Metacosmic police service processing system based on multiple modes

Abstract

The invention discloses a meta universe police service processing system based on multiple modes, and relates to the technical field of data processing. Comprising a data acquisition module used for acquiring multi-modal data of a police service site in real time, including video data, audio data and environment data; the data fusion and analysis module is used for fusing the collected multi-modal data, extracting key event information and detecting abnormal behaviors; the risk prediction and decision support module is used for combining real-time data with historical case data, predicting a potential risk event and generating a coping strategy; and the police service resource scheduling module is used for automatically scheduling police forces, equipment and resources to the site based on the risk prediction and decision suggestions, optimizing site police service operation and updating the task progress in real time. According to the invention, on-site conditions can be comprehensively monitored, risk prediction can be carried out in combination with historical case data, police forces and resources are automatically scheduled, and police service decisions are optimized.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a metaverse policing processing system based on multimodality. Background Art

[0002] In recent years, with the rapid development of information technology, the field of policing has gradually moved towards intelligence and digitization. In particular, the introduction of multimodal data processing technology has enabled the police processing system to make significant progress in data collection, analysis and decision support. Traditional police management usually relies on a single data source, such as video surveillance systems or manual patrol reports, while modern multimodal data collection technology can more comprehensively reflect the on-site situation through comprehensive analysis of video, audio and environmental data.

[0003] However, although the current police processing system has made some progress in data collection and analysis, there are still problems such as difficulty in data integration, poor real-time performance, and inaccurate decision support. Most traditional systems rely on a single data source and cannot process multi-dimensional data in real time, resulting in police officers being unable to fully grasp the situation on the scene. In addition, existing risk prediction systems are mostly based on historical data, but often ignore the real-time multimodal data on the scene, resulting in insufficient prediction accuracy and timeliness of response strategies. In response to these problems, existing technologies often appear to be slow to respond to emergencies, making it difficult to quickly schedule resources and assign tasks. Summary of the invention

[0004] In view of the problems existing in the above-mentioned background technology, the present invention proposes a metaverse policing processing system based on multimodality.

[0005] Therefore, the problem to be solved by the present invention is how to form an intelligent system integrating multimodal data collection, fusion analysis, risk prediction and resource scheduling.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, an embodiment of the present invention provides a metaverse policing processing system based on multimodality, which includes: a data acquisition module, which is used to collect multimodal data of the police scene in real time, including video data, audio data and environmental data; a data fusion and analysis module, which is used to fuse the collected multimodal data, extract key event information and detect abnormal behavior; a risk prediction and decision support module, which is used to combine real-time data with historical case data, predict potential risk events, and generate response strategies; a police resource scheduling module, which is used to automatically dispatch police force, equipment and resources to the scene based on risk prediction and decision recommendations, optimize the police operations on the scene, and update the task progress in real time; the data The fusion and analysis module includes a data preprocessing submodule, a data fusion submodule, a pattern recognition submodule and a data conflict correction submodule; the data preprocessing submodule is used to perform preprocessing operations on the original data; the data fusion submodule is used to fuse multi-modal data using an adaptive weighting algorithm according to the importance and relevance of different modal data; the pattern recognition submodule is used to extract key event information and detect abnormal behavior using a deep learning algorithm; the data conflict correction submodule is used to automatically detect and process conflicting or inconsistent data during the data fusion process to ensure the accuracy of the analysis results; the data conflict correction submodule includes the following contents: suppose the event in the video data is , the events in the audio data are , then the conflict determination condition is defined as:

[0007] That is, if and If the events represented are different, conflicts will arise.

[0008] As a preferred solution of the multimodal metaverse policing processing system described in the present invention, the data acquisition module includes: a video data acquisition submodule, which is used to capture the on-site video stream through a surveillance camera; an audio data acquisition submodule, which is used to obtain audio signals through a microphone and a voice sensor; and an environmental data acquisition submodule, which is used to obtain on-site environmental data through environmental sensors.

[0009] As a preferred solution of the multimodal metaverse policing processing system described in the present invention, the data conflict correction submodule further includes: if a conflict is detected, the conflict data is corrected using Bayesian reasoning: the final correction probability Calculated by Bayes' theorem:

[0010] in, and Respectively represent the events under given data D and The posterior probability of .

[0011] As a preferred solution of the multimodal metaverse police processing system described in the present invention, the risk prediction and decision support module includes: a historical data analysis submodule for analyzing historical police data and building a crime pattern model; a real-time risk prediction submodule for combining real-time data to predict potential security risks; a response strategy generation submodule for automatically generating corresponding response strategies based on predicted security risks; the historical data analysis submodule includes the following contents; building a risk scenario model based on historical data: analyzing the time pattern of historical events through a time series model to predict the occurrence time of a certain behavior:

[0012] in, For time series data, are the parameters of the time series, is the error term; by using the clustering algorithm to classify historical cases, we found the common characteristics of different types of cases:

[0013] Among them, C is the cluster center, N is the number of data sets or samples, k is the number of clusters, is the feature vector of the case, is the center of the jth class.

[0014] As a preferred solution of the multimodal metaverse policing processing system described in the present invention, the police resource scheduling module includes: a police force scheduling submodule, which is used to automatically dispatch police force to the location where it is most needed based on the risk assessment results; an equipment scheduling submodule, which is used to deploy relevant equipment according to on-site needs; a task management submodule, which is used to track task progress in real time, update the execution status of on-site tasks, and optimize resource utilization.

[0015] As a preferred solution of the multimodal metaverse police processing system described in the present invention, the police dispatch submodule includes the following contents: judging the police force demand of a certain area or event based on real-time risk prediction and historical data analysis: using linear programming to solve the police dispatch problem, the goal is to minimize the cost of police dispatch or maximize the efficiency of police resource utilization: having police resources, and the arrival time of each police resource is , optimize police dispatch through the following linear programming model:

[0016] in, represents the time when the i-th police force arrives at the j-th task, is a binary decision variable (1 means scheduled, 0 means not scheduled), and m represents the number of tasks.

[0017] As a preferred solution of the multimodal metaverse police processing system described in the present invention, the equipment scheduling submodule includes the following contents: analyzing the type and quantity of required equipment according to the task type and on-site conditions; evaluating the availability of the current equipment and judging whether it meets the task requirements, and calculating the optimal equipment scheduling plan according to the equipment requirements and available resources; executing the equipment scheduling according to the scheduling results, and monitoring the equipment usage status in real time; the equipment scheduling is performed through a priority scheduling algorithm: there is a The scheduling priority of each device is The urgency of the task is , then the objective function of equipment scheduling is expressed as:

[0018] in, is the number of devices.

[0019] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the multimodal metaverse policing processing system based on the first aspect of the present invention are implemented.

[0020] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: the computer program instructions, when executed by a processor, implement the steps of the multimodal metaverse policing processing system as described in the first aspect of the present invention.

[0021] The beneficial effects of the present invention are as follows: through real-time collection and fusion analysis of multimodal data, the present invention can not only comprehensively monitor the on-site situation, but also make risk predictions in combination with historical case data, automatically dispatch police forces and resources, thereby optimizing police decision-making, and improving on-site emergency response capabilities. It can solve the problems of incomplete data integration, inaccurate predictions, and untimely resource scheduling in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 This is a structural diagram of the multimodal-based metaverse policing system.

[0024] Figure 2 FIG. 1 is a schematic diagram of the structure of a terminal in the first embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments. Example

[0028] Reference Figure 1~Figure 2 , which is the first embodiment of the present invention, and which provides a multimodal metaverse policing processing system, comprising: The data acquisition module is used to collect multimodal data at the police scene in real time, including video (camera), audio (microphone or voice sensor), and environmental data (such as temperature, humidity, position sensor, etc.). It includes a video data acquisition submodule, which is used to capture the on-site video stream through a surveillance camera; an audio data acquisition submodule, which is used to obtain audio signals through a microphone and voice sensor; and an environmental data acquisition submodule, which is used to obtain on-site environmental data through environmental sensors (such as temperature, humidity, GPS).

[0029] The data fusion and analysis module is used to fuse the collected multimodal data, and extract key event information and detect abnormal behavior through pattern recognition and conflict processing technology. It includes a data preprocessing submodule, which is used to perform preprocessing operations such as denoising, filtering, and standardization on the original data to ensure data quality; a data fusion submodule, which is used to fuse multimodal data using an adaptive weighted algorithm based on the importance and relevance of different modal data; a pattern recognition submodule, which is used to extract key event information and detect abnormal behavior (such as fighting, brawl, escape, etc.) using a deep learning algorithm; and a data conflict correction submodule, which is used to automatically detect and process conflicting or inconsistent data during the data fusion process to ensure the accuracy of the analysis results.

[0030] The risk prediction and decision support module is used to combine real-time data with historical case data based on big data analysis models and machine learning algorithms to predict potential risk events and generate response strategies for reference by decision makers or command centers. It includes a historical data analysis submodule, which is used to analyze historical police data and build a crime pattern model; a real-time risk prediction submodule, which is used to combine real-time data to predict potential security risks; and a response strategy generation submodule, which is used to automatically generate corresponding response strategies (such as increasing police force, etc.) based on the predicted security risks.

[0031] The police resource dispatch module is used to automatically dispatch police force, equipment and resources to the scene based on risk prediction and decision-making suggestions, optimize on-site police operations, and update the task progress in real time. It includes a police force dispatch sub-module, which is used to automatically dispatch police force to the location where it is most needed based on risk assessment results; an equipment dispatch sub-module, which is used to deploy relevant equipment (such as mobile command vehicles, monitoring equipment, etc.) according to on-site needs; and a task management sub-module, which is used to track task progress in real time, update the execution status of on-site tasks, and optimize resource utilization.

[0032] Preferably, the data acquisition module specifically includes: The video data acquisition submodule captures the video stream of the scene in real time through the surveillance cameras deployed at the police scene. The cameras usually include ordinary high-definition cameras and cameras with specific functions (such as infrared, thermal imaging, etc.), which can collect data under different environmental conditions.

[0033] The audio data acquisition submodule uses a microphone or voice sensor to obtain sound signals at the police scene, including environmental noise, conversation sounds, cries for help, etc. This module can capture voice exchanges and abnormal sounds at the scene to assist in determining the nature and urgency of the incident.

[0034] The environmental data acquisition submodule obtains environmental data of the police scene through various sensors, including temperature, humidity, air pressure, location coordinates (GPS) and other information. These data can provide additional contextual information for the on-site conditions and help determine the impact of the on-site physical environment on police processing, including obtaining the on-site climate conditions in real time through temperature and humidity sensors to determine whether there are special environmental factors (such as extreme weather, fire, etc.) that affect police operations.

[0035] Use GPS or other positioning technologies (such as Wi-Fi positioning, Bluetooth positioning, etc.) to determine the precise location of police officers or equipment, and ensure the efficiency of resource dispatch and on-site response by tracking the location of police forces in real time.

[0036] In certain scenarios, other environmental sensors (such as smoke detectors, gas sensors, radiation detectors, etc.) may also be used to obtain other environmental data on site, especially in special circumstances such as handling hazardous materials or fires.

[0037] Specifically, the data fusion submodule includes the following contents: First, the data from different sensors are aligned to a unified timestamp and spatial coordinate system. Based on the credibility, importance, and accuracy of different data sources, the information from multiple data sources is combined through weighted fusion, and useful features are extracted from each modal data (such as object recognition in video, sound patterns in audio, etc.).

[0038] Specifically, the pattern recognition submodule includes the following contents: Features for pattern recognition are extracted from the fused data, and machine learning or deep learning models are used for behavioral pattern recognition to identify possible violent behavior in the video or abnormal sounds in the audio.

[0039] Specifically, the data conflict correction submodule includes the following contents: The data conflict correction submodule is responsible for handling possible conflicts and inconsistencies between different data sources. Usually, these conflicts come from errors, delays or noises of different sensors.

[0040] Detect possible inconsistencies between data sources (for example, inconsistent events in video and audio) and correct the data according to preset rules or algorithms. The specific operations are as follows: Assume that the event in the video data is , the events in the audio data are ,if and If the events represented are different, a conflict occurs. The conflict determination condition can be defined as:

[0041] If a conflict is detected, Bayesian reasoning can be used to correct the conflicting data. and Respectively represent the events under given data D and The posterior probability, the final revised probability It can be calculated by Bayes' theorem:

[0042] It can be seen that Bayesian reasoning can correct conflicting data based on prior knowledge and generate a more accurate result.

[0043] Specifically, the historical data analysis submodule includes the following contents: Collect historical data related to policing, such as historical case records and past social events (such as traffic accidents), clean and process the data, and identify possible behavior patterns by analyzing the event sequences in the historical data. For example, certain types of cases may be related to specific locations, times, or population characteristics.

[0044] Build a risk scenario model based on historical data. The specific steps are as follows: Analyze the temporal patterns of historical events through time series models and predict the occurrence time of certain behaviors:

[0045] in, is time series data (such as the number of cases or the frequency of events), are the parameters of the time series, is the error term.

[0046] Through clustering algorithms, historical cases are classified to find common characteristics of different types of cases, such as location, time, victim groups, etc. The classification calculation process is as follows;

[0047] Among them, C is the cluster center, N is the number of data sets or samples, k is the number of clusters, is the feature vector of the case, is the center of the jth class.

[0048] Furthermore, the association rules in historical data are discovered through the Apriori algorithm or FP-growth algorithm. For example, a certain crime may be associated with certain places, events or behavior patterns.

[0049] Specifically, the real-time risk prediction submodule includes the following contents: Extract key features (such as abnormal behavior, suspect location, environmental changes, etc.) from real-time data, use historical data analysis results and real-time data, and use machine learning algorithms to predict risk events. The specific operations are as follows: Through the SVM classifier, the risk events in real-time data and historical data are compared to predict the risk of the current scenario:

[0050] Among them, x is the input feature, is the weight, b is the bias term, is the output of the classification function, predicting whether the current event is a risk.

[0051] Specifically, the response strategy generation submodule includes the following contents: The response strategy generation submodule automatically generates response strategies based on real-time risk prediction results and historical data analysis. The strategies include resource scheduling, personnel dispatch, event intervention, etc., to ensure effective response to emergencies.

[0052] Specifically, heuristic algorithms or optimization algorithms are used to generate optimal resource scheduling strategies. For example, in a linear programming model, the goal is to minimize response time or maximize resource utilization; The basic steps to optimize resource scheduling using genetic algorithms (GA) are as follows: Converting the task and resource allocation scheme into a genotype encoding (e.g., binary encoding or permutation encoding); Initial population generation: randomly generate several initial solutions (scheduling plans), i.e., the initial population; Fitness evaluation: For each individual, calculate its fitness, that is, the value of the objective function (such as task completion time or cost). The lower the fitness, the better the solution; Selection operation: select suitable individuals as parents for crossover and mutation according to fitness; Crossover operation: Generate new solutions through crossover operation and combine parent genes; Mutation operation: Randomly adjust genes in a small range to increase diversity and avoid falling into the local optimal solution; Update the population: Select a new population to replace the old one, and repeat the selection, crossover, and mutation operations until the termination condition is reached (such as the set maximum number of generations or a sufficiently good solution is found).

[0053] Specifically, the police dispatch submodule includes the following contents: Based on real-time risk prediction and historical data analysis, the police force requirements for a certain area or event are determined. For example, if a violent incident is predicted, more police forces will need to be deployed for control and intervention. Based on the currently available police resources (including the number of police officers, vehicles, etc.), it is evaluated whether the mission requirements can be met; and based on the police force requirements and available resources, the optimal police force dispatch plan is calculated to ensure that sufficient police force is allocated to each event or area, and the response time is the shortest. According to the plan generated by the dispatch algorithm, the police force is dispatched and the status of the police force arriving at the scene is tracked in real time.

[0054] Preferably, linear programming is used to solve the police dispatch problem, with the goal of minimizing the cost of police dispatch (e.g., response time) or maximizing the efficiency of the use of police resources. Assume that Police resources (such as police officers, etc.), the arrival time of each police resource is , the following linear programming model can be used to optimize police dispatch:

[0055] in, represents the time when the i-th police force arrives at the j-th task, is a binary decision variable (1 means scheduled, 0 means not scheduled), and m represents the number of tasks.

[0056] Furthermore, the police dispatch plan is updated in real time according to the on-site situation (such as delays in the arrival of police forces, changes in task priorities, etc.). For example, dynamic dispatch algorithms (such as rolling optimization based on prediction models) are used for adjustment.

[0057] Specifically, the device scheduling submodule includes the following contents: Analyze the type and quantity of equipment required based on the mission type and on-site conditions. For example, for high-risk scenarios, it may be necessary to dispatch drones for aerial monitoring or dispatch advanced monitoring equipment for detailed capture.

[0058] Evaluate the availability of current equipment (such as equipment storage, operators, equipment location, etc.) and determine whether it meets the mission requirements. Calculate the optimal equipment scheduling plan based on equipment requirements and available resources to ensure that the required equipment arrives on site and is put into use in a timely manner.

[0059] And according to the scheduling results, execute the equipment scheduling, and monitor the equipment usage status in real time to ensure the normal operation of the equipment.

[0060] Among them, equipment scheduling can be carried out through priority scheduling algorithms. For example, in the case of multiple devices, key equipment is scheduled first for emergency events. There are 2 types of equipment (such as drones, police cars, etc.), and the scheduling priority of each device is The urgency of the task is , then the objective function of equipment scheduling can be expressed as:

[0061] in, For the number of equipment, the goal is to maximize the utilization efficiency of key equipment.

[0062] In device scheduling, it is usually necessary to consider the availability and resource limitations of the device. For example, the battery life of a drone or the limitations of network bandwidth, use integer programming models for scheduling optimization:

[0063] Among them, the constraints ensure that each device can only be assigned to one task.

[0064] Furthermore, the equipment dispatch plan can be adjusted in real time according to the working status of the equipment (such as failure, damage, etc.). The real-time feedback function of the dispatch system can be used to dynamically adjust the equipment position or reallocate equipment tasks.

[0065] Specifically, the task management submodule includes the following contents: Automatically assign tasks to corresponding police forces and equipment based on risk prediction results and response strategies. For example, a task may require rapid traffic control, and the system will assign the task to traffic management police forces and related equipment; Monitor the execution status of each task in real time, track whether the task is progressing as planned, and handle delays or resource conflicts in a timely manner; Dynamically adjust the task allocation and execution strategy based on the feedback from task execution (such as on-site progress, resource allocation, etc.). For example, if there is a shortage of police force in a certain area, the system will automatically increase police force support.

[0066] This embodiment also provides a computer device, which is suitable for the case of a metaverse policing processing system based on multimodality, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the metaverse policing processing system based on multimodality as proposed in the above embodiment.

[0067] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0068] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a multimodal metaverse policing processing system as proposed in the above embodiment.

[0069] In summary, the present invention can not only comprehensively monitor the on-site situation through real-time collection and fusion analysis of multimodal data, but also conduct risk prediction in combination with historical case data, automatically dispatch police force and resources, thereby optimizing police decision-making and improving on-site emergency response capabilities. It can solve the problems of incomplete data integration, inaccurate predictions, and untimely resource scheduling in the existing technology.

[0070] Example 2 Referring to Table 1 and Table 2, the second embodiment of the present invention provides a metaverse policing processing system based on multimodality. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0071] The experiment simulated an urban public safety management scenario. The experimental site was selected as a medium-sized urban core area and covered common public safety incidents such as small-scale fights and traffic accidents.

[0072] In order to simulate real-world scenarios, a variety of sensors and devices were used in the experiment, including: Ten high-definition cameras were deployed on site, including four infrared cameras and two thermal imaging cameras. Five high-sensitivity microphones and voice sensors were installed to capture conversations, cries for help and abnormal sounds at the scene. Four temperature and humidity sensors and three GPS positioning devices were used.

[0073] Use deep learning algorithms to integrate video, audio, and environmental data in real time and identify behavioral patterns, combine historical case data to predict risks, use machine learning algorithms to analyze historical data, and combine real-time data to predict risks and generate response strategies, dispatch police forces, equipment, and resources to the scene in real time, and optimize police operations.

[0074] At the beginning of the experiment, the system activated all sensor modules to collect data. The camera transmitted video data in real time, the microphone and voice sensor captured environmental noise and cries for help, the environmental data sensor recorded the changes in temperature and humidity on site, and the GPS positioning device was used to track the location of police forces and equipment in real time.

[0075] The collected data is preprocessed through the data fusion and analysis module, and then an adaptive weighting algorithm is used to fuse data from different sources. The pattern recognition submodule uses deep learning to identify key events, such as the occurrence of fights.

[0076] The system combines historical case data analysis to predict potential violent behavior at the scene, and generates response strategies through the decision support module, such as sending additional police force. Based on the risk prediction results, the police resource dispatch module automatically dispatches police force, equipment and resources to the scene according to the optimal strategy to ensure a rapid response.

[0077] The experimental data is recorded through real-time analysis of sensors and systems on site, reflecting the performance of the system in different scenarios, as follows: Table 1 Data collection and analysis results

[0078] Table 2 Risk prediction and resource scheduling results

[0079] From the above table we can see that: Judging from the amount of data collected and the accuracy of abnormal event detection in Table 1, the video, audio, and environmental data collection modules can operate efficiently in different scenarios. For example, at the scene of a fight, the amount of video data collected reached 512MB, the amount of audio data collected was 120MB, and the real-time monitoring of humidity and temperature provided additional information for event analysis. This shows that the collection of multimodal data can fully reflect the situation on the scene, and the abnormal event detection accuracy of the pattern recognition submodule is very high, especially in the fight and natural disaster scenes, the anomaly detection accuracy is 98% and 99% respectively.

[0080] Table 2 shows the risk prediction accuracy and resource scheduling effect. In all experimental scenarios, the system's risk prediction accuracy is generally high, especially in high-risk scenarios such as fighting and natural disasters, with risk prediction accuracy rates of 98% and 99% respectively. This result proves the effectiveness of the risk prediction module based on historical data and real-time data in emergency response. Through accurate risk prediction, the system can quickly generate response strategies and dispatch police forces and equipment according to the strategies to ensure that resources can arrive quickly and optimize on-site operations.

[0081] By comparing the existing traditional police processing system, the present invention demonstrates obvious innovation and advantages. In traditional systems, police resource scheduling and incident response often rely on manual analysis and a single data source, lacking comprehensive real-time data support. However, this system achieves efficient and accurate emergency response and resource scheduling through multimodal data fusion, pattern recognition of deep learning algorithms, and risk prediction based on big data. The system demonstrates higher accuracy and response speed when handling emergencies, significantly improves police processing efficiency, and effectively optimizes resource utilization.

[0082] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multimodal metaverse policing system, characterized by: include: Data collection module, used to collect multi-modal data at the police scene in real time, including video data, audio data and environmental data; Data fusion and analysis module, used to fuse the collected multimodal data, extract key event information and detect abnormal behavior; The risk prediction and decision support module is used to combine real-time data with historical case data to predict potential risk events and generate response strategies; The police resource dispatch module is used to automatically dispatch police forces, equipment and resources to the scene based on risk prediction and decision-making suggestions, optimize on-site police operations, and update task progress in real time; The data fusion and analysis module includes a data preprocessing submodule, a data fusion submodule, a pattern recognition submodule and a data conflict correction submodule; The data preprocessing submodule is used to perform preprocessing operations on the original data; The data fusion submodule is used to fuse multimodal data using an adaptive weighting algorithm based on the importance and relevance of different modal data; The pattern recognition submodule is used to extract key event information and detect abnormal behavior using deep learning algorithms; The data conflict correction submodule is used to automatically detect and process conflicting or inconsistent data during the data fusion process to ensure the accuracy of the analysis results.

2. The multimodal metaverse policing system according to claim 1, characterized in that: The data acquisition module comprises: The video data acquisition submodule is used to capture the live video stream through the surveillance camera; The audio data acquisition submodule is used to obtain audio signals through a microphone and a voice sensor; The environmental data acquisition submodule is used to obtain on-site environmental data through environmental sensors.

3. The multimodal metaverse policing system according to claim 2, characterized in that: The data conflict correction submodule includes the following contents: Suppose the event in the video data is , the events in the audio data are , then the conflict determination condition is defined as: That is, if and If the events represented are different, conflicts will arise; The data conflict correction submodule also includes: If a conflict is detected, Bayesian inference is used to correct the conflicting data: Final Correction Probability Calculated by Bayes' theorem: in, and Respectively represent the events under given data D and The posterior probability of .

4. The multimodal metaverse policing system according to claim 3, characterized in that: The risk prediction and decision support module includes: The historical data analysis submodule is used to analyze historical police data and build crime pattern models; Real-time risk prediction submodule, used to combine real-time data to predict potential safety risks; The response strategy generation submodule is used to automatically generate corresponding response strategies based on the predicted security risks; The historical data analysis submodule includes the following contents: Build risk scenario models based on historical data: Analyze the time patterns of historical events through time series models and predict the time of occurrence of certain behaviors: in, For time series data, are the parameters of the time series, is the error term; Through clustering algorithms, historical cases are classified and the common characteristics of different types of cases are found: Among them, C is the cluster center, N is the number of data sets or samples, k is the number of clusters, is the feature vector of the case, is the center of the jth class.

5. The multimodal metaverse policing system according to claim 4, characterized in that: The police resource dispatching module includes: The police dispatch submodule is used to automatically dispatch police forces to the locations where they are most needed based on risk assessment results; Equipment scheduling submodule, used to deploy relevant equipment according to on-site needs; The task management submodule is used to track task progress in real time, update the execution status of on-site tasks, and optimize resource utilization.

6. The multimodal metaverse policing system according to claim 5, characterized in that: The police dispatch submodule includes the following contents: Based on real-time risk prediction and historical data analysis, determine the police force requirements for a certain area or event: Use linear programming to solve the police dispatch problem. The goal is to minimize the cost of police dispatch or maximize the efficiency of using police resources: Features police resources, and the arrival time of each police resource is , optimize police dispatch through the following linear programming model: in, represents the time when the i-th police force arrives at the j-th task, is a binary decision variable (1 means scheduled, 0 means not scheduled), and m represents the number of tasks.

7. The multimodal metaverse policing system according to claim 6, characterized in that: The device scheduling submodule includes the following contents: Analyze the type and quantity of equipment required based on the mission type and site conditions; Evaluate the availability of current equipment and determine whether it meets the task requirements. Calculate the optimal equipment scheduling plan based on equipment requirements and available resources: According to the scheduling results, execute equipment scheduling and monitor the equipment usage status in real time; The device scheduling is performed through a priority scheduling algorithm: Features The scheduling priority of each device is The urgency of the task is , then the objective function of equipment scheduling is expressed as: in, is the number of devices.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, it implements the steps of the multimodal metaverse policing processing system described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multimodal metaverse policing processing system according to any one of claims 1 to 7 are implemented.

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