Portable gas inspection and concentration distribution visualization method, system and equipment

Abstract

The invention relates to a portable gas inspection and concentration distribution visualization method, system and equipment, and the method comprises the steps: carrying out pumping type sampling in a process that a portable gas detector moves in a to-be-detected area, and obtaining gas concentration data sets of different space sampling points in real time; synchronously recording real-time position information corresponding to each sampling point through a positioning unit integrated in the gas detector to form a spatio-temporal data set associated with the gas concentration data set; constructing a three-dimensional concentration field model reflecting continuous spatial distribution of gas concentration in the to-be-detected area by adopting a spatial interpolation algorithm; and performing fusion rendering on the three-dimensional concentration field model and the two-dimensional map or the three-dimensional real scene model of the to-be-detected area to generate a gas concentration distribution visual map, and visually displaying the concentration level and the diffusion trend of each point in the space in a gradient color manner. Therefore, the purposes of visual and accurate analysis and positioning of the gas space distribution, the diffusion trend and the risk area are achieved.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring and industrial safety technology, and in particular to a portable gas inspection and concentration distribution visualization method, system and equipment. Background Technology

[0002] In fields such as industrial safety, environmental monitoring, and emergency response, effective perception and assessment of gas concentration distribution in specific areas are crucial. Traditional gas detection mainly relies on fixed sensor networks or handheld single-point detectors. Fixed networks are costly to deploy, lack flexibility, and struggle to achieve seamless coverage over large areas; while manual inspections typically only acquire instantaneous concentration data at discrete points, failing to form a holistic understanding of spatial distribution, especially in complex or concealed spaces, making it difficult to trace leak sources and assess diffusion trends. While some existing technologies attempt to combine detection data with simple maps, they generally lack the ability to deeply integrate and intelligently analyze spatiotemporal data generated by mobile inspections. They struggle to reconstruct a realistic, continuous three-dimensional concentration field from discrete point data, and even more so to achieve deep fusion and interactive analysis of concentration distribution with highly realistic geographical scenes, resulting in delayed risk identification and insufficient decision support. Summary of the Invention

[0003] The main objective of this invention is to provide a portable gas inspection and concentration distribution visualization method, system, and device. Through portable dynamic inspection, discrete gas concentration sampling data is reconstructed into a continuous, realistic, and deeply integrated three-dimensional concentration distribution visualization map, so as to achieve intuitive and accurate analysis and location of gas spatial distribution, diffusion trend, and risk areas.

[0004] To achieve the above objectives, the present invention provides a portable gas inspection and concentration distribution visualization method, comprising the following steps: As the portable gas detector moves within the detection area, it continuously or at preset intervals performs pump-type sampling and acquires real-time data sets of gas concentrations at different spatial sampling points. Simultaneously, the positioning unit integrated in the gas detector synchronously records the real-time location information corresponding to each sampling point, forming a spatiotemporal dataset associated with the gas concentration dataset; Based on the spatiotemporal dataset, a three-dimensional concentration field model reflecting the continuous spatial distribution of gas concentration in the region to be detected is constructed using a spatial interpolation algorithm. The three-dimensional concentration field model is fused and rendered with the two-dimensional map or three-dimensional real-scene model of the area to be detected to generate a visual map of gas concentration distribution, which intuitively displays the concentration level and diffusion trend of each point in space with gradient colors.

[0005] Furthermore, the steps of the portable gas detector continuously or at preset intervals performing pump-type sampling and acquiring gas concentration datasets from different spatial sampling points in real time while moving within the detection area include: The detector uses a built-in gas pump to perform pump-suction sampling at a constant flow rate and employs at least one of an electrochemical sensor, an infrared sensor, or a photoionization sensor to detect gas concentration. During each sampling, the sensor's temperature compensation data and ambient temperature and humidity data are recorded simultaneously. The original concentration signal is then compensated and filtered in real time to obtain a calibrated gas concentration dataset.

[0006] Furthermore, the step of the portable gas detector continuously or at preset intervals performing pump-type sampling and acquiring gas concentration datasets from different spatial sampling points in real time while moving within the detection area also includes: The movement process involves an inspection personnel holding the detector and walking along a preset route, or a mobile robot carrying the detector and moving along a preset trajectory. During its movement, the detector automatically triggers sampling at preset equal time intervals or equal distance intervals, and automatically increases the sampling frequency when a sudden change in concentration is detected, forming an adaptive sampling gas concentration dataset.

[0007] Furthermore, the step of simultaneously recording the real-time location information corresponding to each sampling point through the positioning unit integrated in the gas detector includes: Location information is obtained by a multi-source fusion positioning unit integrated in the gas detector. The multi-source fusion positioning unit includes a GPS module, an inertial measurement unit, and a geomagnetic sensor. The multi-source fusion positioning unit is configured to prioritize the use of GPS signals in open outdoor areas, and switch to a trajectory estimation method based on inertial measurement units and geomagnetic sensors in indoor or signal-obstructed areas to obtain continuous position information. A particle filter algorithm is used to fuse and correct position information from different sensors, and output the real-time position information of each sampling point for recording.

[0008] Furthermore, the steps for forming a spatiotemporal dataset associated with the gas concentration dataset include: Generate a data record for each sampling point. Each data record includes at least a timestamp, latitude and longitude coordinates, gas concentration value, and sensor type identifier. The data records generated in chronological order are associated and organized to form the spatiotemporal dataset; The spatiotemporal dataset is compressed and encrypted in real time, and cached locally using both time and spatial indexes.

[0009] Further, based on the spatiotemporal dataset, the step of constructing a three-dimensional concentration field model reflecting the continuous spatial distribution of gas concentration within the detection area using a spatial interpolation algorithm includes: Spatial variogram analysis was performed on the spatiotemporal dataset to determine the spatial autocorrelation range and directional heterogeneity of gas concentration; Based on the aforementioned spatial autocorrelation range and directional heterogeneity, the search radius and weight parameters of the spatial interpolation algorithm are dynamically adjusted. The adjusted parameters are used to perform a spatial interpolation algorithm to generate the three-dimensional concentration field model.

[0010] Further, the step of fusing and rendering the three-dimensional concentration field model with the two-dimensional map or three-dimensional real-world model of the area to be detected to generate a visual gas concentration distribution map, which intuitively displays the concentration level and diffusion trend of each point in space using gradient colors, includes: The concentration values ​​of each spatial point in the three-dimensional concentration field model are mapped to preset gradient colors, and a semi-transparent three-dimensional concentration cloud map is generated based on the transparency setting. The three-dimensional concentration cloud map is aligned with the imported two-dimensional geographic information system map or oblique photogrammetry three-dimensional real scene model by coordinate alignment and layer overlay to generate a fused gas concentration distribution visualization map. The visualization map supports interactive operations such as zooming, rotating, and cross-sectional cutting, and automatically marks areas exceeding the standard according to preset concentration thresholds.

[0011] This invention also provides a portable gas inspection and concentration distribution visualization method and system, comprising: The concentration sampling module is used for the portable gas detector to perform continuous or preset interval pump-type sampling while moving within the detection area, and to acquire gas concentration datasets at different spatial sampling points in real time. The location association module is used to simultaneously record the real-time location information corresponding to each sampling point through the positioning unit integrated in the gas detector, forming a spatiotemporal dataset associated with the gas concentration dataset; The model building module is used to construct a three-dimensional concentration field model reflecting the continuous spatial distribution of gas concentration in the region to be detected based on the spatiotemporal dataset and using a spatial interpolation algorithm. The map generation module is used to fuse and render the three-dimensional concentration field model with the two-dimensional map or three-dimensional real scene model of the area to be detected, and generate a visual map of gas concentration distribution, which intuitively displays the concentration level and diffusion trend of each point in space with gradient colors.

[0012] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described portable gas inspection and concentration distribution visualization method.

[0013] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described portable gas inspection and concentration distribution visualization method.

[0014] The portable gas inspection and concentration distribution visualization method, system, and equipment provided by this invention have the following beneficial effects: This invention fundamentally solves the problem of traditional point-based detection's inability to acquire continuous spatial data through continuous sampling and precise positioning during inspection movement, generating a high spatiotemporal resolution concentration-location correlation dataset. Employing an environment-adaptive multi-source fusion positioning and intelligent sampling strategy ensures the continuity of positioning and the efficiency of data acquisition in complex indoor and outdoor environments, especially enabling the sensitive capture of areas with abrupt concentration changes. Furthermore, by analyzing the spatial variation characteristics of the data itself to dynamically optimize interpolation parameters, the constructed three-dimensional concentration field model better conforms to physical diffusion laws, significantly improving the accuracy and reliability of spatial distribution prediction. In addition, the concentration field is deeply integrated with two-dimensional maps or three-dimensional real-world models for interactive visualization, realizing a three-dimensional and intuitive presentation of gas risks in real geographical scenarios. This supports rapid source location, trend analysis, and precise demarcation of areas exceeding standards, greatly improving the analytical depth, decision-making efficiency, and situational awareness capabilities of inspection work. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a portable gas inspection and concentration distribution visualization method according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a portable gas inspection and concentration distribution visualization method and system according to an embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0016] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] Reference Figure 1The diagram below illustrates the process of a portable gas inspection and concentration distribution visualization method proposed in this invention, which includes the following steps: S11, the portable gas detector continuously or at preset intervals performs pump-type sampling while moving within the area to be detected, and acquires gas concentration datasets at different spatial sampling points in real time. S12, simultaneously, through the positioning unit integrated in the gas detector, the real-time location information corresponding to each sampling point is recorded synchronously to form a spatiotemporal dataset associated with the gas concentration dataset; S21. Based on the spatiotemporal dataset, a three-dimensional concentration field model reflecting the continuous spatial distribution of gas concentration in the area to be detected is constructed using a spatial interpolation algorithm. S31, the three-dimensional concentration field model is fused and rendered with the two-dimensional map or three-dimensional real scene model of the area to be detected to generate a gas concentration distribution visualization map, which intuitively displays the concentration level and diffusion trend of each point in space with gradient colors.

[0019] In one embodiment, step S11 includes: The steps of a portable gas detector continuously or at preset intervals perform pump-type sampling while moving within the detection area, and acquire real-time gas concentration datasets from different spatial sampling points include: The detector uses a built-in gas pump to perform pump-suction sampling at a constant flow rate and employs at least one of an electrochemical sensor, an infrared sensor, or a photoionization sensor to detect gas concentration. During each sampling, the sensor's temperature compensation data and ambient temperature and humidity data are recorded simultaneously. The original concentration signal is then compensated and filtered in real time to obtain a calibrated gas concentration dataset.

[0020] In practical implementation, portable gas detectors employ a combination of active pump sampling and multi-sensor fusion detection during mobile inspections to acquire gas concentration datasets. Specifically, the detector incorporates a miniature diaphragm pump or piston pump to continuously extract gas samples from the target environment at a constant flow rate (e.g., 100-500 mL / min), overcoming the shortcomings of traditional diffusion-based detection methods, such as slow response and significant susceptibility to ambient airflow. This makes it particularly suitable for complex industrial scenarios with turbulent airflow or concentration gradients. Gas samples are uniformly delivered to the sensor compartment via a gas path system. Depending on the type and characteristics of the gas to be detected, one or more combinations of electrochemical sensors, non-dispersive infrared (NDIR) sensors, or photoionization (PID) sensors can be used for detection. For example, electrochemical sensors are suitable for detecting electrochemically active gases such as O2, CO, and H2S; infrared sensors, based on the absorption characteristics of gas molecules in specific infrared bands, are suitable for detecting greenhouse gases or hydrocarbons such as CO2 and CH4; and PID sensors have extremely high sensitivity to volatile organic compounds (VOCs). During each sampling cycle, the system synchronously collects the temperature drift compensation data of the sensor itself and the readings of the ambient temperature and humidity sensors. It uses the built-in microprocessor to run a real-time compensation algorithm to perform nonlinear correction of temperature and humidity on the original concentration signal, and combines digital filtering technology (such as moving average filtering or Kalman filtering) to suppress noise interference. Finally, it outputs a calibrated and purified gas concentration dataset, thereby improving the accuracy of the detection data and environmental adaptability.

[0021] In one embodiment, the step of continuously or at preset intervals performing pump-type sampling and acquiring gas concentration datasets from different spatial sampling points in real time while the portable gas detector moves within the detection area further includes: The movement process involves an inspection personnel holding the detector and walking along a preset route, or a mobile robot carrying the detector and moving along a preset trajectory. During its movement, the detector automatically triggers sampling at preset equal time intervals or equal distance intervals, and automatically increases the sampling frequency when a sudden change in concentration is detected, forming an adaptive sampling gas concentration dataset.

[0022] In practical implementation, to adapt to diverse inspection scenarios and needs, the portable gas detector's movement and sampling strategy is set to an intelligent mode that combines pre-defined standardization with environmental adaptability. The movement process mainly includes two methods: one is for inspection personnel to hold the device and walk within the inspection area according to a pre-planned or experience-determined route; the other is for a mobile robot with autonomous navigation capabilities (such as a wheeled, tracked, or drone platform) to carry the detector and move along a pre-programmed precise trajectory. This fully leverages the flexibility and on-the-spot judgment of personnel in complex, unstructured environments, making it suitable for emergency response, temporary inspections, or areas difficult for robots to access. It is also suitable for regular, large-area, or potentially hazardous area inspections, enabling accurate path reproduction and long-term automated operation, improving inspection efficiency and consistency.

[0023] In terms of sampling strategy, the system supports automatically triggered sampling modes based on equal time intervals (e.g., once per second) or equal distance intervals (e.g., once every 0.5 meters). These two preset modes ensure a uniformly distributed base sampling point in the spatiotemporal dimension, providing a structured data foundation for building a continuous and smooth concentration distribution model. The system also has a built-in intelligent sensing and response mechanism: when the gas concentration value detected in real time undergoes a significant change in a short period of time (e.g., the rate of change exceeds a preset threshold), the system will immediately trigger adaptive control logic to temporarily increase the sampling frequency (e.g., switch to a high-speed sampling mode of 10 times per second). Through the adaptive sampling strategy of "steady-state sampling according to preset values, and accelerated sampling for sudden changes," data redundancy is avoided in areas with flat concentrations, saving equipment storage and energy consumption; in areas with large concentration gradients or potential leakage sources, high-density sampling accurately captures the detailed features and boundaries of concentration changes. This forms a set of "adaptive sampling gas concentration datasets" with non-uniform spatiotemporal resolution but higher information efficiency. The datasets fully cover the inspection area, and data enhancement has been performed on key areas, which can be used as data input for subsequent analysis of gas diffusion sources and identification of high-risk areas.

[0024] In one embodiment, step S12 includes: Simultaneously, by using the positioning unit integrated in the gas detector to synchronously record the real-time location information corresponding to each sampling point, the steps include: Location information is obtained by a multi-source fusion positioning unit integrated in the gas detector. The multi-source fusion positioning unit includes a GPS module, an inertial measurement unit, and a geomagnetic sensor. The multi-source fusion positioning unit is configured to prioritize the use of GPS signals in open outdoor areas, and switch to a trajectory estimation method based on inertial measurement units and geomagnetic sensors in indoor or signal-obstructed areas to obtain continuous position information. A particle filter algorithm is used to fuse and correct position information from different sensors, and output the real-time position information of each sampling point for recording.

[0025] In practice, this step simultaneously records the real-time location information of each sampling point, forming a spatiotemporal dataset. This dataset is then implemented through a highly integrated and intelligently adaptive multi-source fusion positioning system, aiming to solve the problem of insufficient reliability of traditional single positioning technologies in complex inspection environments. The multi-source fusion positioning unit integrated into the gas detector integrates a GPS module, an inertial measurement unit (IMU, including a three-axis accelerometer and a three-axis gyroscope), and a three-axis geomagnetic sensor. The multi-source fusion positioning system is based on an intelligent switching and fusion architecture driven by environmental perception. In open outdoor areas, the system prioritizes using GPS signals to obtain absolute geographical location information (latitude, longitude, and altitude). In this case, IMU and geomagnetic sensor data are mainly used for auxiliary verification and motion state perception. However, when the detector enters indoor environments, underground pipelines, dense buildings, or other environments where GPS signals are severely attenuated or blocked, the system seamlessly switches to a trajectory estimation mode that primarily uses inertial navigation and secondarily uses geomagnetic positioning. In this mode, the system uses the acceleration and angular velocity measured in real time by the IMU to calculate the relative displacement and attitude change through integration. At the same time, the heading angle information provided by the geomagnetic sensor is used to correct the directional drift caused by the integration of the gyroscope, thus forming an autonomous positioning system that is relatively reliable in the short term but has no absolute reference.

[0026] However, inertial navigation inherently suffers from cumulative errors, and geomagnetic information is susceptible to interference from local ferromagnetic materials. To address this, this embodiment introduces an intelligent fusion algorithm based on particle filtering. This algorithm treats GPS absolute position information, IMU-calculated relative motion trajectory, and geomagnetic heading information as observation data with uncertainties, and maintains a set of probability particles representing the possible position distribution of the detector. At each sampling moment, the algorithm dynamically updates the weight of each particle based on the latest sensor observation data, and approximates the detector's true posterior probability distribution through a resampling process, outputting an optimally estimated real-time position. This process effectively suppresses the cumulative errors of inertial navigation, especially when GPS signals are briefly lost and then recovered, quickly "pulling" the positioning result back to the correct trajectory; furthermore, it is robust to transient noise or abnormal interference from various sensors. The multi-source fusion positioning system can output continuous, stable, and accurate real-time location information that meets modeling requirements in complex and alternating indoor and outdoor inspection paths without relying on additional external infrastructure (such as indoor Bluetooth beacons or UWB base stations), ensuring that each gas concentration data point can be accurately associated with a reliable spatial coordinate.

[0027] In one embodiment, the step of forming a spatiotemporal dataset associated with a gas concentration dataset includes: Generate a data record for each sampling point. Each data record includes at least a timestamp, latitude and longitude coordinates, gas concentration value, and sensor type identifier. The data records generated in chronological order are associated and organized to form the spatiotemporal dataset; The spatiotemporal dataset is compressed and encrypted in real time, and cached locally using both time and spatial indexes.

[0028] In practice, a standardized data record is generated for each valid sample. Each record is designed to contain a set of minimum necessary and sufficient information tuples, specifically including at least: a high-precision timestamp (usually synchronized to UTC time with millisecond-level accuracy, used to establish a strict time series), latitude and longitude coordinates (spatial location provided by the aforementioned multi-source fusion positioning unit, which may include elevation information), gas concentration value (the final concentration reading after calibration and filtering), and sensor type identifier (indicating whether the concentration value originates from an electrochemical, infrared, or PID sensor, etc.). The system sequentially associates and organizes these discrete data records according to the time sequence of sampling, constructing a data set with a clear internal logic. Each record has a unique and ordered "anchor point" in the spatiotemporal continuum due to its timestamp and location coordinates, which can be used to support timeline playback and spatial trajectory reconstruction of the inspection process. This is the fundamental manifestation of the spatiotemporal attributes of the data, and the resulting set is defined as a spatiotemporal dataset. Considering the practical constraints often faced by portable devices, such as limited storage space, high data security requirements, and high demands for subsequent query efficiency, this invention further implements optimization processing immediately after data generation: real-time compression (using fast lossless compression algorithms such as LZ4) reduces data volume while ensuring data integrity, lowering storage pressure and wireless transmission bandwidth consumption, thereby extending the device's continuous working time in the field; real-time encryption (using lightweight encryption algorithms such as AES-128) provides security for sensitive concentration and location data, preventing unauthorized access or tampering during storage or transmission, meeting the confidentiality requirements of industrial testing data; the system establishes dual temporal and spatial indexes for the compressed and encrypted spatiotemporal dataset. The temporal index allows for rapid data extraction by time period (such as a specific inspection task); while the spatial index (such as an index structure based on R-trees or geohash) supports efficient retrieval of all sampling points within a specific geographical area. Through an indexed local caching mechanism, the performance of the device itself when quickly reviewing historical data, visually previewing, and selectively incrementally synchronizing with the cloud or server is greatly optimized.

[0029] In one embodiment, step S13 includes: Based on the spatiotemporal dataset, the step of constructing a three-dimensional concentration field model reflecting the continuous spatial distribution of gas concentration within the detection area using a spatial interpolation algorithm includes: Spatial variogram analysis was performed on the spatiotemporal dataset to determine the spatial autocorrelation range and directional heterogeneity of gas concentration; Based on the aforementioned spatial autocorrelation range and directional heterogeneity, the search radius and weight parameters of the spatial interpolation algorithm are dynamically adjusted. The adjusted parameters are used to perform a spatial interpolation algorithm to generate the three-dimensional concentration field model.

[0030] In specific implementation, step S13 transforms the discrete, non-uniformly distributed spatiotemporal sampling data into a continuous and complete three-dimensional concentration distribution representation. The key lies in utilizing an adaptive spatial interpolation method based on data-driven parameter optimization: spatial variogram analysis is performed on the spatiotemporal dataset to quantify the structural and random variations in gas concentration in space. By calculating the concentration semivariogram values ​​at different distances and directions, variogram curves are plotted to determine the spatial autocorrelation range and directional heterogeneity of gas concentration. Here, "spatial autocorrelation range" refers to the maximum distance at which concentration values ​​have a significant spatial correlation; beyond this range, concentration changes tend to be random. "Directional heterogeneity" reflects that the rate of diffusion or decay of concentration may differ in different spatial directions, for example, due to the influence of prevailing wind direction or topographical channels. Based on the structural parameters obtained from the above analysis, the key operating parameters of the subsequent spatial interpolation algorithm are dynamically adjusted. Specifically, the system dynamically sets the search radius of the interpolation algorithm based on the "spatial autocorrelation range" to ensure that the sample points used to estimate the concentration of unknown points all come from reasonable relevant regions, avoiding the introduction of irrelevant noise or the omission of key information. Simultaneously, the weight parameters of the interpolation algorithm are dynamically adjusted based on "directional heterogeneity." For example, in Kriging interpolation, different weights are assigned to sample points in different directions, enabling the interpolation model to accurately reflect the anisotropic diffusion characteristics of concentration. This ensures the interpolation model closely matches the actual spatial structure of gas distribution in the current detection area, improving model fidelity and physical plausibility. The system uses the adjusted parameters to execute spatial interpolation algorithms (such as Kriging interpolation or radial basis function interpolation), using all known sampling points in the spatiotemporal dataset as constraints, to estimate concentration on the three-dimensional spatial grid nodes of the entire detection area, ultimately generating a three-dimensional concentration field model. This three-dimensional concentration field model is a continuous, digital scalar field that accurately reflects the gradient changes in concentration in the horizontal and vertical directions, providing an analytical basis for understanding gas diffusion dynamics, identifying potential release sources, and assessing spatial pollution patterns.

[0031] In one embodiment, step S14 includes: The steps of fusing and rendering the three-dimensional concentration field model with the two-dimensional map or three-dimensional real-world model of the area to be detected to generate a visual gas concentration distribution map, and intuitively displaying the concentration level and diffusion trend of each point in space with gradient colors, include: The concentration values ​​of each spatial point in the three-dimensional concentration field model are mapped to preset gradient colors, and a semi-transparent three-dimensional concentration cloud map is generated based on the transparency setting. The three-dimensional concentration cloud map is aligned with the imported two-dimensional geographic information system map or oblique photogrammetry three-dimensional real scene model by coordinate alignment and layer overlay to generate a fused gas concentration distribution visualization map. The visualization map supports interactive operations such as zooming, rotating, and cross-sectional cutting, and automatically marks areas exceeding the standard according to preset concentration thresholds.

[0032] In practical implementation, the three-dimensional concentration field model (a digital matrix containing the concentration values ​​of each three-dimensional grid node) is converted into visual information through a color mapping function. The system maps the concentration value of each spatial point to a corresponding color based on a preset, industry-recognized gradient color scheme (e.g., from blue / green representing low concentration and safety, transitioning to yellow / red representing high concentration and danger). Simultaneously, concentration-based transparency control is introduced, making low-concentration areas more transparent and high-concentration areas more translucent or opaque, thus generating a translucent three-dimensional concentration cloud map that reflects both concentration levels and spatial hierarchy, revealing the spatial volume distribution characteristics of the concentration through visualization. To achieve accurate correlation between concentration information and the real world, the concentration cloud map needs to be fused with a two-dimensional map (such as a GIS map) describing the geographical background of the area to be detected, or a highly realistic three-dimensional real-world model (such as a model constructed through oblique photogrammetry). The key to this process is coordinate alignment, i.e., aligning the spatial coordinates of each point in the concentration cloud map with the coordinate system of the geographic base map or real-world model based on a unified geodetic coordinate system (such as WGS-84). By employing layer overlay rendering technology, a semi-transparent concentration cloud map is superimposed as an independent layer on top of the base map, generating a fused visual map of gas concentration distribution. This allows users to clearly see the gas concentration level at a specific location (such as next to a building or above a pipe), achieving seamless integration of data and scene. To make this map a powerful analytical tool rather than just a static image, interactive capabilities are also included. Users can zoom and rotate the map to observe the concentration distribution from any angle and scale. The profile cutting function allows users to virtually "cut" the three-dimensional concentration field, generating two-dimensional concentration contour maps on any profile (such as vertical or horizontal sections) for analyzing vertical diffusion or pollution at specific heights. Furthermore, the system can automatically identify and mark areas exceeding standards in the three-dimensional scene based on preset safety thresholds for various gas concentrations. This can be highlighted using flashing boundaries, specific icons, or text, and can directly display statistical information such as the exceeding values ​​and area volume. Through a series of functions, the raw detection data is transformed into a dynamic, detectable, and intuitive visualization system that can directly support location-based risk assessment and decision-making, thereby improving the analytical efficiency and situational awareness of inspection work.

[0033] Reference Figure 2 Here is a structural block diagram of a portable gas inspection and concentration distribution visualization method and system according to an embodiment of the present invention, including: The concentration sampling module is used for the portable gas detector to perform continuous or preset interval pump-type sampling while moving within the detection area, and to acquire gas concentration datasets at different spatial sampling points in real time. The location association module is used to simultaneously record the real-time location information corresponding to each sampling point through the positioning unit integrated in the gas detector, forming a spatiotemporal dataset associated with the gas concentration dataset; The model building module is used to construct a three-dimensional concentration field model reflecting the continuous spatial distribution of gas concentration in the region to be detected based on the spatiotemporal dataset and using a spatial interpolation algorithm. The map generation module is used to fuse and render the three-dimensional concentration field model with the two-dimensional map or three-dimensional real scene model of the area to be detected, and generate a visual map of gas concentration distribution, which intuitively displays the concentration level and diffusion trend of each point in space with gradient colors.

[0034] For the specific implementation of each module in the above device example, please refer to the above method embodiments, which will not be repeated here.

[0035] Reference Figure 3 This invention also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0036] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0037] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0038] In summary, this invention utilizes a portable gas detector to continuously or at preset intervals perform pump-like sampling while moving within the detection area, acquiring real-time gas concentration datasets from different spatial sampling points. Simultaneously, a positioning unit integrated into the gas detector synchronously records the real-time location information of each sampling point, forming a spatiotemporal dataset associated with the gas concentration dataset. Based on this spatiotemporal dataset, a spatial interpolation algorithm is used to construct a three-dimensional concentration field model reflecting the continuous spatial distribution of gas concentration within the detection area. This three-dimensional concentration field model is then fused and rendered with a two-dimensional map or three-dimensional real-world model of the detection area to generate a visual gas concentration distribution map. Gradient colors visually display the concentration levels and diffusion trends at various points in space, achieving the goal of intuitive and accurate analysis and location of gas spatial distribution, diffusion trends, and risk areas.

[0039] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0040] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0041] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A portable method for gas inspection and concentration distribution visualization, characterized in that, Includes the following steps: As the portable gas detector moves within the detection area, it continuously or at preset intervals performs pump-type sampling and acquires real-time data sets of gas concentrations at different spatial sampling points. Simultaneously, the positioning unit integrated in the gas detector synchronously records the real-time location information corresponding to each sampling point, forming a spatiotemporal dataset associated with the gas concentration dataset; Based on the spatiotemporal dataset, a three-dimensional concentration field model reflecting the continuous spatial distribution of gas concentration in the region to be detected is constructed using a spatial interpolation algorithm. The three-dimensional concentration field model is fused and rendered with the two-dimensional map or three-dimensional real-scene model of the area to be detected to generate a visual map of gas concentration distribution, which intuitively displays the concentration level and diffusion trend of each point in space with gradient colors.

2. The portable gas inspection and concentration distribution visualization method according to claim 1, characterized in that, The portable gas detector, while moving within the detection area, continuously or at preset intervals performs pump-type sampling and acquires real-time gas concentration datasets from different spatial sampling points, including the following steps: The detector uses a built-in gas pump to perform pump-suction sampling at a constant flow rate and employs at least one of an electrochemical sensor, an infrared sensor, or a photoionization sensor to detect gas concentration. During each sampling, the sensor's temperature compensation data and ambient temperature and humidity data are recorded simultaneously. The original concentration signal is then compensated and filtered in real time to obtain a calibrated gas concentration dataset.

3. The portable gas inspection and concentration distribution visualization method according to claim 1, characterized in that, The step of the portable gas detector continuously or at preset intervals performing pump-type sampling and acquiring gas concentration datasets from different spatial sampling points in real time while moving within the detection area also includes: The movement process involves an inspection personnel holding the detector and walking along a preset route, or a mobile robot carrying the detector and moving along a preset trajectory. During its movement, the detector automatically triggers sampling at preset equal time intervals or equal distance intervals, and automatically increases the sampling frequency when a sudden change in concentration is detected, forming an adaptive sampling gas concentration dataset.

4. The portable gas inspection and concentration distribution visualization method according to claim 1, characterized in that, The step of simultaneously recording the real-time location information corresponding to each sampling point through the positioning unit integrated in the gas detector includes: Location information is obtained by a multi-source fusion positioning unit integrated in the gas detector. The multi-source fusion positioning unit includes a GPS module, an inertial measurement unit, and a geomagnetic sensor. The multi-source fusion positioning unit is configured to prioritize the use of GPS signals in open outdoor areas, and switch to a trajectory estimation method based on inertial measurement units and geomagnetic sensors in indoor or signal-obstructed areas to obtain continuous position information. A particle filter algorithm is used to fuse and correct position information from different sensors, and output the real-time position information of each sampling point for recording.

5. The portable gas inspection and concentration distribution visualization method according to claim 4, characterized in that, The step of forming a spatiotemporal dataset associated with a gas concentration dataset includes: Generate a data record for each sampling point. Each data record includes at least a timestamp, latitude and longitude coordinates, gas concentration value, and sensor type identifier. The data records generated in chronological order are associated and organized to form the spatiotemporal dataset; The spatiotemporal dataset is compressed and encrypted in real time, and cached locally using both time and spatial indexes.

6. The portable gas inspection and concentration distribution visualization method according to claim 1, characterized in that, The step of constructing a three-dimensional concentration field model reflecting the continuous spatial distribution of gas concentration within the detection area using a spatial interpolation algorithm based on the spatiotemporal dataset includes: Spatial variogram analysis was performed on the spatiotemporal dataset to determine the spatial autocorrelation range and directional heterogeneity of gas concentration; Based on the aforementioned spatial autocorrelation range and directional heterogeneity, the search radius and weight parameters of the spatial interpolation algorithm are dynamically adjusted. The adjusted parameters are used to perform a spatial interpolation algorithm to generate the three-dimensional concentration field model.

7. The portable gas inspection and concentration distribution visualization method according to claim 1, characterized in that, The step of fusing and rendering the three-dimensional concentration field model with the two-dimensional map or three-dimensional real-world model of the area to be detected to generate a visual gas concentration distribution map, and intuitively displaying the concentration level and diffusion trend of each point in space with gradient colors, includes: The concentration values ​​of each spatial point in the three-dimensional concentration field model are mapped to preset gradient colors, and a semi-transparent three-dimensional concentration cloud map is generated based on the transparency setting. The three-dimensional concentration cloud map is aligned with the imported two-dimensional geographic information system map or oblique photogrammetry three-dimensional real scene model by coordinate alignment and layer overlay to generate a fused gas concentration distribution visualization map. The visualization map supports interactive operations such as zooming, rotating, and cross-sectional cutting, and automatically marks areas exceeding the standard according to preset concentration thresholds.

8. A portable gas inspection and concentration distribution visualization method and system, characterized in that, include: The concentration sampling module is used for the portable gas detector to perform continuous or preset interval pump-type sampling while moving within the detection area, and to acquire gas concentration datasets at different spatial sampling points in real time. The location association module is used to simultaneously record the real-time location information corresponding to each sampling point through the positioning unit integrated in the gas detector, forming a spatiotemporal dataset associated with the gas concentration dataset; The model building module is used to construct a three-dimensional concentration field model reflecting the continuous spatial distribution of gas concentration in the region to be detected based on the spatiotemporal dataset and using a spatial interpolation algorithm. The map generation module is used to fuse and render the three-dimensional concentration field model with the two-dimensional map or three-dimensional real scene model of the area to be detected, and generate a visual map of gas concentration distribution, which intuitively displays the concentration level and diffusion trend of each point in space with gradient colors.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the portable gas inspection and concentration distribution visualization method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the portable gas inspection and concentration distribution visualization method according to any one of claims 1 to 7.

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