range's upper limit of 1 meter per minute, the emergency scenario presents an increasing risk. This indicates that the fire is spreading faster than before, possibly affected by factors such as wind direction and speed. If more forceful measures are not taken promptly, the fire could spread further, increasing the risk level. If the situation indicator falls below the lower limit of the primary situation threshold range, for example, if the fire spread rate drops to 0.3 meters per minute, below the primary range's lower limit of 0.5 meters per minute, the emergency scenario presents information gaps or monitoring blind spots. This may be due to monitoring equipment failure, inappropriate deployment location, or a fire in a specific phase, resulting in an inability to accurately obtain accurate data on the fire's development. This necessitates a prompt review of the monitoring system and the addition of additional monitoring points.

[0064] When the current scenario's situation indicators are outside the primary situation threshold but within the intermediate situation threshold, for example, a forest fire with a fire spread rate of 2 meters per minute and a smoke concentration of 300 ppm, the emergency scenario risk level is at the warning level. This indicates that the fire has moved beyond the controllable primary stage and is developing into a more serious situation. It is necessary to activate the early warning mechanism and mobilize additional emergency resources, such as additional firefighters and fire trucks, to prepare for a larger-scale fire.

[0065] If the current scenario's situation indicators are outside the intermediate threshold range but within the advanced threshold range, for example, if the fire spread rate reaches 4 meters per minute and the smoke concentration reaches 600 ppm, the risk level is at the warning level. This indicates that the fire is developing seriously, posing a significant threat to the surrounding ecological environment and human safety, and requires more urgent and stringent emergency measures, such as evacuating personnel and demarcating warning areas.

[0066] When the current scenario's situation indicators fall outside the advanced situation threshold range, for example, a fire spread exceeding 5 meters per minute or a smoke concentration exceeding 800 ppm, the risk level is considered emergency. This indicates that the fire is extremely critical and could cause serious casualties and property damage, necessitating the initiation of the highest level of emergency response, mobilizing all available emergency resources for full firefighting and disposal.

[0067] For example, in the context of urban flood emergency, the primary situation threshold range can be set as water depth between 10 cm and 30 cm, and water flow speed between 0.5 m / s and 1 m / s; the intermediate situation threshold range can be set as water depth between 30 cm and 100 cm, and water flow speed between 1 m / s and 2 m / s; the advanced situation threshold range can be set as water depth exceeding 100 cm, and water flow speed exceeding 2 m / s. When the water depth is 40 cm and the water flow speed is 1.5 m / s, it is within the intermediate situation threshold range, and the risk level is warning, prompting relevant departments to prepare for drainage and monitor water level changes.

[0068] In practical applications, different emergency scenarios need to be reasonably set according to their own characteristics and historical data. For example, in the context of chemical plant explosion, factors such as toxic gas concentration and explosion power need to be considered; in the context of high-rise building fire, factors such as fire spread height and personnel evacuation difficulty need to be considered. Through this hierarchical situation deduction method, the risk level of the emergency scenario can be more accurately judged, providing accurate basis for the subsequent instruction generation module, so that the generated emergency control instructions can take appropriate measures according to different risk levels, improving the efficiency and effectiveness of emergency control.

[0069] The construction of dynamic situation atlas is a real-time updating process. As the visual AI large model continuously outputs new analysis results, the situation indicators will change in real time, thereby dynamically adjusting the risk level of the emergency scenario. For example, in the process of forest fire fighting, as the fire spread speed gradually decreases and the smoke concentration gradually decreases, the situation indicators will move from the advanced situation threshold range to the intermediate and primary situation threshold ranges, and the risk level will also decrease accordingly. At this time, the emergency control instructions also need to be adjusted according to the new risk level, such as reducing the input of fire personnel and switching to cleaning residual fire.

[0070] Example 5: The core function of the execution feedback module is to issue emergency control instructions to the execution terminal and collect feedback data during the execution process. In actual operation, the instruction issuing process needs to follow a specific communication protocol to ensure that the instructions can be accurately and timely transmitted to the corresponding execution terminal. For example, in the context of forest fire emergency, the central control system generates instructions to mobilize fire vehicles to the designated area, which will be sent to the vehicle-mounted terminal of each fire vehicle through a wireless communication network. After receiving the instructions, the vehicle-mounted terminal will display specific task content, destination coordinates, and time requirements, etc.

[0071] The collection positions of feedback data are explicitly set as the starting point, the end point, the key operation node of the instruction execution area and the effect verification area. Taking the execution of the drainage pump start instruction in the urban waterlogging emergency scene as an example, the starting point is the control room where the drainage pump is located, the end point is the drainage outlet position, the key operation node includes the pressing of the pump body start button, the opening of the valve and other operation positions, and the effect verification area is the water depth monitoring point near the drainage outlet. Deploying sensors or data collection devices at these positions can comprehensively and accurately obtain feedback data.

[0072] At the starting point position, such as the control room, the instruction receiving time, operator information and device initial state data need to be collected. After receiving the instruction, the operator needs to confirm in the system, and the system will record the confirmation time and the identity information of the operator, while collecting the voltage, current and other device initial state data before the drainage pump starts. These data can reflect whether the instruction is timely and whether the device is in a normal standby state.

[0073] At the end point position, that is, at the drainage outlet, real-time flow data and water quality data of the drainage outlet need to be collected. Flow data can be obtained through the flow meter installed at the drainage outlet, and water quality data can be monitored through the water quality sensor. These data can directly reflect the working effect of the drainage pump, whether the expected drainage volume is reached, and whether the discharged water quality meets the relevant standards.

[0074] The feedback data collection of the key operation node is particularly important. Taking the valve opening operation in the drainage pump start process as an example, a position sensor is installed on the valve to monitor the opening angle and opening time of the valve in real time. When the operator performs the valve opening operation, the position sensor will transmit the real-time state data of the valve to the feedback module, and the system can judge whether the valve is opened according to the specified process and time requirements, whether there is operation delay or failure.

[0075] The feedback data collection of the effect verification area is used to evaluate whether the execution effect of the emergency control instruction meets the expected target. In the urban waterlogging scene, multiple water depth monitoring points are set in the waterlogged area near the drainage outlet, and water depth data is collected every certain time. By comparing the water depth changes before and after the execution of the instruction, it can be clearly understood whether the work of the drainage pump effectively reduces the water depth, and whether the water depth decline speed meets the expectation.

[0076] The collection frequency of feedback data can be adjusted according to the urgency of the emergency scene and the importance of the instructions. For emergency instructions of high emergency level, such as personnel evacuation instructions in high-rise building fire, the collection frequency of feedback data may need to be set to once per second to grasp the evacuation progress and personnel safety in real time; while for general level emergency instructions, such as daily fire facility inspection instructions, the collection frequency of feedback data can be set to once per minute or longer.

[0077] The collected feedback data will be subjected to preliminary encoding and packaging, and then transmitted to the central processing system through a dedicated communication link. In the transmission process, in order to ensure the safety and integrity of the data, encryption technology will be used to encrypt the data, and check code and other information will be added, so that the central processing system can check and restore the data.

[0078] After receiving the feedback data, the central processing system will decode and analyze the data, convert it into a recognizable format, and store it in the database. At the same time, the system will analyze the feedback data in real time to determine whether the execution of the emergency control instructions is normal or whether there are abnormal situations. For example, in forest fire fighting, if the feedback data shows that the fire vehicle has not arrived at the designated area within the specified time, the system will automatically issue an alarm and notify the relevant personnel to investigate and handle it.

[0079] The application of feedback data is not limited to monitoring the execution of current instructions, but also provides reference for subsequent emergency control decisions. Through the analysis of historical feedback data, the execution effect and existing problems of various instructions in different emergency scenes can be summarized, so as to optimize the instruction generation strategy and execution process. For example, through the analysis of feedback data of multiple urban waterlogging emergency instructions, it is found that the drainage efficiency of some drainage areas is low due to pipeline aging, so in the subsequent instruction generation, instructions for the maintenance and update of the pipelines in these areas can be added.

[0080] In the actual operation process of the execution feedback module, various problems may be encountered, such as communication link interruption causing feedback data unable to be transmitted, sensor failure causing inaccurate data collection, etc. In order to deal with these problems, the system has fault detection and alarm functions. When the communication link is interrupted, the system will automatically try to reconnect and issue a communication fault alarm; when the sensor fails, the system will mark the data collected by the sensor and notify the maintenance personnel for repair.

[0081] In addition, the execution feedback module also maintains close interaction with other modules. For example, when the feedback data shows that the execution effect of the emergency control instructions does not meet the expectations, the instruction generation module will regenerate or adjust the instructions according to the feedback data; the model processing module can use the feedback data to optimize the visual AI big model and improve the prediction accuracy of the model.

[0082] The execution feedback module realizes comprehensive monitoring and evaluation of the emergency control instruction execution process by collecting feedback data at key positions in the instruction execution area. These feedback data provide important information support for ensuring the effective execution of the instruction, timely discovery and solution of problems, and optimization of subsequent emergency control decisions, making the entire panoramic situation intelligent emergency control system based on visual AI large model form a closed-loop management process, and improving the emergency response capability and control effect of the system.

[0083] It should be noted that, in this text, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0084] Although embodiments of the present application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, substitutions and alterations can be made thereto without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A panoramic situation intelligent emergency management and control system based on a large visual AI model, characterized by: include: The data perception module acquires multi-dimensional data information of global emergency scenarios through multi-source perception devices; The model processing module pre-processes the acquired multi-dimensional data information and inputs it into the visual AI model for feature extraction and situation analysis; The situation deduction module builds a dynamic situation map of emergency scenarios based on the analysis results output by the visual AI large model; The instruction generation module generates corresponding emergency control instructions based on the deduction conclusions of the dynamic situation map; The execution feedback module sends emergency control instructions to the execution terminal and collects feedback data of the execution process.

2. The panoramic situation intelligent emergency management and control system based on the visual AI large model according to claim 1 is characterized in that: The data perception module adopts a distributed sensor network structure, and each sensor node can adjust the perception parameters to adapt to the monitoring needs of different emergency scenarios; The multi-dimensional data information of the global emergency scene is obtained through multi-source sensing equipment. Specifically, each sensor node collects image, audio and environmental parameter data according to a preset period, and the data is preliminarily screened by the edge computing device before being transmitted to the central processing unit.

3. The panoramic situation intelligent emergency management and control system based on the visual AI large model according to claim 1 is characterized in that: The model processing module includes a data preprocessing unit and a large model parsing unit; the data preprocessing unit unifies the format of multidimensional data information and filters noise to obtain standardized input data; the large model parsing unit processes the standardized input data through an end-to-end processing method to extract target feature information containing the relationship between scene elements and events; The end-to-end processing method specifically includes: Input standardized input data; convert the standardized input data into a tensor form that can be recognized by the large model; enhance the key area features through the attention mechanism and perform multi-scale feature fusion; use dynamic masking technology to suppress redundant information interference and obtain the final feature analysis results.

4. The panoramic situation intelligent emergency management and control system based on the visual AI large model according to claim 1 is characterized in that: The model processing module includes a feature extraction unit; The feature extraction unit is used to extract multi-dimensional features from target feature information; The extracted multi-dimensional features include spatiotemporal features, semantic features, contextual association features, event evolution features, and key element saliency features; The spatiotemporal characteristics include: the spatial distribution range of each element in the emergency scene and the time series change rate; The semantic features are used to describe the category attributes and mutual relationships of physical objects in the scene; The contextual association feature is used to identify the development of events through the correlation analysis of previous and next frame data, and is used to reflect the dynamic relevance of the scene; The event evolution characteristics are used to reflect the development trends and potential impacts of different emergency events; The key element saliency feature is a prominent representation of key objects in complex scenes, has scene adaptability and multimodal compatibility, and is used to reflect the importance of core elements.

5. The panoramic situation intelligent emergency management and control system based on the visual AI large model according to claim 1 is characterized in that: The model processing module also includes a model optimization unit and a situation indicator acquisition unit; the model optimization unit uses a transfer learning method to optimize the parameters of the visual AI large model, and uses the optimized and trained visual AI large model to classify the extracted target feature information to identify the specific situation type of the emergency scene; the situation indicator acquisition unit quantifies and counts the key situation elements of each scene area, and obtains the situation indicators of the emergency scene based on the quantitative statistical results and the scene coverage.

6. The panoramic situation intelligent emergency management and control system based on the visual AI large model according to claim 5 is characterized in that: The transfer learning method is used to optimize the parameters of the visual AI large model. The specific process is as follows: According to the preset source domain dataset size, a pre-trained model that is highly relevant to the target emergency scenario is selected as the initial model; The fully connected layer parameters of the initial model are fine-tuned using a small sample dataset of the target domain. The classification accuracy of the model is calculated using the cross-entropy loss function, and the classification accuracy is used as the evaluation basis for model optimization. Update the adjustment direction and step size of model parameters based on the evaluation criteria; Repeat the parameter adjustment and evaluation process until the convergence conditions are met, and output the optimized visual AI model parameter combination. The optimized parameter combination includes the weight coefficient of the feature extraction layer and the bias parameter of the classification layer.

7. The panoramic situation intelligent emergency management and control system based on the visual AI large model according to claim 5 is characterized in that: The visual AI model uses historical emergency scene data as input and scene situation type labels as output during training; During the training process, contrastive learning is used to enhance the algorithm's ability to distinguish similar scenes, and adversarial training is used to improve the algorithm's robustness to noisy data. The reasoning process of the visual AI large model is as follows: Input the target feature information to be analyzed; A high-dimensional feature vector is generated through the feature extraction layer; the high-dimensional feature vector is linearly transformed and activated by the classification layer; the probability distribution of the situation type of the emergency scenario is output, and the category with the highest probability is the final identified situation type.

8. The panoramic situation intelligent emergency management and control system based on the visual AI large model according to claim 1 is characterized in that: The situation deduction module is used to construct a dynamic situation map of the emergency scenario based on the analysis results output by the visual AI large model. The specific process is as follows: Setting a primary situation threshold range, an intermediate situation threshold range, and an advanced situation threshold range; wherein the primary situation threshold range is within the intermediate situation threshold range; and wherein the intermediate situation threshold range is within the advanced situation threshold range; If the situation indicators of the current scenario are within the primary situation threshold range, the emergency scenario is under control; If the situation indicator of the current scene exceeds the upper limit of the primary situation threshold range, the emergency scene has a risk expansion trend; if the situation indicator of the current scene is lower than the lower limit of the primary situation threshold range, the emergency scene has information missing or monitoring blind spots; If the situation indicators of the current scenario are outside the primary situation threshold range and within the intermediate situation threshold range, the risk level of the emergency scenario is the warning level; if the situation indicators of the current scenario are outside the intermediate situation threshold range and within the advanced situation threshold range, the risk level is the alert level; if the situation indicators of the current scenario are outside the advanced situation threshold range, the risk level is the emergency level.

9. The panoramic situation intelligent emergency management and control system based on the visual AI large model according to claim 1 is characterized in that: The data perception module is used to obtain multi-dimensional data information of the global emergency scene through multi-source perception devices. The deployment locations of the perception devices are located at the key nodes, key protection areas and high-incidence areas of the emergency scene, and each deployment location is assigned a unique geographic identification code.

10. The panoramic situation intelligent emergency management and control system based on the visual AI large model according to claim 1 is characterized in that: The execution feedback module is used to send emergency control instructions to the execution terminal and collect feedback data of the execution process. The feedback data is collected at the starting point, end point, key operation nodes and effect verification area of ​​the instruction execution area.

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