Alarm visualization method, device, equipment and medium

Abstract

The embodiment of the invention discloses an alarm visualization method and device, equipment and a medium. The method comprises the following steps: acquiring geographic information of a preset area, and constructing a three-dimensional model corresponding to the preset area based on the geographic information; obtaining multi-modal alarm data corresponding to the preset area, wherein the multi-modal alarm data comprises alarm data sent by a plurality of platforms; performing data fusion processing on the multi-modal alarm data to obtain fused alarm data; and determining a target alarm area on the three-dimensional model based on the fused alarm condition data, and performing alarm condition visual display in the target alarm area. According to the embodiment of the invention, the target alarm area can be determined on the three-dimensional model based on the fused alarm condition data, and the alarm condition is visually displayed, so that a commander can quickly position the position of the alarm condition, and can visually feel the situation of the alarm condition in a three-dimensional scene, and the efficiency of alarm condition processing and the scientificity of decision making are greatly improved.

Description

Technical Field

[0001] This application relates to the field of police incident handling technology, specifically to a police incident visualization method, device, electronic device, and computer-readable storage medium. Background Technology

[0002] In today's increasingly complex social security situation, with a growing number and types of police incidents, the visualization of police incidents is becoming increasingly important for improving the efficiency of police command and decision-making and enhancing emergency response capabilities. This involves presenting police incident information in an intuitive and dynamic manner.

[0003] However, existing police situation visualization technologies still have significant shortcomings. On the one hand, some systems only display information in a two-dimensional plane, making it difficult to comprehensively and accurately reflect the distribution and correlation of police situations in real three-dimensional space, and failing to provide commanders with an immersive on-site perception. On the other hand, existing technologies have shortcomings in data integration and dynamic updates, making it difficult to access multi-source heterogeneous police situation data in real time, resulting in lag and inaccuracy in the visualized information. Summary of the Invention

[0004] To address the aforementioned technical problems, embodiments of this application provide a method and apparatus for visualizing police situations, an electronic device, a computer-readable storage medium, and a computer program product.

[0005] According to one aspect of the embodiments of this application, a method for visualizing police incidents is provided, comprising: acquiring geographic information of a preset area and constructing a three-dimensional model corresponding to the preset area based on the geographic information; acquiring multimodal police incident data corresponding to the preset area, wherein the multimodal police incident data includes police incident data sent from multiple platforms; performing data fusion processing on the multimodal police incident data to obtain fused police incident data; determining a target alarm area on the three-dimensional model based on the fused police incident data, and visualizing the police incident in the target alarm area.

[0006] According to one aspect of the embodiments of this application, the step of performing data fusion processing on the multimodal alarm data to obtain fused alarm data includes: obtaining timestamp information and location information in the multimodal alarm data; and performing fusion processing on the multimodal alarm data based on the timestamp information and the location information to obtain fused alarm data.

[0007] According to one aspect of the embodiments of this application, the method further includes: determining the time series corresponding to the alarm data based on the timestamp information; determining the spatial index corresponding to the alarm data based on the location information, so as to determine the target alarm area on the three-dimensional model based on the spatial index.

[0008] According to one aspect of the embodiments of this application, the method further includes: determining the alarm type corresponding to the fused alarm data; determining an alarm display model based on the alarm type, the time series, and the target alarm area, wherein the alarm display model includes at least one or more of color, shape, icon, and light.

[0009] According to one aspect of the embodiments of this application, the method further includes: acquiring real-time multimodal data according to a preset acquisition strategy; updating the target alarm area on the three-dimensional model based on the real-time multimodal data; and visually displaying the alarm situation in the updated target alarm area.

[0010] According to one aspect of the embodiments of this application, the plurality of platforms include a monitoring platform, a geofence, and a duty deployment. The method further includes: acquiring multimodal police incident data based on the monitoring platform, the geofence, and the duty deployment; acquiring historical multimodal police incident data; and determining a police force deployment strategy based on the historical multimodal police incident data and the multimodal police incident data. The police force deployment strategy includes target patrol routes and target control areas.

[0011] According to one aspect of the embodiments of this application, the method further includes: determining police status information based on a duty deployment platform, the police status information including the number of police officers, the location of police officers, and the status of police officers; determining high-incidence areas and high-incidence periods of police incidents based on the multimodal police incident data; and adjusting at least one of the number of police officers, the location of police officers, and the status of police officers based on the high-incidence areas and the high-incidence periods of police incidents to determine a police deployment strategy.

[0012] According to one aspect of the embodiments of this application, a police situation visualization device is provided. The device includes: a construction module, configured to acquire geographic information of a preset area and construct a three-dimensional model corresponding to the preset area based on the geographic information; an acquisition module, configured to acquire multimodal police situation data corresponding to the preset area, the multimodal police situation data including police situation data sent from multiple platforms; a fusion module, configured to perform data fusion processing on the multimodal police situation data to obtain fused police situation data; and a visualization module, configured to determine a target alarm area on the three-dimensional model based on the fused police situation data and perform police situation visualization display in the target alarm area.

[0013] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the alarm visualization method as described above.

[0014] According to one aspect of the embodiments of this application, a computer-readable storage medium is provided, on which computer-readable instructions are stored, which, when executed by a computer's processor, cause the computer to perform the alarm visualization method as described above.

[0015] According to one aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps in the alarm visualization method as described above.

[0016] In the technical solution provided by the embodiments of this application, by acquiring the geographic information of a preset area and constructing a corresponding three-dimensional model, a highly realistic and accurate spatial carrier for displaying police incidents is provided, enabling commanders to intuitively understand the spatial layout and geographic features of the preset area; acquiring multimodal police incident data sent from multiple platforms ensures the comprehensiveness and diversity of police incident information, avoiding the limitations brought by data from a single platform; the multimodal police incident data is fused and processed, effectively integrating data from different sources and in different formats, eliminating redundancy and conflicts between data, and obtaining accurate and unified fused police incident data; finally, based on the fused police incident data, the target alarm area is determined on the three-dimensional model and the police incident is visualized, which not only allows commanders to quickly locate the location of the incident, but also allows them to intuitively feel the situation in the three-dimensional scene, greatly improving the efficiency of police incident handling and the scientific nature of decision-making, and helping to allocate police resources more efficiently and formulate response strategies, thereby better maintaining social order and public safety.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0019] Figure 1 This is a schematic diagram illustrating an implementation environment for visualizing police situations, as shown in an exemplary embodiment of this application.

[0020] Figure 2 This is a flowchart illustrating an exemplary embodiment of the alarm visualization method of this application;

[0021] Figure 3 This is a flowchart illustrating a police situation visualization method, as shown in another exemplary embodiment of this application;

[0022] Figure 4 This is a flowchart illustrating a police situation visualization method, as shown in another exemplary embodiment of this application;

[0023] Figure 5 This is a flowchart illustrating a police situation visualization method, as shown in another exemplary embodiment of this application;

[0024] Figure 6 This is a flowchart illustrating a police situation visualization method, as shown in another exemplary embodiment of this application;

[0025] Figure 7 This is a flowchart illustrating a police situation visualization method, as shown in another exemplary embodiment of this application;

[0026] Figure 8 This is a flowchart illustrating a police situation visualization method, as shown in another exemplary embodiment of this application;

[0027] Figure 9 This is a block diagram illustrating an alarm visualization device in an exemplary embodiment of this application;

[0028] Figure 10 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0029] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0030] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0031] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0032] In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0033] First, it's important to note that in today's increasingly complex social security situation, with a growing number and types of police incidents, incident visualization has become a key element in improving the efficiency of police command and decision-making and enhancing emergency response capabilities. When facing complex incidents, commanders need to comprehensively consider various factors, such as the surrounding geographical environment, population distribution, and traffic conditions. Incident visualization systems can combine this relevant geographical information with incident data, clearly displaying it on a 3D model. For example, when planning police deployment, commanders can intuitively see the distance of each police unit from the incident location, the surrounding road conditions, etc., allowing them to quickly determine which police units can reach the scene rapidly and how to allocate resources to form the optimal encirclement or rescue posture, improving the scientific accuracy of decision-making. Modern policing often requires collaboration among multiple departments, such as public security, fire, medical, and traffic. Incident visualization platforms provide a unified information sharing and decision-making platform for these departments. Personnel from all departments can see the real-time dynamics and related information of the incident on the same visualization interface, jointly analyze and discuss, and formulate collaborative operational plans. For example, when dealing with large-scale fire alarms, fire departments can determine the best fire extinguishing routes and rescue plans based on visualized information, medical departments can plan the transfer routes and treatment points for the injured in advance, and transportation departments can promptly manage surrounding traffic to ensure the smooth passage of rescue vehicles. The various departments can achieve efficient collaboration through visualized information, thereby improving the overall decision-making efficiency.

[0034] Figure 1 This is a schematic diagram illustrating an implementation environment for alarm visualization, as shown in an exemplary embodiment of this application. Figure 1 As shown, server 110 can acquire geographic information of a preset area, including topography, latitude and longitude, etc. Server 110 then constructs a 3D model corresponding to the preset area based on the geographic information. Server 110 can also acquire multimodal alarm data through multiple platforms 120. Server 110 then performs data fusion processing on the multimodal alarm data to obtain fused alarm data. Finally, server 110 determines the target alarm area on the 3D model based on the fused alarm data and displays the alarm information in the target alarm area. This achieves 3D visualization of the alarm situation.

[0035] Among them, the server-side 110 can be, for example, an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms, and there are no restrictions here.

[0036] However, existing police incident visualization technologies still have significant shortcomings. On the one hand, some systems only display incidents on a two-dimensional plane, making it difficult to comprehensively and accurately reflect the distribution and correlation of incidents in real three-dimensional space, and failing to provide commanders with an immersive on-site perception. On the other hand, existing technologies have shortcomings in data integration and dynamic updates, making it difficult to access multi-source heterogeneous incident data in real time and to efficiently clean, integrate, and synchronously update it, resulting in lag and inaccuracy in the visualized information. In addition, in terms of correlation analysis and judgment assistance functions, existing technologies do not have sufficient depth in mining incident data, lack accurate prediction of potential incident trends and patterns, and cannot meet the urgent need for scientific basis in modern police command and decision-making.

[0037] To address these issues, embodiments of this application propose a method for visualizing police situations, a device for visualizing police situations, an electronic device, a computer-readable storage medium, and a computer program product, which will be described in detail below.

[0038] Please see Figure 2 , Figure 2 This is a flowchart illustrating an exemplary embodiment of the alarm visualization method of this application. This method can be applied to... Figure 1 The implementation environment shown is specifically executed by server 110 within that implementation environment. It should be understood that this method can also be applied to other exemplary implementation environments and specifically executed by devices in other implementation environments; this embodiment does not limit the implementation environment to which the method is applicable.

[0039] like Figure 3 As shown, in an exemplary embodiment, the alarm visualization method includes at least steps S210 to S240, which are described in detail below:

[0040] Step S210: Obtain the geographic information of the preset area and construct a three-dimensional model corresponding to the preset area based on the geographic information.

[0041] For example, comprehensively collecting geographic information about the predetermined area is fundamental to constructing a 3D model. Topographic data can be obtained using various professional methods. Satellite remote sensing technology can observe the Earth's surface over a wide area from high altitudes. Through high-resolution satellite imagery, macroscopic geomorphic features such as topographic relief, mountain range orientation, and river distribution can be clearly captured. Aerial photogrammetry utilizes aircraft equipped with specialized equipment to take low-altitude photographs of the target area, acquiring more detailed and high-precision geographic information. The captured imagery data can be accurate to small-scale geomorphic details on the ground, such as building outlines and road directions. Furthermore, ground surveying is indispensable. Surveyors use total stations, GPS positioning devices, and other equipment to conduct on-site measurements of key points within the predetermined area, obtaining accurate coordinates and elevation data. This data is crucial for correcting and refining the overall geographic information. Simultaneously, existing geographic information databases, such as topographic maps and geological maps, can be collected. These materials contain rich historical geographic information, providing reference and supplementation for 3D model construction. After data preprocessing, a suitable 3D modeling algorithm needs to be selected to construct the 3D model of the predetermined area. Common 3D modeling algorithms include Triangular Irregular Network (TIN) modeling and Digital Elevation Model (DEM) modeling. The TIN modeling algorithm connects discrete elevation points into an irregular network of triangles, accurately representing the undulations of terrain, and is particularly suitable for areas with complex and undulating terrain. This algorithm automatically generates the optimal triangle network based on the distribution of elevation points and terrain features, making the model more closely resemble the actual terrain. The DEM modeling algorithm, on the other hand, represents terrain elevation using a regular grid. It divides a predefined area into a series of regular grids, assigning each grid a corresponding elevation value, thereby constructing a terrain model.

[0042] Step S220: Obtain multimodal alarm data corresponding to the preset area. The multimodal alarm data includes alarm data sent from multiple platforms.

[0043] For example, acquiring multimodal police incident data corresponding to a preset area is a crucial foundation for building an intelligent policing system. This data comes from multiple platforms, covering information of different types and formats. These platforms include, but are not limited to: alarm receiving platforms: this is the most direct source of police incidents, recording public alarm information, including alarm time, location, and incident type; public security monitoring systems: automatically detecting abnormal events through technologies such as video surveillance and facial recognition; social media and online platforms: scraping help requests or abnormal event reports from social media using web crawling technology; IoT devices: such as smart access control and vehicle monitoring systems, uploading abnormal data in real time; and third-party data services: such as weather forecasts and traffic flow data, which, while not directly related to police incidents, may influence the probability of incidents occurring. Because the data formats and structures of different platforms may vary significantly, data format standardization is necessary. Furthermore, to ensure the real-time nature of police incident data, real-time updates and synchronization are required, for example, through real-time data stream processing.

[0044] Step S230: Perform data fusion processing on the multimodal alarm data to obtain fused alarm data.

[0045] Step S240: Determine the target alarm area on the 3D model based on the fused alarm data, and visualize the alarm in the target alarm area.

[0046] For example, firstly, it is necessary to perform spatiotemporal alignment on multimodal police incident data from different platforms. Specifically, the timestamps of different platforms are unified to the same time zone, and data transmission delays are considered to ensure that all police incident data are aligned in the time dimension. Additionally, geographical location data in different coordinate systems (such as GPS coordinates and map projection coordinates) are converted to a unified coordinate system to ensure spatial consistency. Based on spatiotemporal alignment, data association and matching are performed. For example, the same event reported by different platforms can be associated using event descriptions, geographical locations, and time windows. Alternatively, natural language processing (NLP) techniques can be used to extract key entities (such as names of people, places, and license plate numbers) from text data, and entity disambiguation and coreference resolution can be performed.

[0047] Then, spatial clustering algorithms (such as DBSCAN and K-means) are used to cluster the locations of incidents, identifying high-incidence areas. Combined with historical incident data, geographic information, and socioeconomic data, a risk assessment model is constructed to predict potential future incident areas. This allows for dynamic adjustment of the boundaries and range of alert areas based on real-time incident data, ensuring consistency with the current situation. After determining the target alert area, a 3D model is visualized. Specifically, a pre-built 3D geographic model is loaded into the visualization platform, ensuring the model's detail and accuracy meet requirements. The target alert area is then highlighted on the 3D model, using different colors or icons to distinguish different types of incidents (e.g., red for emergency incidents, yellow for general incidents). Furthermore, animation effects can be used to demonstrate the spread of incidents or the dynamic process of police deployment, enhancing the visualization effect.

[0048] In some embodiments of this application, a three-dimensional model is constructed by acquiring geographic information of a preset area, providing an intuitive spatial carrier for displaying police incidents; integrating and fusing police incident data from multiple platforms and modalities can comprehensively and accurately integrate various types of police incident information; determining the target alarm area based on the fused police incident data and visually displaying it on the three-dimensional model helps to quickly locate the location of the police incident, intuitively present the police situation, improve the efficiency and accuracy of police incident handling, and provide strong support for relevant decision-making.

[0049] Furthermore, based on the above embodiments, please refer to... Figure 3 In one exemplary embodiment provided in this application, the specific implementation process of performing data fusion processing on multimodal alarm data to obtain fused alarm data may further include steps S310 and S320, which are described in detail below:

[0050] Step S310: Obtain the timestamp information and location information from the multimodal alarm data;

[0051] Step S320: Based on timestamp information and location information, the multimodal alarm data is fused to obtain fused alarm data.

[0052] For example, timestamp information is extracted from police incident data obtained from various platforms. Specifically, the time formats of different platforms (such as ISO 8601, Unix timestamps, and local time strings) are identified and converted into a unified UTC time format. For timestamps containing time zone information, time zone conversion is performed to ensure that all time data is based on the same time zone. Optionally, the precision of timestamps can also be standardized, for example, converting millisecond-level timestamps to second-level timestamps, or retaining higher precision as needed. On the other hand, the coordinate systems used by different platforms (such as WGS84, GCJ-02, and BD-09) are identified and coordinate system conversion is performed. For example, they can be unified to the WGS84 coordinate system. For text-based location descriptions (such as "east side of People's Square"), geocoding services (such as Baidu Maps API and Gaode Maps API) are used to convert them into latitude and longitude coordinates. Optionally, the precision of location data can also be standardized, for example, converting meter-level precision to ten-meter-level precision to reduce data noise.

[0053] In addition, data fusion weights can be assigned based on the spatiotemporal characteristics of the alarm data. For example, weights can be assigned based on temporal proximity, with the most recent alarm data receiving higher weights; or weights can be assigned based on spatial proximity, with data closer to the core area receiving higher weights. In some feasible embodiments, the reliability of the data source platform can also be considered, such as official platform data being more reliable than social media data.

[0054] In some embodiments of this application, timestamps and location information are extracted from multimodal police incident data as key elements, and the data is then fused based on these elements. Timestamp information ensures the alignment of police incident data from different sources in the time dimension, avoiding information confusion due to time differences; location information enables precise spatial correlation of police incidents, allowing the integration of incidents from different platforms but involving the same area. The fused police incident data obtained in this way is more accurate and consistent in both time and space dimensions, helping to comprehensively and clearly present the full picture of the incident, providing a reliable basis for subsequent incident analysis and response decisions, and improving the overall efficiency and accuracy of emergency response.

[0055] Furthermore, based on the above embodiments, please refer to... Figure 4 In one exemplary embodiment provided in this application, the specific implementation process of the above-mentioned alarm visualization method further includes steps S410 and S420, which are described in detail below:

[0056] Step S410: Determine the time series corresponding to the alarm data based on the timestamp information;

[0057] Step S420: Determine the spatial index corresponding to the alarm data based on the location information, and determine the target alarm area on the three-dimensional model based on the spatial index.

[0058] Following the above embodiments, timestamp extraction and standardization are performed on multimodal police incident data from different platforms. Various time formats (such as Unix timestamps and local time strings) are uniformly converted to UTC time format to ensure data consistency. The standardized timestamps are arranged chronologically to construct a time series for each police incident event. The time series is segmented to identify high-frequency periods and periodic characteristics of incidents, for example, by analyzing the periodicity of the time series using Fourier transform. Time series analysis methods (such as sliding window averaging and exponential smoothing) are used to identify trends and abnormal fluctuations in incident occurrences. Furthermore, historical data can be combined to predict the probability of incidents occurring in the future, providing time-dimensional decision support for police deployment. Location information in the police incident data undergoes coordinate system transformation and geocoding, converting textual descriptions of location information into unified latitude and longitude coordinates. Spatial encoding techniques such as geohash or quadtrees are used to encode the location information, facilitating subsequent spatial index construction. Based on the encoded location information, a spatial index structure (such as an R-tree or R* tree) is constructed to accelerate spatial queries and range searches. The spatial index is then associated with a 3D geographic model to ensure that each location information corresponds to a specific area in the 3D model. Range searches are performed using the spatial index to identify spatial areas with high alarm incidence within a specific time window. Identified target alarm areas are highlighted on the 3D geographic model, using different colors or icons to distinguish different types of alarms. Interactive functionality is provided, allowing users to observe alarm areas from different perspectives and displaying the spatiotemporal evolution trends of alarms in conjunction with time-series data.

[0059] In some embodiments of this application, time series and spatial indexes of alarm data can be constructed based on timestamps and location information, thereby accurately determining and visualizing target alarm areas on a three-dimensional model, providing strong spatiotemporal situational awareness support for police command.

[0060] Furthermore, based on the above embodiments, please refer to... Figure 5 In one exemplary embodiment provided in this application, the specific implementation process of the above-mentioned police situation visualization method may further include steps S510 and S520, which are described in detail below:

[0061] Step S510: Determine the type of police incident corresponding to the merged police incident data;

[0062] Step S520: Determine the alarm display model based on the alarm type, time series, and target alarm area. The alarm display mode includes at least one or more of color, shape, icon, and light.

[0063] For example, key features are extracted from the fused multimodal police incident data, including keywords in text descriptions, object features in images or videos, and statistical features from sensor data. Natural Language Processing (NLP) techniques are used to perform sentiment analysis and keyword extraction on the text data to identify key information in the incident descriptions. Machine learning classification models (such as random forests, support vector machines, or deep learning models) can also be built to classify the police incident data based on the extracted features, determining the corresponding incident type (such as robbery, fire, traffic accident, etc.). Then, mapping rules for the incident display modes (color, shape, icon, light) are formulated according to the incident type, urgency, and impact range. For example: color mapping: red represents high-urgency incidents (such as robbery), yellow represents medium-urgency incidents (such as traffic accidents), and green represents low-urgency incidents (such as noise complaints); specific icons are assigned to different incident types (such as a flame icon for fire and a vehicle icon for traffic accidents); light effect mapping: in the 3D model, flashing light effects represent developing incidents, and constant light represents stable incidents.

[0064] Furthermore, by combining time-series data, the development trend of alarms can be analyzed. For example, for alarms with a long duration, the display color can be gradually deepened to indicate an escalation of urgency. The range and intensity of the display mode can be adjusted using spatial information of the target alarm area. For instance, in high-density alarm areas, more conspicuous color and icon combinations can be used. A dynamic adjustment mechanism should be established to automatically update the display mode based on real-time changes in the alarm (such as escalation of urgency or area spread). A user interface should be provided to allow commanders to manually adjust the display mode according to actual needs, such as enlarging icons in specific areas or changing the color scheme.

[0065] In some embodiments of this application, by determining the type of alarm data fused from the alarm data, a basic classification basis is provided for subsequent targeted processing. Then, by combining the alarm type, time series, and target alarm area, an alarm display model is determined, presenting the alarm through a combination of various visual elements such as color, shape, icon, and light. This comprehensive approach can intuitively and comprehensively display the characteristics of the alarm. Different alarm types correspond to different display modes for easy differentiation, the time series reflects the dynamic development of the alarm, and the target alarm area clearly indicates its spatial location, helping decision-makers to quickly grasp the overall picture of the alarm and improve the efficiency of alarm analysis and response.

[0066] Furthermore, based on the above embodiments, please refer to... Figure 6 In one exemplary embodiment provided in this application, the specific implementation process of the above-mentioned police situation visualization method may further include steps S610 and S620, which are described in detail below:

[0067] Step S610: Acquire real-time multimodal data according to the preset acquisition strategy;

[0068] Step S620: Update the target alarm area on the 3D model based on real-time multimodal data, and visualize the alarm situation in the updated target alarm area.

[0069] For example, according to a preset acquisition strategy (such as setting data source priority, acquisition frequency, trigger conditions, etc.), multimodal data such as text, images, videos, and sensor values ​​are acquired in real time from various sources such as sensors, monitoring platforms, and alarm systems. After preprocessing the acquired real-time data, such as cleaning, format conversion, and spatiotemporal alignment, the current alarm situation is reassessed in combination with historical data and spatial analysis algorithms. The boundaries, range, and risk level of the target alarm area in the 3D model are dynamically updated. According to the preset alarm type and display mode mapping rules (such as using different colors, flashing frequencies, icon styles, etc. to distinguish the urgency and type of the alarm), the alarm information is rendered and visualized in real time in the updated target alarm area, ensuring that the command personnel can intuitively perceive the latest alarm dynamics and thus make quick decisions and deployments.

[0070] In some embodiments of this application, real-time multimodal data is acquired through a preset acquisition strategy to ensure timely capture of the latest information and guarantee the timeliness and comprehensiveness of the data. Based on this real-time data, the target alarm area on the 3D model is updated so that the 3D model always keeps pace with the actual alarm situation and accurately reflects the dynamic changes of the alarm situation. The alarm situation is visualized in the updated target alarm area, which allows decision-makers to see the real-time changes of the alarm situation intuitively and clearly, which helps to make accurate decisions quickly, improve the efficiency and effectiveness of responding to sudden alarms, and enhance the scientific nature and timeliness of emergency management.

[0071] Furthermore, based on the above embodiments, please refer to... Figure 7 In one exemplary embodiment provided in this application, the aforementioned multiple platforms include a monitoring platform, a geofence, and a duty deployment. The specific implementation process of the aforementioned alarm visualization may further include steps S710 and S720, which are detailed below:

[0072] Step S710: Obtain multimodal alarm data based on the monitoring platform, geofencing, and duty deployment;

[0073] Step S720: Obtain historical multimodal police incident data, and determine police deployment strategy based on historical multimodal police incident data and multimodal police incident data. The police deployment strategy includes target patrol routes and target prevention and control areas.

[0074] For example, in the process of building an intelligent police command system, to comprehensively and accurately grasp the police situation and optimize the allocation of police resources, this solution integrates data resources from multiple platforms such as monitoring platforms, geofencing, and duty deployment. First, based on the monitoring platform, video stream data is collected in real time using its camera equipment distributed throughout key urban areas. Through intelligent video analysis technologies, such as target detection and behavior recognition, abnormal events (such as crowd gatherings, fights, and vehicles driving in the wrong direction) are automatically identified, and relevant police information is extracted from them. At the same time, with the help of geofencing technology, key areas (such as schools, shopping malls, and transportation hubs) are pre-defined on electronic maps. When a specific target (such as key personnel or suspicious vehicles) enters or leaves these areas, an alarm mechanism is triggered to obtain multimodal police data, including target location, entry / exit time, and target type. In addition, combined with the duty deployment platform, information such as the real-time location of police forces, patrol routes, and task status is collected to understand the current distribution and working status of police forces. This data also serves as an important component of multimodal police data.

[0075] While acquiring real-time multimodal emergency data, the system also retrieves historical multimodal emergency data from the database. This historical data covers emergency occurrences in different time periods and regions, including detailed information such as emergency type, time of occurrence, location, and handling results. Based on this rich historical and real-time data, data mining and machine learning algorithms are used for in-depth analysis. On the one hand, spatiotemporal clustering analysis identifies high-incidence areas and time periods of emergency incidents, revealing spatiotemporal patterns in emergency occurrences. On the other hand, association rule mining is used to analyze the correlation between different emergency types and the potential connection between emergency incidents and surrounding environmental factors (such as population density, business activities, and weather conditions).

[0076] Based on the above analysis, a scientific and reasonable police deployment strategy was formulated. Targeted patrol routes were planned for high-incidence areas and times of high crime, enabling police forces to more accurately cover key areas and improve patrol efficiency and response speed. Simultaneously, based on the type and relevance of crime, target control areas were identified, and police resources were rationally allocated to strengthen control in these areas, such as increasing patrol frequency and setting up temporary checkpoints. Furthermore, the police deployment strategy also considered the real-time location and task status of police officers, ensuring efficient coordination during mission execution and avoiding resource waste and task conflicts. In this way, dynamic optimization of police deployment based on multi-platform data fusion was achieved, effectively enhancing the initiative and targeting of policing work and providing strong support for maintaining social security and stability.

[0077] In some embodiments of this application, multimodal police incident data is acquired through a comprehensive monitoring platform, geofencing, and duty deployment. This allows for the comprehensive aggregation of incident information from different channels and dimensions, providing a rich and diverse data foundation for subsequent analysis. Simultaneously, historical multimodal police incident data is introduced and combined with current incident data to determine police deployment strategies. This approach considers both past incident patterns and trends, as well as current realities, making the established target patrol routes and target control areas more scientific and targeted. This helps to rationally allocate police resources, improve the accuracy and efficiency of police deployment, enhance the ability to prevent and respond to potential incidents, and improve the overall level of public security and crime prevention.

[0078] Furthermore, based on the above embodiments, please refer to... Figure 8 In one exemplary embodiment provided in this application, the specific implementation process of the above-mentioned alarm visualization method may further include steps S810 to S830, which are described in detail below:

[0079] Step S810: Determine police status information based on the duty deployment platform. The police status information includes the number of police officers, their locations, and their status.

[0080] Step S820: Determine high-incidence areas and high-incidence periods of police incidents based on multimodal police incident data;

[0081] Step S830: Adjust at least one of the following based on the high-crime area and high-crime phase: number of police officers, location of police officers, and status of police officers, in order to determine the police deployment strategy.

[0082] For example, to achieve precise deployment and efficient scheduling of police resources, this solution first relies on the duty deployment platform to conduct in-depth data mining. This platform integrates real-time police force information from all levels and regions within the police system. By interacting with the platform's database, it accurately extracts police status information, including the number of currently available police officers, the precise location coordinates of each officer on the electronic map, and the specific status of the officers (such as performing patrol duties, being on standby, or handling incidents). This constructs a comprehensive and dynamic panoramic view of police resources, providing a solid data foundation for subsequent decision-making.

[0083] Simultaneously, multi-dimensional analysis is conducted on multimodal police incident data. This data comes from a wide range of sources, including but not limited to abnormal event information derived from surveillance video analysis, geofencing-triggered data on target activity in key areas, and various police reports received by alarm systems. On one hand, spatial analysis techniques are used to cluster the locations of incidents, identifying high-density distribution areas in geographic space—i.e., high-incidence areas. On the other hand, time series analysis methods are employed to statistically analyze and mine the timing of incidents, identifying concentrated periods of occurrence and determining high-incidence periods. Through these two aspects of analysis, a comprehensive understanding of the spatiotemporal patterns of police incidents is achieved, providing precise and targeted guidance for police deployment.

[0084] Based on the precise identification of high-incidence areas and times of incidents, the system automatically activates a dynamic adjustment mechanism for police deployment strategies. For high-incidence areas, if the current number of police officers is insufficient to effectively handle potential incidents, the system will intelligently plan the allocation of reinforcements based on the distribution of surrounding police forces and their mission status, increasing the number of officers in the area. If it is found that the existing police positions cannot effectively cover the high-incidence area, the system will combine the path planning algorithm of the Geographic Information System (GIS) to plan the optimal movement route for the police force, adjusting their positions to ensure that they can quickly reach key areas. Simultaneously, based on the characteristics of high-incidence times, the system will rationally adjust the status of police forces. For example, before the peak period, standby officers will be switched to patrol status, increasing patrol frequency and density; or during incident handling, the system will dynamically adjust the police cooperation status according to the situation on the ground, ensuring efficient collaborative operations among officers. Through this series of adjustments to police deployment strategies based on the spatiotemporal characteristics of incidents, precise matching of police resources with the incident situation is achieved, significantly improving the response speed and handling efficiency of police work, and effectively maintaining social security and stability.

[0085] In some embodiments of this application, police status information such as the number, location, and status of police officers is obtained through a duty deployment platform to clearly understand the distribution and status of existing police resources. Combined with multimodal crime data analysis, high-incidence areas and time periods of crimes are identified, accurately pinpointing crime risk points. Based on this, at least one of the following—the number, location, or status of police officers—is flexibly adjusted according to the characteristics of high-incidence crimes, ensuring that police deployment closely aligns with actual crime needs. This optimizes the allocation of police resources, improves the response speed and handling capabilities of police forces to high-incidence crimes, enhances the initiative and effectiveness of public security prevention and control, and improves the overall efficiency and quality of policing work.

[0086] Figure 9 This is a block diagram illustrating an exemplary embodiment of an alarm visualization device according to this application. The device can be applied to… Figure 1The implementation environment shown is specifically configured in server 110. This device can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applicable.

[0087] like Figure 9 As shown, the exemplary alarm visualization device includes: a construction module 910, used to acquire geographic information of a preset area and construct a three-dimensional model corresponding to the preset area based on the geographic information; an acquisition module 920, used to acquire multimodal alarm data corresponding to the preset area, the multimodal alarm data including alarm data sent from multiple platforms; a fusion module 930, used to perform data fusion processing on the multimodal alarm data to obtain fused alarm data; and a visualization module 940, used to determine the target alarm area on the three-dimensional model based on the fused alarm data and to perform alarm visualization display in the target alarm area.

[0088] According to one aspect of the embodiments of this application, the fusion module 930 is further configured to acquire timestamp information and location information in the multimodal alarm data; and perform fusion processing on the multimodal alarm data based on the timestamp information and location information to obtain fused alarm data.

[0089] According to one aspect of the embodiments of this application, the fusion module 930 is further configured to: determine the time series corresponding to the alarm data based on timestamp information; determine the spatial index corresponding to the alarm data based on location information, so as to determine the target alarm area on the three-dimensional model based on the spatial index.

[0090] According to one aspect of the embodiments of this application, the above-mentioned fusion module 930 is further configured to determine the alarm type corresponding to the fused alarm data; and determine the alarm display model based on the alarm type, time series and target alarm area, wherein the alarm display mode includes at least one or more of color, shape, icon and light.

[0091] According to one aspect of the embodiments of this application, the visualization module 940 is further configured to: acquire real-time multimodal data according to a preset acquisition strategy; update the target alarm area on the three-dimensional model based on the real-time multimodal data; and display the alarm situation in the updated target alarm area.

[0092] According to one aspect of the embodiments of this application, the above-mentioned police situation visualization device further includes: a deployment module, used to acquire multimodal police situation data based on a monitoring platform, a geofence, and duty deployment; acquire historical multimodal police situation data, and determine a police force deployment strategy based on the historical multimodal police situation data and the multimodal police situation data, wherein the police force deployment strategy includes target patrol routes and target prevention and control areas.

[0093] According to one aspect of the embodiments of this application, the above-mentioned deployment module is further configured to: determine police status information based on the duty deployment platform, the police status information including the number of police officers, the location of police officers, and the status of police officers; determine high-incidence areas and high-incidence periods of police incidents based on multimodal police incident data; and adjust at least one of the number of police officers, the location of police officers, and the status of police officers based on the high-incidence areas and high-incidence periods of police incidents to determine a police deployment strategy.

[0094] It should be noted that the alarm visualization device and the alarm visualization method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the alarm visualization device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0095] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, enable the electronic device to implement the alarm visualization method provided in the above embodiments.

[0096] Figure 10 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 10 The computer system 1000 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0097] like Figure 10 As shown, the computer system 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from storage portion 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004.

[0098] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0099] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of this application.

[0100] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0101] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0102] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0103] Another aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned alarm visualization method. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not incorporated into the electronic device.

[0104] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the alarm visualization method provided in the various embodiments described above.

[0105] The above description is merely a preferred exemplary embodiment of this application and is not intended to limit the implementation of this application. Those skilled in the art can easily make corresponding modifications or alterations based on the main concept and spirit of this application. Therefore, the scope of protection of this application should be determined by the scope of protection claimed in the claims.

Claims

1. A method for visualizing police incidents, characterized in that, include: Obtain geographic information of a preset region, and construct a three-dimensional model corresponding to the preset region based on the geographic information; Acquire multimodal alarm data corresponding to the preset area, wherein the multimodal alarm data includes alarm data sent from multiple platforms; The multimodal alarm data is fused to obtain fused alarm data. Based on the fused alarm data, the target alarm area on the 3D model is determined, and the alarm is visualized and displayed in the target alarm area.

2. The method as described in claim 1, characterized in that, The process of fusing the multimodal alarm data to obtain fused alarm data includes: Obtain the timestamp and location information from the multimodal alarm data; The multimodal police incident data is fused based on the timestamp information and the location information to obtain fused police incident data.

3. The method as described in claim 2, characterized in that, The method further includes: The time series corresponding to the alarm data is determined based on the timestamp information; Based on the location information, the spatial index corresponding to the alarm data is determined, and the target alarm area on the three-dimensional model is determined based on the spatial index.

4. The method as described in claim 3, characterized in that, The method further includes: Determine the type of police incident corresponding to the fused police incident data; An alarm display model is determined based on the alarm type, the time series, and the target alarm area. The alarm display mode includes at least one or more of color, shape, icon, and light.

5. The method as described in claim 1, characterized in that, The method further includes: Acquire real-time multimodal data according to the preset acquisition strategy; The target alarm area on the three-dimensional model is updated based on the real-time multimodal data, and the alarm situation is visualized in the updated target alarm area.

6. The method as described in claim 1, characterized in that, The multiple platforms include a monitoring platform, geofencing, and duty deployment; the method further includes: Multimodal alarm data is acquired based on the monitoring platform, the geofence, and the duty deployment. Historical multimodal police incident data is acquired, and a police deployment strategy is determined based on the historical multimodal police incident data and the multimodal police incident data. The police deployment strategy includes target patrol routes and target control areas.

7. The method as described in claim 6, characterized in that, The method further includes: Police status information is determined based on the duty deployment platform, including the number of police officers, their locations, and their status. Based on the multimodal police incident data, high-incidence areas and high-incidence time periods of police incidents are determined; Based on the high-crime areas and the high-crime phases, at least one of the following is adjusted: the number of police officers, the location of police officers, and the status of police officers, in order to determine the police deployment strategy.

8. A police situation visualization device, characterized in that, The device includes: A construction module is used to acquire geographic information of a preset region and construct a three-dimensional model corresponding to the preset region based on the geographic information. The acquisition module is used to acquire multimodal alarm data corresponding to the preset area, wherein the multimodal alarm data includes alarm data sent by multiple platforms; The fusion module is used to perform data fusion processing on the multimodal alarm data to obtain fused alarm data; The visualization module is used to determine the target alarm area on the three-dimensional model based on the fused alarm data, and to visualize the alarm in the target alarm area.

9. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the alarm visualization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-readable instructions, which, when executed by the computer's processor, cause the computer to perform the alarm visualization method according to any one of claims 1 to 7.

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