Method and device for radon source localization and intensity estimation with a multi-detector array

Abstract

This application provides a method and apparatus for radon source localization and intensity estimation using a multi-detector array. The method includes: acquiring *a* valid reference events detected by the multi-detector array within a preset time period; the multi-detector array includes N detection nodes; performing spatiotemporal clustering on the *a* valid reference events to obtain *b* candidate homologous event clusters; filtering and analyzing the *b* candidate homologous event clusters based on the target space corresponding to the multi-detector array to obtain *c* event spatial coordinates; generating a three-dimensional density map of event point clouds based on the *c* event spatial coordinates; determining the location of the radon release source based on the local maxima of the three-dimensional density map of the event point cloud; and determining the relative release intensity corresponding to the location of the radon release source based on a preset diffusion decay model. By identifying radon sources through the spatial localization and density distribution of decay events, the accuracy of radon release source localization and intensity estimation is effectively improved.

Description

Technical Field

[0001] This application relates to the field of radon source localization technology, and in particular to a method and apparatus for radon source localization and intensity estimation using a multi-detector array. Background Technology

[0002] Radon gas and the alpha rays produced by its decay products are highly ionizing, and long-term exposure can easily cause radiation damage to the human respiratory system. Current radon detection methods mostly use single-point fixed detectors, which can only detect the average radon concentration in a local area and cannot accurately identify the source of radon leakage.

[0003] Traditional multi-point network monitoring methods typically deploy multiple independent detection terminals within the monitoring space, aggregate concentration monitoring data from each node, and generate a concentration distribution heat map using spatial interpolation algorithms. While this method can visually present the differences in radon concentration distribution, it still has significant technical limitations: the concentration heat map only reflects the steady-state accumulation of radon in the space and cannot distinguish the causes of high-concentration areas. It is difficult to determine whether high-concentration areas are caused by the continuous release of radon from local sources or by gas accumulation due to poor ventilation. Furthermore, radon concentration is highly susceptible to interference from external factors such as ambient airflow disturbances, temperature, and humidity, which can easily lead to missed or incorrect source area detection.

[0004] Therefore, improving the accuracy of radon gas release source location and intensity estimation is an urgent problem to be solved. Summary of the Invention

[0005] This application provides a method and apparatus for radon source localization and intensity estimation using a multi-detector array. By spatially locating effective reference events corresponding to alpha particle decay, and identifying radon release source regions based on the spatial density distribution of a large number of decay events, rather than relying on the indirect distribution of radon concentration accumulation, the accuracy of radon release source localization and intensity estimation is improved.

[0006] In a first aspect, embodiments of this application provide a method for radon source localization and intensity estimation using a multi-detector array, the method comprising: Acquire a valid reference events detected by a multi-detector array within a preset time period; the multi-detector array includes N detection nodes, where N is an integer greater than 3 and a is an integer greater than 1. Spatiotemporal clustering is performed on the a valid reference events to obtain b candidate homologous event clusters; b is a positive integer less than or equal to a; Based on the target space corresponding to the multi-detector array, the b candidate homologous event clusters are screened and analyzed to obtain c event space coordinates; c is a positive integer less than or equal to b. Generate a 3D density map of event point clouds based on the c event spatial coordinates; The location of the radon gas release source is determined based on the local maxima of the three-dimensional density map of the event point cloud. The relative release intensity corresponding to the location of the radon gas release source is determined based on the preset diffusion attenuation model.

[0007] Secondly, embodiments of this application provide a radon source localization and intensity estimation device with a multi-detector array. The device includes an acquisition module, a clustering module, a processing module, a generation module, and a determination module, wherein: The acquisition module is used to acquire a valid reference events detected by the multi-detector array within a preset time period; the multi-detector array includes N detection nodes, where N is an integer greater than 3 and a is an integer greater than 1. The clustering module is used to perform spatiotemporal clustering on the a valid reference events to obtain b candidate homologous event clusters; b is a positive integer less than or equal to a. The processing module is used to filter and analyze the b candidate homogeneous event clusters based on the target space corresponding to the multi-detector array, and obtain c event space coordinates; c is a positive integer less than or equal to b; The generation module is used to generate a three-dimensional density map of event point clouds based on the c event spatial coordinates. The determining module is used to determine the location of the radon gas release source based on the local maxima of the three-dimensional density map of the event point cloud; and to determine the relative release intensity corresponding to the location of the radon gas release source based on a preset diffusion attenuation model.

[0008] Thirdly, embodiments of this application provide an electronic device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing steps in any method of the first aspect of this application.

[0009] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in any method of the first aspect of this application.

[0010] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in any method of the first aspect of this application. The computer program product may be a software installation package.

[0011] By implementing the embodiments of this application, the effective reference events corresponding to alpha particle decay are spatially located, and radon emission source regions are identified based on the spatial density distribution of a large number of decay events, rather than relying on the indirect distribution of radon concentration accumulation, thereby improving the accuracy of radon emission source location and intensity estimation. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the composition of a radon gas source location and intensity estimation system provided in an embodiment of this application; Figure 2 This is a system architecture diagram of a radon gas source location and intensity estimation system provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a radon gas detection node provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application; Figure 5 This is a flowchart illustrating a method for locating and estimating the intensity of a radon gas source using a multi-detector array, as provided in an embodiment of this application. Figure 6 This is a schematic diagram of a process for generating a 3D density map of event point clouds provided in an embodiment of this application; Figure 7 This is a schematic flowchart illustrating a method for determining the location of a radon gas release source, provided in an embodiment of this application. Figure 8 This is a block diagram of the functional modules of a radon gas source localization and intensity estimation device with a multi-detector array provided in an embodiment of this application. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0015] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0016] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document indicates that the preceding and following related objects are in an "or" relationship. In the embodiments of this application, "multiple" refers to two or more.

[0017] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.

[0018] In this application, the term "connection" refers to various connection methods, such as direct connection or indirect connection, to achieve communication between devices. This application does not impose any limitations on this.

[0019] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0020] Radon gas and the alpha rays produced by its decay products are highly ionizing, and long-term exposure can easily cause radiation damage to the human respiratory system. Current radon detection methods mostly use single-point fixed detectors, which can only detect the average radon concentration in a local area and cannot accurately identify the source of radon leakage.

[0021] Traditional multi-point network monitoring methods typically deploy multiple independent detection terminals within the monitoring space, aggregate concentration monitoring data from each node, and generate a concentration distribution heat map using spatial interpolation algorithms. While this method can visually present the differences in radon concentration distribution, it still has significant technical limitations: the concentration heat map only reflects the steady-state accumulation of radon in the space and cannot distinguish the causes of high-concentration areas. It is difficult to determine whether high-concentration areas are caused by the continuous release of radon from local sources or by gas accumulation due to poor ventilation. Furthermore, radon concentration is highly susceptible to interference from external factors such as ambient airflow disturbances, temperature, and humidity, which can easily lead to missed or incorrect source area detection.

[0022] Therefore, improving the accuracy of radon gas release source location and intensity estimation is an urgent problem to be solved.

[0023] To address the aforementioned issues, this application provides a method and apparatus for radon source localization and intensity estimation using a multi-detector array. The method involves acquiring *a* valid reference events detected by the multi-detector array within a preset time period. The multi-detector array comprises N detection nodes, where N is an integer greater than 3 and *a* is an integer greater than 1. The *a* valid reference events are spatiotemporally clustered to obtain *b* candidate homologous event clusters, where *b* is a positive integer less than or equal to *a*. Based on the target space corresponding to the multi-detector array, the *b* candidate homologous event clusters are filtered and analyzed to obtain *c* event spatial coordinates, where *c* is a positive integer less than or equal to *b*. A three-dimensional density map of event point clouds is generated based on the *c* event spatial coordinates. The location of the radon release source is determined based on the local maxima of the three-dimensional density map of the event point cloud. The relative release intensity corresponding to the location of the radon release source is determined based on a preset diffusion attenuation model. By spatially locating effective reference events corresponding to alpha particle decay and identifying radon emission source regions based on the spatial density distribution of a large number of decay events, rather than relying on the indirect distribution of radon concentration accumulation, the accuracy of radon emission source location and intensity estimation can be improved.

[0024] For easier understanding, please refer to Figure 1 , Figure 1This is a schematic diagram of a radon source localization and intensity estimation system provided in an embodiment of this application. The radon source localization and intensity estimation system includes a multi-detector array, a synchronization beacon, and a data fusion and calculation unit. The multi-detector array includes N detection nodes, where N is an integer greater than or equal to 3. Each detection node is used to detect alpha particle events under a synchronized clock and generate a valid reference event containing a timestamp, waveform feature vector, energy window information, and signal amplitude. The synchronization beacon is used to periodically broadcast a time synchronization signal (i.e., a synchronization clock) to all detection nodes to keep the sampling clock error of each detection node within a preset accuracy range. The data fusion and calculation unit is used to receive the valid reference events generated by each detection node and execute the radon source localization and intensity estimation method described herein, outputting the identified radon release source location and its corresponding relative release intensity.

[0025] For easier understanding, please refer to Figure 2 , Figure 2 This is a system architecture diagram of a radon source location and intensity estimation system provided in this application embodiment. The bottom-layer synchronization beacon serves as a unified, high-precision time reference across the entire domain, providing a synchronization clock signal to all radon detection nodes. This ensures strict alignment of the alpha particle event timestamps collected by each node, providing a timing basis for subsequent spatiotemporal clustering and time-of-arrival (TOA) location. The middle layer consists of a multi-detector array composed of multiple independently deployed radon detection nodes. Each node receives the clock signal from the synchronization beacon, synchronously collects alpha particle decay events in the environment, and uploads valid event data, including timestamps, waveform feature vectors, energy window information, and signal amplitude, to the data fusion and computation unit. Simultaneously, it receives configuration commands from the data fusion and computation unit to dynamically adjust the acquisition parameters. The top-layer data fusion and computation unit establishes a bidirectional communication connection with each radon detection node, receives the event data uploaded by the nodes, performs spatiotemporal clustering, calculates three-dimensional event coordinates, constructs a three-dimensional density map of event point clouds, identifies the location of radon release sources, and estimates the relative release intensity, ultimately outputting a visualized source tracing result.

[0026] It is evident that by using multi-node synchronous detection, spatiotemporal clustering identification, and diffusion attenuation model inversion, the precise location of radon release sources and the quantitative estimation of their relative intensity have been achieved, thereby improving the source tracing capability and response speed of radon monitoring.

[0027] For easier understanding, please refer to Figure 3 , Figure 3This is a schematic diagram of a radon detection node provided in an embodiment of this application. Radon enters the electrostatic collection chamber, where its decay produces charged particles that are directionally driven by a high-voltage power supply and efficiently collected onto the surface of a semiconductor radon sensor, significantly improving detection efficiency. The semiconductor radon sensor receives the collected alpha particles, converting their ionization energy into weak electrical pulse signals, serving as the signal source for the radon detection node. The signal conditioning unit amplifies and shapes the weak pulse signals output by the semiconductor radon sensor, converting them into standard pulse signals with stable amplitude and regular shape. The noise suppression circuit (filter) filters out irrelevant signals such as environmental electromagnetic interference and circuit thermal noise, retaining genuine alpha particle event pulses, avoiding false triggering, and improving the signal-to-noise ratio. The MCU (microcontroller unit) is responsible for event recognition, waveform feature extraction, timestamp marking, vibration / attitude anomaly judgment, and interacts with the wireless communication module. The high-precision clock module provides a nanosecond-level time synchronization reference, marking each detection event with a high-precision timestamp, which is a crucial foundation for subsequent multi-node spatiotemporal clustering and time-of-arrival localization. Accelerometers / vibration sensors are used to monitor node attitude changes and environmental vibrations, assist in attitude correction, identify abnormal disturbance events, eliminate false triggers, and further improve data reliability. Wireless communication modules (such as Wi-Fi, Zigbee, LoRa) are used to establish bidirectional communication with the data fusion and computing unit, upload valid event data, and receive configuration parameters and synchronization commands.

[0028] It is evident that by integrating high-sensitivity detection, adaptive noise suppression, time synchronization, and auxiliary attitude correction modules, low-interference, high-precision alpha particle event acquisition and reliable data output are achieved, providing a solid front-end foundation for the accurate location and intensity estimation of radon gas sources.

[0029] The following is combined Figure 4 The electronic devices in the embodiments of this application will be described. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 4 As shown, the electronic device includes one or more processors, a memory, a communication interface, and one or more programs. The processor is connected to the memory and the communication interface via an internal communication bus.

[0030] The processor can be used for: Acquire a valid reference events detected by a multi-detector array within a preset time period; the multi-detector array includes N detection nodes, where N is an integer greater than 3 and a is an integer greater than 1. Spatiotemporal clustering is performed on the a valid reference events to obtain b candidate homologous event clusters; b is a positive integer less than or equal to a; Based on the target space corresponding to the multi-detector array, the b candidate homologous event clusters are screened and analyzed to obtain c event space coordinates; c is a positive integer less than or equal to b. Generate a 3D density map of event point clouds based on the c event spatial coordinates; The location of the radon gas release source is determined based on the local maxima of the three-dimensional density map of the event point cloud. The relative release intensity corresponding to the location of the radon gas release source is determined based on the preset diffusion attenuation model.

[0031] The one or more programs are stored in the aforementioned memory and configured to be executed by the aforementioned processor, and the one or more programs include instructions for performing any step in the above method embodiments.

[0032] The processor can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, cells, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication unit can be a communication interface, transceiver, transceiver circuit, etc., and the storage unit can be a memory.

[0033] The memory can be volatile or non-volatile, or a combination of both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0034] It is understood that the electronic device may include more or fewer structural elements than those shown in the block diagram above, such as a power module, physical buttons, a Wi-Fi module, a speaker, a Bluetooth module, sensors, a display module, etc., without limitation. It is understood that the electronic device may incorporate elements such as... Figure 1 The system architecture described above.

[0035] After understanding the software and hardware architecture of this application, the following will be combined with... Figure 5 This application describes a method for radon source localization and intensity estimation using a multi-detector array, as described in an embodiment of the present application. Figure 5 This is a flowchart illustrating a method for locating and estimating the intensity of a radon gas source using a multi-detector array, provided in an embodiment of this application. The method specifically includes the following steps: Step S501: Obtain a valid reference events detected by the multi-detector array within a preset time period.

[0036] The multi-detector array includes N detection nodes, where N is an integer greater than 3 and a is an integer greater than 1.

[0037] Specifically, N (N>3) radon detection nodes of the multi-detector array synchronously collect alpha particle signals generated by radon decay within a preset time period under a unified high-precision clock provided by the synchronization beacon. After front-end detection, signal conditioning, noise suppression, and event identification, the effective reference events are timestamped and encapsulated into data packets containing waveform feature vectors, energy window information, and signal amplitude. These data packets are then uploaded to the data fusion and calculation unit via the wireless communication module, where they are aggregated, deduplicated, and verified to finally obtain a (a>1) effective reference events within the preset time period.

[0038] Step S502: Spatiotemporal clustering is performed on the a valid reference events to obtain b candidate homologous event clusters.

[0039] Where b is a positive integer less than or equal to a, each valid reference event includes a timestamp, waveform feature vector, and energy window information, and the step of performing spatiotemporal clustering on the a valid reference events to obtain b candidate homogeneous event clusters includes the following steps: Perform a preset spatiotemporal clustering step on the a valid reference events to obtain the b candidate homogeneous event clusters; The spatiotemporal clustering steps are as follows: S11. Obtain any two valid reference events from the a valid reference events to obtain the first valid reference event and the second valid reference event; S12. Determine the first timestamp, first waveform feature vector, and first energy window information corresponding to the first valid reference event; S13. Determine the second timestamp, second waveform feature vector, and second energy window information corresponding to the second valid reference event; S14. If the time difference between the first timestamp and the second timestamp is less than a preset time threshold, the similarity between the first waveform feature vector and the second waveform feature vector is greater than a preset similarity threshold, and the first energy window information and the second energy window information represent the same type of decay nuclide, then the first valid reference event and the second valid reference event are classified into the same candidate homologous event cluster.

[0040] In a specific embodiment, after acquiring *a* valid reference events, spatiotemporal clustering is performed on these *a* valid reference events to identify a set of valid reference events that may originate from the same α decay event, i.e., a candidate homologous event cluster. For any two valid reference events among the *a* valid reference events, denoted as the first valid reference event and the second valid reference event, their respective timestamps, waveform feature vectors, and energy window information are extracted. Wherein: the first timestamp and the second timestamp represent the precise times at which the two valid reference events were recorded by their respective detector nodes; the first waveform feature vector and the second waveform feature vector characterize the waveform shape features of the pulse signals corresponding to the two valid reference events; the first energy window information and the second energy window information identify the decay nuclide categories corresponding to the two valid reference events, such as Po-218 or Po-214.

[0041] Next, it is determined whether the time difference between the first and second timestamps (i.e., the absolute value of the difference between the first and second timestamps) is less than a preset time threshold (e.g., 1 microsecond). This preset time threshold is set based on the maximum distance and synchronization accuracy between each detection node to ensure that the two events are sufficiently close in time. Then, the similarity between the first and second waveform feature vectors is calculated, and it is determined whether this similarity is greater than a preset similarity threshold. The cross-correlation coefficient method can be used to calculate the Pearson cross-correlation coefficient between the first and second waveform feature vectors. The Pearson cross-correlation coefficient ranges from -1 to 1; the closer the value is to 1, the more similar the waveforms are. When the absolute value of the Pearson cross-correlation coefficient (i.e., the similarity) is greater than the preset similarity threshold (e.g., 0.85), the two waveforms are considered similar. This preset similarity threshold can be determined through pre-calibration experiments and is used to distinguish between homologous events and random coincidences. Finally, it is determined whether the first and second energy window information represent the same type of decay nuclide. For example, the condition is satisfied if both are identified as Po-218 events or both are identified as Po-214 events; it is not satisfied if one is identified as Po-218 and the other as Po-214.

[0042] When all three conditions are met simultaneously—namely, the time difference is less than a preset time threshold, the similarity of the waveform feature vectors is greater than a preset similarity threshold, and the energy window information represents the same type of decay nuclide—then the first and second valid reference events are grouped into the same candidate homologous event cluster. By performing the above judgment on all possible pairs of valid reference events among *a* valid reference events, the valid reference events that meet the conditions can be gradually merged to ultimately form *b* candidate homologous event clusters. Each candidate homologous event cluster corresponds to an α decay event observed jointly by multiple detection nodes.

[0043] It is evident that by simultaneously verifying time synchronization, waveform feature similarity, and decay nuclide consistency, accurate clustering of homologous alpha particle events was achieved, effectively eliminating non-homologous interference events and providing a reliable event cluster basis for subsequent radon source location and intensity estimation.

[0044] Step S503: Based on the target space corresponding to the multi-detector array, the b candidate homologous event clusters are screened and analyzed to obtain c event space coordinates.

[0045] Where c is a positive integer less than or equal to b, the specific steps of filtering and analyzing the b candidate homogeneous event clusters to obtain c event space coordinates include: S21. Filter the candidate homogeneous event clusters that contain valid reference events greater than or equal to a preset threshold number from the b candidate homogeneous event clusters to obtain c target homogeneous event clusters; S22. Obtain multiple valid reference events corresponding to the first target homogeneous event cluster; the first target homogeneous event cluster is any one of the c target homogeneous event clusters; S23. Determine the multiple timestamps corresponding to the multiple valid reference events; S24. Determine the multiple detection nodes corresponding to the multiple valid reference events among the N detection nodes; S25. Obtain the spatial coordinates of the multiple detection nodes in the target space to obtain the spatial coordinates of the multiple nodes; S26. Using a preset time-of-arrival positioning algorithm, determine the event space coordinates corresponding to the first target homogeneous event cluster among the c event space coordinates based on the multiple node space coordinates and the multiple timestamps.

[0046] In a specific embodiment, firstly, a quality screening is performed on the b candidate homologous event clusters. That is, the number of valid reference events contained in each candidate homologous event cluster is counted, and the candidate homologous event clusters containing a number of valid reference events greater than or equal to a preset threshold (e.g., 3) are retained as target homologous event clusters, resulting in a total of c target homologous event clusters. Here, c is a positive integer less than or equal to b.

[0047] It should be noted that this preset threshold is based on the geometric constraints of the preset time-of-arrival (TOA) localization algorithm. For example, in three-dimensional space, observation data from at least three different detector nodes are required to uniquely determine the spatial coordinates of an event occurrence point. Therefore, a candidate cluster of related events must contain at least three valid reference events from different detector nodes to meet the basic conditions for localization. Candidate clusters of related events with insufficient valid reference events are discarded and not included in subsequent localization calculations.

[0048] Then, for each of the c target homogeneous event clusters obtained after filtering, the following localization solution process is executed to obtain c event space coordinates. Each event space coordinate corresponds to the location of an α decay event observed by multiple nodes in the target space. The localization solution process is illustrated using any one of the target homogeneous event clusters (denoted as the first target homogeneous event cluster) as an example: First, multiple valid reference events contained in the first target homogeneous event cluster are obtained, and the timestamp of each valid reference event is extracted, resulting in multiple timestamps. These multiple timestamps record the precise time when the same alpha decay event was detected by different detector nodes, and are the core input parameters for time difference of arrival (TDOA) localization. Next, the detector nodes corresponding to each of the multiple valid reference events are determined, resulting in multiple detector nodes. Based on the deployment information of the multi-detector array, the spatial coordinates of each detector node in the target space are obtained, resulting in multiple node spatial coordinates. These multiple node spatial coordinates are known quantities and can be manually measured and entered during on-site deployment, or automatically obtained by the localization module built into each detector node.

[0049] Next, using the time-of-arrival (TOA) localization algorithm, the three-dimensional spatial coordinates of the α-decay event corresponding to the first target homogeneous event cluster are calculated based on the spatial coordinates of multiple detector nodes and their corresponding timestamps. Specifically, any two detector nodes are used as focal points, and the spatial distance difference corresponding to the time difference between the arrival of the valid reference event by these two detector nodes is used as a fixed length to determine the first hyperboloid equation. When there are three or more detector nodes, multiple hyperboloid equations can be constructed. The intersection of these hyperboloids is the spatial location of the event occurrence point, i.e., the spatial coordinates of the first event. Optimization methods such as least squares or weighted least squares can be used to find the spatial coordinates that minimize the sum of squared residuals of each hyperboloid equation, which is then used as the localization result for this decay event.

[0050] It is evident that by selecting event clusters with a sufficient number of valid events and employing a time difference of arrival (TDOA) positioning algorithm based on multi-node timestamps and spatial coordinates, precise three-dimensional spatial positioning of event clusters from the same source was achieved, effectively eliminating interference from invalid clusters with a low number of events.

[0051] Step S504: Generate a three-dimensional density map of event point clouds based on the c event spatial coordinates.

[0052] For easier understanding, please refer to Figure 6 , Figure 6 This is a flowchart illustrating a process for generating a 3D density map of event point clouds according to an embodiment of this application. The specific steps for generating the 3D density map of event point clouds based on the c event spatial coordinates include: S31. Divide the target space into multiple grid spaces according to a preset space size; S32. Map each of the c event space coordinates to its corresponding grid space. S33. Count the number of event space coordinates corresponding to each of the multiple grid spaces to obtain multiple density values; S34. Generate the three-dimensional density map of the event point cloud based on the multiple grid spaces and the multiple density values.

[0053] In a specific embodiment, the target space covered by the multi-detector array is divided into multiple regular grid spaces according to a preset spatial size (i.e., spatial resolution), and each grid space is a voxel. The smaller the voxel size, the higher the resolution of the 3D density map of the event point cloud, but the fewer events per voxel, and the greater the statistical fluctuations; the larger the voxel size, the better the statistical stability, but the lower the positioning accuracy. In practical applications, the voxel side length can be dynamically adjusted according to the scale of the target space and the total number of event coordinates, and is usually between 0.1 meters and 1 meter, without specific limitation here.

[0054] For each of the c event space coordinates, determine the grid space (i.e., voxel grid) in which its spatial location falls, and establish a correspondence between the event space coordinate and the grid space. After completing the grid mapping of all event space coordinates, for each of the multiple grid spaces, count the number of event space coordinates falling within that grid space. This number is the density value corresponding to that grid space, directly reflecting the frequency of alpha decay events occurring within the spatial region represented by that grid space.

[0055] Based on multiple grid spaces and multiple density values, a three-dimensional scalar field data structure, namely a three-dimensional density map of event point clouds, is constructed. This three-dimensional density map of event point clouds includes grid space partitioning information, event count density values, and adjacency topology relationships. Grid space partitioning information includes the spatial coordinates and voxel dimensions of each grid space; event count density values ​​represent the number of event spatial coordinates within each grid space; and adjacency topology relationships define the connections between each grid space and its adjacent grid spaces in the six directions (up / down, left / right, front / back), providing topological support for subsequent searches for local maxima.

[0056] As can be seen, by gridding the target space and generating a three-dimensional density map by counting the number of event coordinate mappings, an intuitive quantitative representation of the spatial distribution of radon decay events is achieved, providing clear and reliable data for subsequent identification of radon gas source locations.

[0057] Step S505: Determine the location of the radon gas release source based on the local maxima of the three-dimensional density map of the event point cloud.

[0058] For easier understanding, please refer to Figure 7 , Figure 7 This is a flowchart illustrating a method for determining the location of a radon gas release source, provided in an embodiment of this application. The specific steps for determining the location of the radon gas release source based on the local maxima of the three-dimensional density map of the event point cloud include: S41. Filter out the local maxima of the multiple density values ​​that are higher than a preset density threshold and higher than the density values ​​of all adjacent grid spaces; S42. Determine the target grid space corresponding to the local maxima in the plurality of grid spaces; S43. Determine the location of the radon gas release source based on the spatial coordinates of the center point corresponding to the target grid space.

[0059] In a specific embodiment, local maxima are determined for each of the multiple grid spaces in the 3D density map of the event point cloud according to a first and a second determination condition. The first determination condition is that the density value of the grid space is greater than a preset density threshold. This preset density threshold is used to filter out low-density spurious peaks caused by statistical fluctuations or background noise, ensuring that the identified peaks are statistically significant. The specific value of this preset density threshold can be set according to the total number of event coordinates and the target space volume in the actual application scenario. For example, it can be a multiple of the average density value of all grid spaces, or determined by comparison with calibration data in a passive environment. No specific limitation is made here. The second determination condition is that the density value of the grid space is higher than the density values ​​of all its adjacent grid spaces. Where each grid space has multiple adjacent grid spaces in 3D space (such as shared faces, edges, or vertices), the local maxima requirement is that the density value of the grid space is strictly greater than the density value of each adjacent grid space, thereby ensuring that the identified peak is a local maximum in space, rather than a transition point located on a density slope. Through the filtering of these two conditions, local maxima points that meet the conditions are selected from the density values ​​of all grid spaces. In a typical indoor setting, there is usually at least one local maximum point, which corresponds to different potential radon release sources or radon accumulation centers.

[0060] Then, the grid space corresponding to the selected local maxima points in the 3D density map of the event point cloud is obtained, and this is used as the target grid space. This target grid space is the spatial cell where the density peak is located, and its spatial location is the region where the potential radon emission source is located. The spatial coordinates of the center point of the target grid space can be directly used as the estimated value of the radon emission source location. It should be noted that if there is at least one target grid space, then there is also at least one corresponding radon emission source location.

[0061] As can be seen, by selecting grid spaces with density values ​​higher than the preset threshold and being local maxima, and taking their center coordinates, the location of radon gas release sources can be accurately identified from the 3D density map of the event point cloud. This effectively eliminates the interference of random event noise and non-source region accumulation, providing reliable spatial positioning results for subsequent source strength estimation.

[0062] Step S506: Determine the relative release intensity corresponding to the location of the radon gas release source based on the preset diffusion attenuation model.

[0063] The specific steps for determining the relative release intensity corresponding to the location of the radon gas release source based on a preset diffusion attenuation model include: S51. Obtain the number of valid reference events that meet the preset conditions in each of the N detection nodes to obtain the N event count rate; the preset conditions are within a preset time period and within a preset angle range with the direction pointing to the radon gas release source as the axis. S52. Obtain the spatial distance between each of the N detection nodes and the location of the radon gas release source, thus obtaining N spatial distances; S53. Using the diffusion attenuation model, determine the relative release intensity corresponding to the location of the radon gas release source based on the N event count rates and the N spatial distances.

[0064] In a specific embodiment, for each detection node in the multi-detector array at the determined radon emission source location, the number of valid reference events meeting preset conditions within a preset time period is counted and converted into a count rate per unit time, resulting in N event count rates. The preset condition is that the event space coordinates corresponding to the valid reference event fall within a conical spatial region with the detection node as the vertex, the spatial vector pointing to the radon emission source location as the axis, and the angle not exceeding a preset angle threshold (e.g., 15°). Decay events originating from the direction of the radon emission source can be filtered from all valid reference events recorded by the detection node, excluding background events from other directions or irrelevant source regions, thereby improving the accuracy of source intensity inversion. The length of the preset time period can be set according to actual application requirements. In conventional monitoring scenarios, 60 minutes can be used to obtain a statistically stable event count rate; in emergency scenarios requiring rapid assessment, it can be shortened to 10 to 30 minutes, sacrificing some statistical accuracy for faster response.

[0065] Then, based on the spatial coordinates of each detection node and the spatial coordinates of the radon gas release source, the Euclidean distance between them is calculated to obtain N spatial distances. These spatial distances are used to characterize the length of the spatial transmission path of radon gas from the release source to each detection node.

[0066] Next, using a pre-defined diffusion attenuation model, the relative release intensity corresponding to the location of the radon emission source is derived through data fitting based on N event count rates and N spatial distances. Key parameters in the diffusion attenuation model, including the attenuation exponent and the proportionality constant related to detector efficiency, have been pre-determined through calibration experiments under a standard radon source environment before the system leaves the factory and are stored in a pre-defined algorithm library. In the inversion calculation, the relative release intensity is used as the variable to be determined, and the spatial distances of each detection node are used as known inputs to construct expressions for the theoretical event count rates of each detection node. Then, the optimal relative release intensity is solved by minimizing the error between the theoretical event count rate of each detection node and the actual observed event count rate (i.e., the N event count rates). Optimization algorithms such as weighted least squares can be used, with the inverse variance of the event count rate of each detection node as a weight to reduce the contribution of detection nodes with large statistical fluctuations. This relative release intensity is a dimensionless intensity level, applicable to comparing the relative strength of multiple radon emission sources within the same target space.

[0067] It is evident that by combining the statistical event count rate with the directional range and relying on the spatial distance and diffusion attenuation model to calculate the release intensity, a precise quantitative assessment of the relative release intensity can be achieved.

[0068] In one possible embodiment, node geometric parameters and attenuation model coefficients can be pre-obtained to avoid ambiguity or errors during on-site calculations. Node geometric parameters include the spatial coordinates of each probe node. , ), detect solid angle (Determined by the detector's shielding structure; for example, nodes using a hemispherical detector structure...) Pick Attenuation model coefficients: proportionality constant K and diffusion attenuation exponent, which are related to detector efficiency. , In a closed space without forced ventilation, a standard value of 2 is used. In environments with weak ventilation, it can be calibrated through pre-experimentation, with a value ranging from 1.8 to 2.2, without specific limitations here. It should be noted that the diffusion attenuation model describes the relationship between the event count rate C at the detection node and the relative release intensity Q, and the spatial distance d from the node to the source, which can be expressed in power-law form: .

[0069] In one possible embodiment, for each radon gas release source location =( , The relative release intensity is calculated using the following procedure: Step 1: Select valid observation nodes: First, exclude two types of invalid nodes and retain only the observation samples that participate in the calculation, such as nodes that are blocked by walls / furniture or are not within the visible range of the candidate source (which can be determined by ray collision detection on the spatial grid map when the node is deployed); nodes whose event count rate in this direction is lower than the background noise threshold (e.g., ≤0.01cpm) to avoid excessive statistical error.

[0070] Step 2: Calculate the theoretical event count rate for a single observation node: For the i-th valid observation node, first calculate its Euclidean distance to the current radon emission source location: ; in, This represents the Euclidean distance between the location of the s-th radon gas release source and the i-th detection node.

[0071] Substituting this back into the diffusion decay model, we obtain the theoretical event count rate corresponding to this probe node: ; in, This represents the relative release intensity at the location of the s-th radon release source; This represents the theoretical event count rate of the i-th detection node for the location of the s-th radon release source; This represents a proportionality constant related to detector efficiency; This represents the diffusion decay index.

[0072] Step 3: Match the actual event count rate to the actual observed event count rate: Within the past T time window (default is 1 hour, short-term rapid assessment can be 10 minutes), all location events fall within the specified time frame. The total number of events within a spherical region centered at a radius of r (default value is 0.5m), converted into the actual event count rate. .

[0073] Step 4: Least squares method to invert source strength: For all valid detection nodes, construct the error function: ; in, This represents the actual event count rate observed by the i-th probe node within the statistical window T. This can be achieved by minimizing... Solving for the optimal The final output relative release intensity It is a dimensionless intensity level, which can be directly used to compare the strength of multiple sources in the same space.

[0074] The method further includes the following steps: S61. Obtain the architectural floor plan or 3D model of the target space; S62. Overlay and display the real-time event point cloud in the three-dimensional density map of the event point cloud onto the building floor plan or the three-dimensional model; S63. Determine the source location icon based on the relative release intensity; the source location icon is used to represent the magnitude of the relative release intensity with different sizes or colors; S64. At the location of the radon gas release source in the building plan or the three-dimensional model, display the source location icon overlaid.

[0075] In a specific embodiment, firstly, a building plan or 3D model of the target space covered by the multi-detector array is acquired. The building plan is suitable for 2D display scenarios of a single-layer space, while the 3D model is suitable for display scenarios of multi-layer or three-dimensional spaces. On the building plan or 3D model, a real-time event point cloud from a 3D density map is overlaid as a semi-transparent scatter plot layer. Each scatter plot corresponds to the spatial coordinates of a successfully located alpha decay event. The spatial position of the scatter plots is aligned with the coordinate system of the target space, allowing the user to intuitively observe the distribution density and clustering areas of decay events within the target space.

[0076] Then, based on the relative release intensity corresponding to each radon emission source location, a source location icon is determined for each radon emission source location. This source location icon is used to represent the magnitude of the relative release intensity with different visual attributes, making it easy for users to quickly identify the intensity level of each source. Visual attributes include, but are not limited to, icon size and icon color, which are not specifically limited here. The greater the relative release intensity, the larger the source location icon and the darker the icon color, to intuitively reflect the intensity level of the radon emission source. Next, on the building floor plan or 3D model, the generated source location icons are overlaid at the spatial coordinates corresponding to each radon emission source location. The center point of this source location icon is precisely aligned with the spatial coordinates of the determined radon emission source location to ensure the accuracy of the indicated location. When multiple radon emission source locations are identified within the target space, all source location icons are displayed simultaneously, allowing users to intuitively determine the main radon emission source by comparing the size and color intensity of each icon.

[0077] As can be seen, by visually overlaying the distribution of events and the location of the source, and distinguishing the release intensity with icon styles, the monitoring results are presented intuitively, which facilitates the rapid investigation of radon gas hazards.

[0078] The above primarily describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the electronic device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0079] This application embodiment can divide the electronic device into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0080] When dividing each function into modules according to its corresponding function. Figure 8 This is a functional block diagram of a radon source localization and intensity estimation device with a multi-detector array provided in an embodiment of this application. The radon source localization and intensity estimation device 800 with a multi-detector array includes an acquisition module 810, a clustering module 820, a processing module 830, a generation module 840, and a determination module 850, wherein: The acquisition module 810 is used to acquire a valid reference events detected by the multi-detector array within a preset time period; the multi-detector array includes N detection nodes, where N is an integer greater than 3 and a is an integer greater than 1. The clustering module 820 is used to perform spatiotemporal clustering on the a valid reference events to obtain b candidate homologous event clusters; b is a positive integer less than or equal to a. The processing module 830 is used to filter and analyze the b candidate homogeneous event clusters based on the target space corresponding to the multi-detector array, and obtain c event space coordinates; c is a positive integer less than or equal to b; The generation module 840 is used to generate a three-dimensional density map of event point clouds based on the c event spatial coordinates. The determining module 850 is used to determine the location of the radon gas release source based on the local maxima of the three-dimensional density map of the event point cloud; and to determine the relative release intensity corresponding to the location of the radon gas release source based on a preset diffusion attenuation model.

[0081] Optionally, each valid reference event includes a timestamp, waveform feature vector, and energy window information. Regarding the spatiotemporal clustering of the a valid reference events to obtain b candidate homogeneous event clusters, the clustering module 820 is specifically used for: Perform a preset spatiotemporal clustering step on the a valid reference events to obtain the b candidate homogeneous event clusters; The spatiotemporal clustering steps are as follows: Obtain any two valid reference events from the a valid reference events to obtain the first valid reference event and the second valid reference event; Determine the first timestamp, first waveform feature vector, and first energy window information corresponding to the first valid reference event; Determine the second timestamp, second waveform feature vector, and second energy window information corresponding to the second valid reference event; If the time difference between the first timestamp and the second timestamp is less than a preset time threshold, the similarity between the first waveform feature vector and the second waveform feature vector is greater than a preset similarity threshold, and the first energy window information and the second energy window information represent the same type of decay nuclide, then the first valid reference event and the second valid reference event are classified into the same candidate homologous event cluster.

[0082] Optionally, in the process of filtering and analyzing the b candidate homogeneous event clusters to obtain c event space coordinates, the processing module 830 is specifically used for: By filtering the b candidate homogeneous event clusters, those containing valid reference events greater than or equal to a preset threshold number of events are obtained, c target homogeneous event clusters are obtained. Obtain multiple valid reference events corresponding to the first target homogeneous event cluster; the first target homogeneous event cluster is any one of the c target homogeneous event clusters. Determine the multiple timestamps corresponding to the multiple valid reference events; Determine the multiple probe nodes corresponding to the multiple valid reference events among the N probe nodes; Obtain the spatial coordinates of the multiple detection nodes in the target space to obtain the spatial coordinates of multiple nodes; Using a preset time-of-arrival (TOA) positioning algorithm, the event space coordinates corresponding to the first target homogeneous event cluster among the c event space coordinates are determined based on the multiple node spatial coordinates and the multiple timestamps.

[0083] Optionally, in generating the 3D density map of the event point cloud based on the c event space coordinates, the generation module 840 is specifically used for: The target space is divided into multiple grid spaces according to a preset space size; Map each of the c event space coordinates to its corresponding grid space. The number of event space coordinates corresponding to each of the multiple grid spaces is counted to obtain multiple density values; The event point cloud 3D density map is generated based on the multiple grid spaces and the multiple density values.

[0084] Optionally, in determining the location of the radon gas release source based on the local maxima of the three-dimensional density map of the event point cloud, the determining module 850 is specifically used for: The local maxima of the density values ​​are selected from the plurality of density values ​​that are higher than a preset density threshold and higher than the density values ​​of all adjacent grid spaces. Determine the target grid space corresponding to the local maxima in the plurality of grid spaces; The location of the radon gas release source is determined based on the spatial coordinates of the center point corresponding to the target grid space.

[0085] Optionally, in determining the relative release intensity corresponding to the location of the radon gas release source based on a preset diffusion attenuation model, the determining module 850 is further specifically used for: The number of valid reference events that meet preset conditions in each of the N detection nodes is obtained to obtain the N event count rate; the preset conditions are that they are within a preset time period and within a preset angle range with the direction pointing to the radon gas release source as the axis; Obtain the spatial distance between each of the N detection nodes and the location of the radon gas release source to obtain N spatial distances; Using the diffusion decay model, the relative release intensity corresponding to the location of the radon gas release source is determined based on the N event count rates and the N spatial distances.

[0086] Optionally, the processing module 830 is further specifically used for: Obtain the architectural floor plan or 3D model of the target space; The real-time event point cloud in the three-dimensional density map of the event point cloud is overlaid and displayed on the building floor plan or the three-dimensional model. The source location icon is determined based on the relative release intensity; the source location icon is used to represent the magnitude of the relative release intensity using different sizes or colors. An icon representing the radon gas release source is overlaid on the building floor plan or the 3D model.

[0087] It should be noted that the specific implementation of each operation can be described in the corresponding description of the method embodiment shown above. The radon gas source localization and intensity estimation device 800 with multiple detector arrays can be used to execute the method embodiment of this application, and will not be described again here.

[0088] This application also provides a computer-readable storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.

[0089] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0090] It should be noted that, for the sake of simplicity, the above embodiments are all described as a series of actions. Those skilled in the art should understand that this application is not limited to the described order of actions, as some steps in the embodiments of this application can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions, steps, modules, or units involved are not necessarily essential to the embodiments of this application.

[0091] In the above embodiments, the descriptions of each embodiment in this application have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0092] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0093] The steps of the methods or algorithms described in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, portable hard disk, read-only optical disk (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Furthermore, the ASIC can reside in a terminal device or management device. Alternatively, the processor and storage medium can exist as discrete components in the terminal device or management device.

[0094] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in the embodiments of this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0095] The modules / units included in the various devices and products described in the above embodiments can be software modules / units, hardware modules / units, or a combination of both. For example, for devices and products applied to or integrated into a chip, all modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits. For devices and products applied to or integrated into a chip module, all modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The implementation is achieved through a software program that runs on the processor integrated within the chip module. The remaining modules / units (if any) can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into terminal equipment, each of their modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components within the terminal equipment. Alternatively, at least some modules / units can be implemented through a software program that runs on the processor integrated within the terminal equipment, while the remaining modules / units (if any) can be implemented using hardware methods such as circuits.

[0096] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this application. It should be understood that the above descriptions are merely specific embodiments of the embodiments of this application and are not intended to limit the protection scope of the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solutions of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. A method of radon source localization and intensity estimation with a multi-probe array, characterized in that, The method includes: Acquire a valid reference events detected by a multi-detector array within a preset time period; the multi-detector array includes N detection nodes, where N is an integer greater than 3 and a is an integer greater than 1. Spatiotemporal clustering is performed on the a valid reference events to obtain b candidate homogeneous event clusters; b is a positive integer less than or equal to a; Based on the target space corresponding to the multi-detector array, the b candidate homologous event clusters are screened and analyzed to obtain c event space coordinates; c is a positive integer less than or equal to b. Generate a 3D density map of event point clouds based on the c event spatial coordinates; The location of the radon gas release source is determined based on the local maxima of the three-dimensional density map of the event point cloud. Based on a preset diffusion attenuation model, the relative release intensity corresponding to the location of the radon gas release source is determined; Each valid reference event includes a timestamp, waveform feature vector, and energy window information. The spatiotemporal clustering of the *a* valid reference events yields *b* candidate clusters of homologous events, including: Perform a preset spatiotemporal clustering step on the a valid reference events to obtain the b candidate homogeneous event clusters; The spatiotemporal clustering steps are as follows: Obtain any two valid reference events from the a valid reference events to obtain the first valid reference event and the second valid reference event; Determine the first timestamp, first waveform feature vector, and first energy window information corresponding to the first valid reference event; Determine the second timestamp, second waveform feature vector, and second energy window information corresponding to the second valid reference event; If the time difference between the first timestamp and the second timestamp is less than a preset time threshold, the similarity between the first waveform feature vector and the second waveform feature vector is greater than a preset similarity threshold, and the first energy window information and the second energy window information represent the same type of decay nuclide, then the first valid reference event and the second valid reference event are classified into the same candidate homologous event cluster.

2. The method of claim 1, wherein, The process of filtering and analyzing the b candidate homogeneous event clusters to obtain c event space coordinates includes: By filtering the b candidate homogeneous event clusters, those containing valid reference events greater than or equal to a preset threshold number of events are obtained, c target homogeneous event clusters are obtained. Obtain multiple valid reference events corresponding to the first target homogeneous event cluster; the first target homogeneous event cluster is any one of the c target homogeneous event clusters. Determine the multiple timestamps corresponding to the multiple valid reference events; Determine the multiple probe nodes corresponding to the multiple valid reference events among the N probe nodes; Obtain the spatial coordinates of the multiple detection nodes in the target space to obtain the spatial coordinates of multiple nodes; Using a preset time-of-arrival (TOA) positioning algorithm, the event space coordinates corresponding to the first target homogeneous event cluster among the c event space coordinates are determined based on the multiple node spatial coordinates and the multiple timestamps.

3. The method of claim 1, wherein, The step of generating a 3D density map of event point clouds based on the c event spatial coordinates includes: The target space is divided into multiple grid spaces according to a preset space size; Map each of the c event space coordinates to its corresponding grid space. The number of event space coordinates corresponding to each of the multiple grid spaces is counted to obtain multiple density values; The event point cloud 3D density map is generated based on the multiple grid spaces and the multiple density values.

4. The method of claim 3, wherein, The step of determining the location of the radon gas release source based on the local maxima of the three-dimensional density map of the event point cloud includes: The local maxima of the density values ​​are selected from the plurality of density values ​​that are higher than a preset density threshold and higher than the density values ​​of all adjacent grid spaces. Determine the target grid space corresponding to the local maxima in the plurality of grid spaces; The location of the radon gas release source is determined based on the spatial coordinates of the center point corresponding to the target grid space.

5. The method of claim 4, wherein, The step of determining the relative release intensity corresponding to the location of the radon gas release source based on a preset diffusion attenuation model includes: The number of valid reference events that meet preset conditions in each of the N detection nodes is obtained to obtain the N event count rate; the preset conditions are that they are within a preset time period and within a preset angle range with the direction pointing to the radon gas release source as the axis; Obtain the spatial distance between each of the N detection nodes and the location of the radon gas release source to obtain N spatial distances; Using the diffusion decay model, the relative release intensity corresponding to the location of the radon gas release source is determined based on the N event count rates and the N spatial distances.

6. The method according to any one of claims 1 to 5, wherein, The method further includes: Obtain the architectural floor plan or 3D model of the target space; The real-time event point cloud in the three-dimensional density map of the event point cloud is overlaid and displayed on the building floor plan or the three-dimensional model. The source location icon is determined based on the relative release intensity; the source location icon is used to represent the magnitude of the relative release intensity using different sizes or colors. An icon representing the radon gas release source is overlaid on the building floor plan or the 3D model.

7. A radon source localization and intensity estimation apparatus for a multi-detector array for performing the method according to any one of claims 1 to 6, characterized in that The device includes an acquisition module, a clustering module, a processing module, a generation module, and a determination module, wherein: The acquisition module is used to acquire a valid reference events detected by the multi-detector array within a preset time period; the multi-detector array includes N detection nodes, where N is an integer greater than 3 and a is an integer greater than 1. The clustering module is used to perform spatiotemporal clustering on the a valid reference events to obtain b candidate homologous event clusters; b is a positive integer less than or equal to a. The processing module is used to filter and analyze the b candidate homogeneous event clusters based on the target space corresponding to the multi-detector array, and obtain c event space coordinates; c is a positive integer less than or equal to b; The generation module is used to generate a three-dimensional density map of event point clouds based on the c event spatial coordinates. The determining module is used to determine the location of the radon gas release source based on the local maxima of the three-dimensional density map of the event point cloud; and to determine the relative release intensity corresponding to the location of the radon gas release source based on a preset diffusion attenuation model.

8. An electronic device, comprising: include: Processor, memory, communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-6.

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