143 results about "Color-coding" patented technology

The invention discloses an underwater robot inspection obstacle avoidance control method and system, and relates to the technical field of robots, an event camera is introduced to construct a two-dimensional event response distribution diagram, local feature vectors acquired by multiple sensors are fused, a semantic point cloud is formed, and a geometric, semantic and dynamic residual triple optimization mechanism is constructed in combination with a historical environment map. According to the method, depth alignment of a current state and a historical map is realized, the dynamic obstacle identification precision and the adaptive ability of obstacle avoidance path planning are further improved, risk level division can be carried out on an updated environment map through calculation of a dynamic confidence coefficient C and a color coding mechanism, high-dynamic-risk, medium-risk and low-risk areas are distinguished, and the accuracy of obstacle avoidance is improved. According to the method, the intelligent decision-making capability of path selection is effectively enhanced, and on the basis, the data in the red area is preferentially uploaded by combining a return priority scheduling mechanism, so that the consistency matching of the task risk level and the data uploading priority is realized, and redundant data transmission is reduced.

The invention provides a digital human generation system based on a visual autoregression model, and the system introduces a BSQ multi-scale image word segmentation device, a condition image guide network and a time sequence attention layer based on the visual autoregression model to construct a digital human generation model, and adjusts the time sequence attention weight distribution through a temperature map. Target digital human image sequence generation guided by a human identity tag and a color coding image sequence is realized, and the visual generation quality and the time sequence consistency of the generated image sequence are improved; lPIPS perception loss is introduced during model training, direct constraint on the visual quality of the generated image is realized, and the visual generation quality of the generated image is further improved; in the digital human image sequence generation stage, a frame insertion smooth reasoning method is adopted, and the time sequence consistency of the generated image sequence is further improved; according to the invention, the modules cooperate to realize the generation of the motion-controllable target digital human image sequence, and the image sequence generation speed is obviously improved on the premise of ensuring the quality of the generated image.

The invention relates to the technical field of intelligent management, and particularly discloses a building intelligent management method and system based on a BIM technology, and the method comprises the steps: firstly constructing a BIM model of a building, inputting the equipment operation and maintenance information of building equipment into the BIM model, and collecting the operation state data of the building equipment in real time through a sensor group, a deep learning algorithm is introduced, feature extraction and deep interaction response analysis are performed on the real-time operation state data and the historical operation and maintenance information of the building equipment, and the historical operation and maintenance data and the real-time state of the equipment are combined to mine a potential fault mode of the equipment, so that intelligent fault diagnosis of the building equipment is realized; and the health state of the equipment is visually displayed in the BIM model through color coding. In this way, the operation and maintenance efficiency and the fault diagnosis accuracy of the building equipment can be improved, potential fault risks can be found and early warned in time, and meanwhile the intelligent level of building management is enhanced.

The invention discloses a high-fidelity three-dimensional Gaussian sputtering (3DGS) lightweight method for resource-constrained equipment. The method aims at solving the problems of high storage and computing resource consumption caused by the fact that a large number of parameters are stored in an existing 3DGS technology, and geometric distortion possibly occurring when details of a scene center are processed is overcome. The core of the method lies in a multi-stage progressive optimization framework, and the framework cooperatively applies four key technologies of Gaussian cutting and opacity regularization, dynamic spherical harmonic function adjustment, entropy constraint vector quantization and coordinate space shrinkage. Wherein in Gaussian clipping, redundant gauss are eliminated by quantifying the contribution degree of a Gaussian unit; the dynamic spherical harmonic function adjustment adaptively adjusts the order of color coding according to the scene complexity; the entropy constraint vector quantization is used for compressing a plurality of Gaussian attributes so as to realize more compact representation; and the coordinate space shrinkage is realized through nonlinear transformation, so that the rendering precision of details of the center of the scene is remarkably improved. According to the method, while the rendering precision and quality are kept, remarkable storage compression is realized, and the method is particularly suitable for deployment of resource-limited platforms such as mobile equipment.

The present invention provides a multilingual, AI-powered healthcare system integrating a personalized medical assistant, real-time clinical decision support, and closed-loop device control. The system ingests multimodal patient-generated and institutional data, applies advanced language-model-driven reasoning for clinical insights and triage, and supports regulated, auditable actuation of medical devices. Key features include multilingual overlays, role- and jurisdiction-specific visualization, co-signature enforcement, audit traceability, and seamless integration with healthcare infrastructure. A “Multilingual Overlay” is a dynamically generated display layer that the system composites in real-time from clinical text, icons, and color-coded indicators, automatically adapting its language, reading direction, terminology, visual density, and role-based data visibility to the preferences, locale, and device form-factor of each authenticated viewer. The architecture supports provider- and patient-facing use cases, agentic AI for autonomous yet regulated decision-making, and robust safety and compliance features.

The invention discloses a gait analysis method, system and equipment. The method comprises the following steps: acquiring a pressure value of each measuring point in a sole area in real time when a tested person stands or walks; generating a pressure distribution map and a change curve map based on the acquired pressure values; gait-related parameters are calculated and an electronic report is generated. In order to improve image visibility and analysis precision, a perceptual disturbance guided multi-scale color coding mechanism is adopted, and a high-resolution and vision enhanced pressure distribution diagram is generated through the steps of interpolation expansion, normalization, disturbance enhancement, HSV color wheel mapping and the like. The system also supports two analysis modes of balanced standing and dynamic walking, can output multi-dimensional gait indexes including pressure parameters, gait cycle parameters, pressure center tracks and the like, is matched with a built-in high-density sensor array and a man-machine interaction module of the system, is widely suitable for scenes of rehabilitation medical treatment, sports science, old-age health management and the like, and has wide application prospects. The method has the advantages of high detection precision, visual result, comprehensive analysis and the like.

The invention relates to the technical field of deep sleep management, in particular to a deep sleep staging management method and system based on electroencephalogram feedback. Comprising the steps that electroencephalogram feedback of a target user is collected, an actual deep sleep waveform is extracted and judged with an expected spectral domain of an ideal state transfer stage, and an unreached stage is locked; in an unreached stage, constructing a signal mode prediction model, and determining an optimal guide window stage by combining with intervention entry point migration analysis; performing wavelet basis decomposition to obtain an excellent wave band and a clutter band of the optimal guide window period; when the signal-to-noise ratio is high, crosstalk coupling analysis is executed, and white noise is generated by using the rhythm coding library for guide management; when the signal-to-noise ratio is low, light environment and temperature response regulation is planned, and deep sleep is managed in combination with a color coding chain and a signal excitation result. According to the method and the system, guide management of different decisions can be executed in the stage of entering the deep sleep of the target user, and the deep sleep quality, stability and continuity are improved.

The invention discloses a remote sensing image intelligent interpretation method and system based on multi-modal fusion, and relates to the technical field of image fusion interpretation, and the method comprises the steps: extracting multi-modal space and frequency domain features, splicing the features into a comprehensive feature vector, calculating a mutual information value between modals, generating a modal weight, and calculating a fusion feature vector by using a weighted sum method; constructing a support vector machine (SVM) model to calculate an SVM decision value, converting the SVM decision value into a probability by using a Platt scaling method, generating a classification result, mapping back to a multi-modal remote sensing image coordinate by using an affine transformation method, generating an interpretation mask, and generating a visualization result by using RGB color coding. According to the method, the ground feature distinguishing capability of the image is enhanced by combining space and frequency domain feature extraction, and the utilization efficiency of modal complementary information by fusion features is improved through mutual information weighting and ReLU enhancement.

The Contextual Behavioral Analysis and Response (CBAR) system is an advanced cybersecurity solution for real-time insider threat detection and mitigation. It features a multi-layered architecture that includes modules for data collection, integration, analysis, risk assessment, and response. The system aggregates data from diverse sources, such as network logs, user activities, and HR records, using multi-device technologies. A patented ETL process and APIs normalize this data into a unified dataset. The analysis module applies machine learning algorithms and forensic statement analysis to identify threats by analyzing behavioral patterns and communication anomalies. Real-time risk scores are assigned color-coded levels for easy interpretation, enabling customizable automated responses based on assessed threat levels. This comprehensive approach enhances organizational security by providing a nuanced method for detecting and mitigating insider threats.

The invention discloses a building structure crack intelligent detection method based on machine vision, and relates to the technical field of detection. The method comprises the steps that AR glasses are used for scanning the surface of a building, an RGB image is obtained and denoised, and then a three-dimensional space coordinate system is constructed through the SLAM technology; based on a lightweight convolutional neural network model, constructing a crack classification semantic model, inputting a de-noised image, and outputting a crack semantic tag and a center point coordinate; training an end side voice RNNT model, converting the voice instruction into a retrieval instruction, and determining a crack corresponding to the instruction through a voiceprint positioning technology; according to the crack center point coordinate and a crack corresponding to the instruction, utilizing an image processing algorithm to extract a crack geometric feature; and constructing a crack prediction model, inputting geometric features, outputting a crack propagation prediction result, and marking a high-risk region on an AR glasses interface by color coding. And building structure crack detection and risk assessment are realized through the crack semantic tag and the crack propagation prediction result.

The invention provides an intelligent construction safety monitoring system and method for a U-shaped stacked box rigid frame aqueduct, and the system is suitable for the U-shaped stacked box rigid frame aqueduct for hanging basket construction, and comprises a sensing layer which is used for collecting the stress state of a hanging basket; the environment monitoring unit is a micro weather station mounted on an upper cross beam of the hanging basket; the posture monitoring unit is used for monitoring the lateral and longitudinal inclination angles of the hanging basket in real time; the execution layer comprises a catenary anti-overturning device arranged behind the rear anchor point of the hanging basket, the catenary anti-overturning device comprises a hinged steel chain, and the hinged steel chain and the foundation anchoring end form a catenary structure; the control layer comprises an edge computing terminal and a threshold library in which early warning thresholds are stored, and the edge computing terminal is used for receiving the data of the sensing layer in real time, performing multi-source data fusion analysis and judging whether an early warning or emergency mechanism is triggered or not according to an analysis result; and the interaction layer comprises a visual platform, and the visual platform is used for carrying out fusion display on the monitoring data and the BIM model and presenting the risk level in a color coding mode.

The invention discloses a retail machine goods arrangement visualization method and system, and the method comprises the steps: building a goods channel topological relation matrix through analyzing the physical structure of a retail machine, and initializing a goods channel attribute data table; a visual page is generated based on the topological relation, the goods channel type and the inventory state are visually displayed through color coding and a dynamic progress bar, and dragging interaction operation is supported; performing multi-level verification on user operation through a progressive verification mechanism, including temperature compatibility, path compliance and topological connectivity detection, and avoiding physical constraint conflicts; after the verification is passed, updating inventory data in real time and dynamically reconstructing a cargo channel topological relation; analyzing features such as inventory turnover rate and goods channel visibility by using a pre-trained DQN network, and generating intelligent decisions such as goods channel merging and splitting and goods positioning; finally, dynamic updating of the interface is achieved through the differential rendering technology, and the response performance is optimized in combination with double buffering and local redrawing. The problems of fragmentation of management information, decision lag and the like of a traditional retail machine are solved.

Systems and methods are described for dynamically generating a graphical code that is placed within an advertisement, which may in in turn be placed dynamically over video or image content delivered to a client device. A portion of a stored advertisement unit may be created to have a color-coded section designated to be later replaced with a graphical code. When a user requests to view image or video content on or in which a given advertisement will be shown, a server may generate a tag that contains a URI that includes tracking parameters to identify what advertisement was scanned and / or other data associated with the advertisement, a publisher, or a client device. A graphical code may be generated to represent the given URI, then that graphical code may be inserted into a previously color-coded area of an ad creative before delivery to a client device for presentation.

An image frame loss detection method is performed by a computer device, including: acquiring first coded data respectively corresponding to a plurality of first image frames and a color signal corresponding to at least one second image frame; obtaining second coded data corresponding to at least one second image frame generated by a terminal device through image rendering of a color signal based on the coded data respectively corresponding to the plurality of first image frames; and comparing the first coded data respectively corresponding to the plurality of first image frames with the second coded data corresponding to the at least one second image frame to determine whether a frame loss occurs. The first coded data and the second coded data each include color-coded data respectively corresponding to M image blocks of a correspond image frame, and each of the M image blocks has a color in the image frame.

Provided in the present application are a color noise reduction method and apparatus, and an electronic device and a computer-readable storage medium. The method comprises: converting a source image in a YUV format into a target image in a target format, wherein the target format is a color encoding format that does not use luminance for encoding; calculating the pixel difference between adjacent pixel points in the target image to obtain a difference distribution; and on the basis of the difference distribution, performing noise reduction on the source image. An image in a target format is used to indirectly introduce information of a Y channel in a source image, thereby not only effectively distinguishing the similarity between neighboring pixels, but also performing color noise reduction on edge regions to the maximum extent, and also solving the problems in respect of color overflow and residual edge noise during color noise reduction.

A Real-Time Traffic Flow Indicator device for vehicles, comprising an advanced sensor assembly with Time-of-Flight cameras, LIDAR, and FMCW radar to capture surrounding data, including lane markings, vehicle positions, speeds, and distances. Edge-based lane detection algorithms employing CNNs detect lane boundaries, while real-time traffic analysis algorithms utilizing sensor fusion assess traffic conditions. Color-coded light indicators on rear and front displays represent specific traffic scenarios, and a V2V communication module enables bidirectional transmission of lane-specific traffic information. An Intelligent Data Processing Algorithm processes integrated sensor and V2V data to assess traffic conditions, communicating assessments and hazards through an LCD interface and FPGA-powered safety alert subsystem. The device also detects emergency vehicles using machine vision, activating LED indicators to alert the driver.

The invention relates to the technical field of defect detection, in particular to an outer wall ultrasonic automatic detection device, a detection method and an identification method. The detection device is used for detecting the surfacing layer of the hydrogenation reactor, comprises a wall-climbing moving vehicle, an electronic control system, a probe frame, an ultrasonic coupling system, a probe device, a manual remote controller, a notebook computer and a cable, is high in automation degree and has the characteristics of multi-probe integration and high coupling stability, and a coupling system is optimized. According to the detection method, a full-automatic process from positioning, starting and multi-modal detection to real-time imaging is realized, multi-dimensional and full-coverage scanning of a detection area is realized, and various defects are effectively detected. According to the automatic identification method, intelligent identification and rechecking of defects are achieved through multi-probe calibration, color coding, image fusion and confidence degree evaluation, calibration and identification innovation is carried out, defect classification and mode identification science can be carried out, and comprehensive judgment capacity and a rechecking mechanism are achieved.

The application provides a harmonic source modeling method based on time-frequency feature extraction, first determines harmonic principal component frequencies of a harmonic source; then extracts harmonic voltage amplitude and phase angle corresponding to the harmonic principal component frequencies of the harmonic source, obtains a frequency domain feature set of the harmonic source, reconstructs the frequency domain feature set through IDFT to form a harmonic source frequency domain feature component matrix, and splices the frequency domain feature component matrix with an original voltage sequence collected by a synchronous phasor measurement device to obtain a time-frequency feature component matrix; the component matrix is converted into a gray scale image by using a matrix normalization method, and is converted into a three-channel time-frequency feature component color image by using a pseudo-color coding technology; finally, the collected harmonic source voltage data and the time-frequency feature component color image are taken as inputs of the model, the collected corresponding harmonic source current data are taken as outputs of the model, the network model is continuously trained through the measured data until accurate mapping of a relationship between harmonic voltage and harmonic current of the harmonic source is realized, and a harmonic source data-driven model is obtained.

The invention provides a spatial positioning method for surveying and mapping, and relates to the technical field of laser radar positioning. The method comprises the following steps: projecting color grid light to a to-be-mapped area through color coding to form a positioning grid, acquiring two images for turning on and off the grid light by using image acquisition equipment, extracting grid intersection points as control points through color recognition, and obtaining pixel coordinates of the control points; measuring light is emitted to the control point through at least three measuring light projection points, and the three-dimensional coordinates of the control point are solved through spatial distance intersection; in combination with the pixel coordinates and the three-dimensional coordinates of the control points, reversely solving the pose of the image acquisition equipment through a heterovariance total least square model; the pixel offset of a target feature point and the pixel offset of an adjacent control point of the target feature point are recognized and converted into the actual distance offset in combination with the pose, and therefore the three-dimensional coordinates of the feature point are calculated. According to the invention, optical active projection and optical distance measurement are effectively fused, and high-efficiency three-dimensional space positioning is realized.

A storage container device is provided. The device is comprised of a storage container, designed for organization and versatility through features such as color-coded components, nesting capabilities, and enhanced usability. The device comprises a durable, lightweight body and matching lids made of resilient materials like HDPE or polypropylene, ensuring resistance to wear and deformation. Available in various shapes and sizes, the containers nest within one another to optimize storage, aided by interlocking fasteners on the lids that secure multiple units compactly. Each size is identified through a distinct color-coding system for easy lid-body pairing, enhancing usability. Additional features include vents for airflow, with options for adjustability and filtering, and molded handles for convenient transport. The invention further includes a method for selecting, nesting, and storing the devices, ensuring practicality in household or commercial applications.

The invention belongs to the technical field of road surface detection, and relates to a multi-dimensional radar map construction method based on electromagnetic signals, which comprises the following steps: S1, scanning a target area through high-frequency three-dimensional ground penetrating radar equipment, acquiring electromagnetic reflection signals, and preprocessing the acquired signals; s2, converting the time domain data of the preprocessed electromagnetic reflection signal into spatial domain data, and generating a three-dimensional data model; s3, blank data in the three-dimensional data model are complemented through an interpolation method, and high-resolution three-dimensional grid data are generated; s4, extracting a disease area based on a reflection intensity threshold value and performing type identification; and S5, constructing a three-dimensional disease visualization model, and mapping disease characteristic parameters by adopting color coding. According to the method provided by the invention, the depth resolution and the spatial positioning precision of disease detection are improved, and the real distribution and form of the diseases in the pavement can be reflected more comprehensively.

The invention relates to a method for verifying standards, norms, and specifications (R1 to Rn) in the design development of user interfaces in the automotive sector, wherein design drafts (UD) are compared with the specifications (R1 to Rn), wherein a Vision Language Model (VLM) is used to compare the design drafts (UD) with the specifications (R1 to Rn), wherein the Vision Language Model (VLM) is controlled via a system prompt (SP) which includes the instruction that input images (IB) are design drafts (UD) for digital content in the automotive sector, wherein the Vision Language Model (VLM) refers to various automotive-related guidelines or specifications (R1, R2, R3, Rn) for digital content, wherein the Vision Language Model (VLM) or an evaluation process (EP) uses design drafts (UD) as input images (IB) and evaluates them according to the specifications (R1 to Rn), wherein the evaluation results in an evaluated design (EUD).in which an overlay is placed over the design draft (UD), which uses color coding to show which areas and / or design elements comply with or do not comply with the specifications (R1 to Rn).

Systems and methods are provided for automatically generating prioritized, color-coded tasks based on user input received through a conversation interface of a task management application. User input provided to an automated software assistant is analyzed along with contextual data from multiple sources, including existing tasks, calendar events, emails, and messages. The system determines task attributes such as title, status, deadline, geographic location, urgency, and frequency, and assigns a color tag based on color assignment parameters including task category, priority, and location. Machine learning algorithms analyze historical user-assigned colors in relation to task attributes to predict color assignments for new tasks and establish intelligent color relationships, thereby dynamically updating a prioritized, color-coded task list displayed in a graphical user interface.

The application discloses a kind of medical mid-infrared 1720nm pulsed fiber lasers, it is related to laser medical technical field, including the following steps: acquisition pulsed fiber laser data, respectively calculate thulium ion concentration uniformity index, laser energy intensity and each small target tissue ablation area ablation index, and then construct the thermal map matrix of each small thulium-doped fiber section, and draw the anti-fake color coding diagram of each target tissue ablation area, the spectral analysis technique in the method, multiple linear regression algorithm and convolution neural network algorithm are closely combined with modern information technology, the degree of uniformity of thulium ion concentration distribution and target tissue ablation degree are achieved in real time and comprehensive monitoring, so that the monitored data becomes more accurate index under the same conditions, significantly enhance the intelligent degree of medical mid-infrared 1720n pulsed fiber laser in use process.

The present invention discloses a magnetic imaging recognition method and a magnetometer based on deep learning. The method includes: preprocessing the collected magnetic field data to obtain encrypted and reconstructed magnetic map data; calculating magnetic gradient tensor components based on the encrypted and reconstructed magnetic map data, and extracting magnetic field gradient features by using different combination methods of the magnetic gradient tensor components; identifying magnetic field anomaly information by analyzing the magnetic field gradient features, combining the magnetic source boundary with the encrypted and reconstructed magnetic map data, performing pseudo-color coding, and establishing a magnetic image data set; training an improved R-CNN pipeline defect detection model by using the magnetic image data set, where the input of the pipeline defect detection model is a magnetic image and the output is the position and type of pipeline defects; and using the trained pipeline defect detection model to identify pipeline defects in newly input magnetic images. The present invention improves the accuracy, efficiency and adaptability of pipeline defect detection through advanced data processing technologies and artificial intelligence algorithms.

The application discloses a kind of based on three-dimensional visualization dynamic segmentation double integral teaching demonstration software algorithm, belong to computer-aided instruction technical field.The system includes mathematical modeling module, grid segmentation module, geometry construction module, rendering engine module, calculation analysis module, error visualization module, animation generation module and user interaction module.System is through gradual regional division strategy, the long rectangular cuboid column unit of the rectangular domain on the dynamic segmentation of the curved top cylinder corresponding to the integral function, using transparent curved surface rendering and entity cylinder color coding technology, intuitively show the riemann and approximation process of double integral.System supports multi-angle view angle animation synthesis, real-time calculation and display approximate volume, accurate integral value and error index, solve the technical problem that double integral concept is abstract and difficult to understand in traditional teaching, lack intuitive cognitive tool, significantly improve the visualization effect of mathematical teaching and student's learning efficiency.

The invention provides a point cloud pickup method and electronic equipment, which can encode an index value of each point in point cloud data into a unique color value, and render the unique color values corresponding to different points to a graphic buffer area of a non-visual area in an off-screen manner to obtain a color data set; and when a screen click operation of a user is detected, determining a target color value corresponding to the image pixel coordinates according to the color data set, and determining a pickup result by reversely decoding the target color value. The index value of each point cloud is converted into a unique color code, and each point is independently coded, so that the pickup precision is improved. The unique color value is rendered to the graph buffer area of the non-visual area in an off-screen mode, the rendering and picking process is separated from a main thread, calculation is not dependent on the main thread, and the picking efficiency is improved. After a user clicks a screen, a pickup result is determined by taking a target color value inverse solution as an index, and a unique index is obtained through color inverse solution, so that false pickup is avoided, and a quick, accurate and interference-free target pickup function is realized.

The invention discloses a POCT (Point of Care Testing) microfluidic detection chip carrying a photonic crystal structure color coding microcarrier and a preparation method of the POCT microfluidic detection chip. The POCT chip comprises a high-definition and high-transparency cover glass used for packaging and visual detection and a chip main body used for placing the photonic crystal structure color coding microcarrier. And the liquid inlets on the chip main body are uniformly dispersed into the reaction chamber of the chip through the multi-stage flow channel. The reaction chamber comprises a plurality of limiting columns which can separate different microcarriers, so that each microcarrier can be in uniform contact with reaction liquid. After the microcarrier is placed on the chip main body in a random mode, the chip main body is packaged by cover glass and biological glue. Due to the fact that the whole chip is subjected to hydrophilic treatment, when an extremely small amount of to-be-detected sample is dropped into the liquid inlet, reaction liquid can rapidly infiltrate the interior of the whole chip and react with the coding microcarrier in the chip. And after incubation for half an hour, washing the residual sample with a buffer solution, and finally reading the result. According to the invention, trace unknown samples can be detected in a short time, and development of on-site rapid detection can be promoted.

The invention discloses a method and a system for detecting the interference of an electrified body on a power terminal field signal, which overcome the problem that the interference degree of the electrified body on the power terminal field signal cannot be accurately detected in the prior art, and the method comprises the following steps: obtaining the geographical location information of a to-be-detected area, establishing an electronic map and marking the electrified body; testing points are arranged around the electrified body, position information of the testing points is recorded, and signal parameters of all operators are collected at the testing points; establishing a signal interference discrimination model based on a machine learning algorithm in combination with environmental parameters of the test points; and obtaining the interference degree of each test point and each operator signal by an electrified body, and displaying a signal interference thermodynamic diagram on a map in a color coding manner. The signal intensity of the public network near the electrified body can be accurately measured, the signal interference degree of different operators can be judged, the interference condition of the electrified body on the field signal of the power terminal near the electrified body can be obtained, and the reason for weakening of the field signal of the power terminal can be accurately judged.

This invention discloses a method, device, fusion map, and storage medium for generating a safety risk emergency evacuation fusion map. The method includes acquiring multiple safety risk factors contained in a target floor, a functional zoning plan of the target floor, and multiple emergency evacuation factors contained in the target floor; assessing the risk level of each safety risk factor to obtain its actual risk level; determining the target color for each safety risk factor based on its actual risk level and the correspondence between risk level and color coding; and drawing the target colors and emergency evacuation factors on the functional zoning plan based on a first correspondence, a second correspondence, and preset drawing rules to obtain the safety risk emergency evacuation fusion map. This application significantly improves emergency response efficiency, achieving an upgrade from scattered data to integrated, visualized, and intelligent management.