A mobile device real-time positioning monitoring method and system based on multi-mode wireless sniffing and cloud computing

Abstract

The application discloses a kind of based on multi-mode wireless sniffing and cloud computing mobile device real-time positioning monitoring method and system, the method includes: distributed sniffing terminal is in listening mode, the sniffing data of surrounding mobile device is collected, and the sniffing data is encrypted and uploaded to cloud processing platform;Cloud processing platform carries out unified access and time synchronization to the sniffing data of multiple terminals, through multi-mode heterogeneous data fusion and association mechanism, WiFi sniffing data and Bluetooth sniffing data from the same mobile device are matched, and position estimation is completed in combination with multi-point observation and multi-band signal characteristics, and the positioning result of mobile device is output;The cloud processing platform is based on the positioning result of multiple mobile devices, generates dynamic heat map in user interface display, to support historical trajectory query and regional people flow density statistical analysis.The application realizes high concurrency, real-time, wide coverage mobile device sniffing, positioning and heat visualization.

Description

Technical Field

[0001] This invention relates to the field of real-time positioning and monitoring technology for mobile devices in indoor-outdoor integrated scenarios, and particularly to a method and system for real-time positioning and monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing. Background Technology

[0002] In large indoor spaces or semi-open spaces such as shopping malls, exhibition halls, and hospitals, real-time location monitoring and thermal distribution analysis of mobile devices carried by personnel are crucial foundational capabilities for smart security, emergency evacuation, operational management, and resource allocation. Compared to outdoor satellite positioning, indoor environments are generally characterized by multipath propagation and non-line-of-sight (NLoS) effects due to factors such as wall obstruction, metal structure reflections, and dense crowds. This makes the strength and propagation path of wireless signals highly unstable, leading to drift and jumps in location estimation. Furthermore, practical applications often require systems with wide coverage, high concurrency, and second-level real-time update capabilities. This means continuously collecting surrounding wireless signals across multiple floors and areas, rapidly processing massive amounts of data streams from terminals, and outputting continuous location results suitable for heatmap display and trajectory analysis. Therefore, passive sensing using general-purpose wireless signals and unified processing and visualization through centralized cloud computing is gradually becoming an important development direction for related applications.

[0003] Existing technologies mainly include WiFi-based positioning technology, Bluetooth-based positioning or sniffing technology, and positioning computing and data processing architectures at the edge or cloud. For WiFi positioning, common solutions include weighted centroid positioning based on Received Signal Strength Indication (RSSI), trilateration, or multipoint positioning methods. These methods typically use observations from multiple access points or probes to infer the terminal's location, making deployment relatively convenient. However, due to the complex indoor propagation environment, the relationship between RSSI and distance is unstable, and accuracy and robustness are significantly affected by multipath propagation, occlusion, and changes in pedestrian flow. Another type of WiFi fingerprint positioning solution establishes a fingerprint database by offline collection of "location-signal feature" mappings, and outputs the location through matching during the online phase. This type of solution achieves good results in relatively static environments, but it is highly dependent on the amount of offline data collected and fingerprint database updates. Once the venue structure, equipment layout, or population distribution changes, the fingerprint database requires frequent maintenance, resulting in high engineering costs and difficulty in maintaining consistent performance over a large scale and long term. In addition, there is a positioning and trajectory analysis technology based on WiFi passive sniffing. In this technology, the probe device works in listening mode, passively capturing the detection requests or related management frames sent by the mobile terminal, extracting features such as identification information, timestamps, signal strength and channel, and then combining multi-probe observations to estimate the location. This type of technology is closely related to the link of "distributed sniffing terminal + data reporting + centralized processing", but in high-density scenarios, it is easy to form massive, redundant and noisy data streams, which puts higher demands on acquisition throughput, deduplication and aggregation, correlation matching and real-time computing.

[0004] In Bluetooth-related technologies, common methods include deploying Bluetooth Low Energy (BLE) beacons for area triggering or RSSI positioning, and using Bluetooth scanning / listening to obtain surrounding broadcast information for device identification or near-field judgment. BLE beacon solutions typically require dense deployment in a given location, along with battery replacement, inspection, and maintenance, resulting in high overall operation and maintenance costs. Furthermore, the signal is also susceptible to obstruction and environmental changes. While Bluetooth passive sniffing can supplement device identification and near-field sensing capabilities, relying solely on Bluetooth makes it difficult to stably output continuous and usable spatial coordinates in complex scenarios. It usually needs to be used in conjunction with other wireless modes to improve positioning stability and coverage.

[0005] In summary, existing single WiFi or single Bluetooth positioning solutions generally suffer from insufficient positioning accuracy and stability, and difficulty in balancing coverage and maintenance costs in complex indoor environments. While passive sniffing-based solutions have the advantage of scalable data collection, they are easily limited by data collection throughput and real-time computing capabilities in high-concurrency scenarios. Furthermore, the lack of efficient synchronization and fusion mechanisms between multiple probes and modalities makes it difficult to stably form a continuous business closed loop of "real-time positioning output - dynamic thermal visualization - historical trajectory and regional density analysis," and thus cannot meet the comprehensive requirements of real-time performance, wide coverage, and high concurrency processing under large-scale deployment conditions.

[0006] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0007] The main objective of this invention is to provide a method and system for real-time location monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing, aiming to solve the problems of insufficient positioning accuracy and stability, and difficulty in balancing coverage and maintenance costs in complex indoor environments, which are common problems with existing single WiFi positioning or single Bluetooth solutions.

[0008] To achieve the above objectives, this invention provides a method for real-time location monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing. This method is applied to a real-time location monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing. The system includes one or more distributed sniffing terminals and a cloud processing platform. The method comprises the following steps: In monitoring mode, the distributed sniffing terminal collects sniffing data from surrounding mobile devices, encrypts the sniffing data, and uploads it to the cloud processing platform. The sniffing data includes identification information, signal strength, and channel-related information. The cloud processing platform performs unified access and time synchronization of the sniffing data from multiple terminals. Through a multi-mode heterogeneous data fusion and association mechanism, it matches WiFi sniffing data and Bluetooth sniffing data from the same mobile device, and combines multi-point observation and multi-frequency signal characteristics to complete location estimation and output the positioning result of the mobile device. The positioning result includes real-time coordinates and trajectory information. The cloud processing platform generates dynamic heatmaps based on the positioning results of multiple mobile devices and displays them on the user interface to support historical trajectory queries and regional population density statistical analysis.

[0009] Optionally, in the aforementioned method for real-time location monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing, the distributed sniffing terminal includes a WiFi sniffing module, a Bluetooth sniffing module, and a computing module; the sniffing data includes WiFi sniffing data and Bluetooth sniffing data.

[0010] Optionally, the method for real-time location monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing includes the following: In monitoring mode, the distributed sniffing terminal collects sniffing data from surrounding mobile devices, encrypts the sniffing data, and uploads it to the cloud processing platform. Specifically, this includes: In monitoring mode, the WiFi sniffing module captures wireless frames generated by surrounding mobile devices and extracts WiFi sniffing data for location purposes. The Bluetooth sniffing module scans the BLE broadcast signal and acquires Bluetooth sniffing data for supplementary positioning. The computing module performs data preprocessing and encryption on the WiFi sniffing data and the Bluetooth sniffing data before uploading them to the cloud processing platform. The WiFi sniffing data includes device identification information, received signal strength (RSSI), channel or frequency band information, frame type, and timing information; the Bluetooth sniffing data includes device identification information and received signal strength (RSSI).

[0011] Optionally, in the aforementioned method for real-time positioning and monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing, the cloud processing platform uniformly accesses and synchronizes the sniffing data from multiple terminals. Through a multi-mode heterogeneous data fusion and association mechanism, it matches WiFi sniffing data and Bluetooth sniffing data from the same mobile device, and combines multi-point observation and multi-frequency signal characteristics to complete location estimation, outputting the positioning result of the mobile device. The positioning result includes real-time coordinates and trajectory information, specifically including: During the data acquisition phase, the cloud processing platform performs identity authentication and access control on the distributed sniffing terminals, verifies the integrity of the reported data and decrypts it, and writes the data into a scalable streaming processing link to adapt to the high-throughput data requirements generated by multiple terminals reporting simultaneously. In the data fusion stage, the cloud processing platform corrects the clock deviation of different distributed sniffing terminals, so that sniffing data from different terminals are fused under a unified time reference, and matches and fuses WiFi sniffing data and Bluetooth sniffing data from the same mobile device to generate a fused observation sequence for the same device object. In the location estimation stage, the cloud processing platform performs location estimation based on the fused observation sequence and uses an improved multilateral positioning algorithm to calculate the real-time coordinates and trajectory information of the mobile device.

[0012] Optionally, the method for real-time positioning and monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing, wherein the step of using an improved multilateral positioning algorithm to calculate the real-time coordinates and trajectory information of the mobile device specifically includes: The RSSI to distance conversion is performed using a logarithmic distance path loss model; When the reference distance is taken At that time, the relationship between RSSI and distance is: ; in, This represents the reference received power at a reference distance of 1m. This represents the path loss index. This indicates the distance from the distributed sniffing terminal to the mobile device. Indicates a distance of The received signal strength value; The distance from the distributed sniffing terminal to the mobile device for: ; When reference distance Not 1 m At that time, the relationship between RSSI and distance is: ; in, RSSI ( d 0) indicates that at a distance of The received signal strength value; If there is The nth distributed sniffing terminal, known to be the nth i The coordinates of the distributed sniffing terminals are The distance from the distributed sniffing terminal to the mobile device, calculated using RSSI, is: The location of the mobile device is When there is no error, the following conditions must be met: ; Using the linear least squares method, subtracting the quadratic terms pairwise from the circle equation yields the linear equation: ; The obtained linear equations are transformed into matrix form and solved to obtain the real-time coordinates and trajectory information of the mobile device.

[0013] Optionally, in the aforementioned method for real-time location monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing, the cloud processing platform generates a dynamic heatmap based on the location results of multiple mobile devices and displays it on the user interface to support historical trajectory queries and regional population density statistical analysis. Specifically: The cloud processing platform performs spatiotemporal aggregation and statistical analysis based on the positioning results of multiple mobile devices, generates dynamic heat maps for display on the user interface, and supports historical trajectory queries and regional population density statistical analysis.

[0014] In addition, to achieve the above objectives, the present invention also provides a real-time positioning monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing, wherein the real-time positioning monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing includes: one or more distributed sniffing terminals and a cloud processing platform. The distributed sniffing terminal is used to collect sniffing data from surrounding mobile devices in monitoring mode, and encrypt the sniffing data before uploading it to the cloud processing platform. The sniffing data includes identification information, signal strength, and channel-related information. The cloud processing platform is used to uniformly access and synchronize the sniffing data from multiple terminals. Through a multi-mode heterogeneous data fusion and association mechanism, it matches WiFi sniffing data and Bluetooth sniffing data from the same mobile device, and combines multi-point observation and multi-frequency signal characteristics to complete location estimation and output the positioning result of the mobile device. The positioning result includes real-time coordinates and trajectory information. The cloud processing platform is also used to generate dynamic heat maps based on the positioning results of multiple mobile devices and display them on the user interface to support historical trajectory queries and regional population density statistical analysis.

[0015] Optionally, in the aforementioned real-time location monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing, the distributed sniffing terminal includes: The computing module is used to filter, format and package data from different sniffing modules, assign a local timestamp and terminal identifier to each sniffed data, perform lightweight preprocessing on the data, and encrypt the data. An expansion interface is provided for connecting WiFi sniffing modules, Bluetooth sniffing modules, and communication modules. WiFi sniffing module is used to capture wireless frames generated by surrounding mobile devices and extract WiFi sniffing data for positioning. Bluetooth sniffing module, used to scan BLE broadcast signals and acquire Bluetooth sniffing data for supplementary positioning; The communication module is used to process the collected raw or semi-structured sniffing data through security mechanisms and then upload it to the cloud processing platform in real time.

[0016] Optionally, in the aforementioned real-time location monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing, the WiFi sniffing data includes device identification information, received signal strength RSSI, channel or frequency band information, frame type, and timing information; the Bluetooth sniffing data includes device identification information and received signal strength RSSI.

[0017] Optionally, the real-time positioning monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing further includes: The terminal management and configuration module is used for terminal registration and authentication, key and certificate management, remote parameter sending, health status monitoring, and upgrade maintenance.

[0018] In this invention, a distributed sniffing terminal, in monitoring mode, collects sniffing data from surrounding mobile devices and encrypts the sniffing data before uploading it to a cloud processing platform. The sniffing data includes identification information, signal strength, and channel-related information. The cloud processing platform performs unified access and time synchronization of the sniffing data from multiple terminals. Through a multi-mode heterogeneous data fusion and association mechanism, it matches WiFi sniffing data and Bluetooth sniffing data from the same mobile device, and combines multi-point observation and multi-frequency signal characteristics to complete location estimation, outputting the positioning result of the mobile device. The positioning result includes real-time coordinates and trajectory information. Based on the positioning results of multiple mobile devices, the cloud processing platform generates a dynamic heatmap and displays it on the user interface to support historical trajectory queries and regional population density statistical analysis. This invention improves device recognition rate and positioning stability through multi-mode fusion of dual-band WiFi and Bluetooth collaborative sniffing; reduces bottlenecks on the acquisition side through high-density parallel sniffing and high-speed data channels, enabling continuous capture capability in high-concurrency scenarios; and decouples acquisition and computation through an edge-cloud collaborative architecture, with real-time processing and unified visualization centrally completed in the cloud, thereby achieving wide-coverage, low-latency real-time positioning monitoring of mobile devices under large-scale deployment conditions, and providing dynamic thermal display and trajectory analysis. Attached Figure Description

[0019] Figure 1 This is a structural diagram of a preferred embodiment of the real-time positioning and monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing of the present invention; Figure 2 This is a schematic diagram of the distributed sniffing terminal composition structure in a preferred embodiment of the real-time positioning and monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing of the present invention. Figure 3 This is a flowchart of a preferred embodiment of the real-time positioning and monitoring method for mobile devices based on multi-mode wireless sniffing and cloud computing of the present invention; Figure 4This is a software processing flowchart in a preferred embodiment of the real-time positioning and monitoring method for mobile devices based on multi-mode wireless sniffing and cloud computing of the present invention. Figure 5 This is a schematic diagram of the principle of the multilateral positioning algorithm in a preferred embodiment of the real-time positioning and monitoring method for mobile devices based on multi-mode wireless sniffing and cloud computing of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, 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 of the invention and are not intended to limit the invention.

[0021] This invention relates to the field of real-time positioning and monitoring technology for mobile devices in integrated indoor and outdoor scenarios. Addressing the problems of unstable positioning accuracy, limited coverage, high deployment and maintenance costs, and difficulty in real-time processing of massive amounts of sniffed data in complex environments, existing single WiFi positioning or Bluetooth beacon solutions propose a real-time positioning and monitoring system and method for mobile devices based on multi-mode wireless sniffing and cloud computing. This system enables high-concurrency, real-time, and wide-coverage mobile device sniffing, positioning, and thermal visualization.

[0022] This invention aims to enable high-concurrency sniffing and data collection, real-time location calculation, and thermal visualization of mobile devices in high-density scenarios involving people and terminals. The invention adopts an edge-cloud collaborative architecture, decoupling sniffing and data collection from location analysis. Multiple distributed sniffing terminals collect WiFi and Bluetooth signals from mobile devices in real time, which are then uploaded to a cloud processing platform via cellular networks for multilateral location calculation. Finally, a dynamic heat map is generated and displayed on the user interface, thus maintaining real-time performance and scalability under large-scale deployment conditions.

[0023] The preferred embodiment of the present invention describes a real-time location monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing, such as... Figure 1 As shown, the real-time location monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing includes one or more distributed sniffing terminals (e.g., Figure 1 The system includes a sniffing terminal 1, a sniffing terminal 2, and a sniffing terminal 3, and a cloud processing platform. The distributed sniffing terminals are deployed on different floors or in different areas according to coverage requirements. The cloud processing platform can be deployed in a centralized or distributed manner and provides functional modules such as data access, streaming processing, positioning engine, storage, and visualization services.

[0024] The distributed sniffing terminal is used to collect sniffing data from surrounding mobile devices in monitoring mode, and encrypts the sniffing data before uploading it to the cloud processing platform. The sniffing data includes identification information, signal strength, and channel-related information. The cloud processing platform is used to uniformly access and synchronize the sniffing data from multiple terminals. Through a multi-mode heterogeneous data fusion and association mechanism, it matches WiFi sniffing data and Bluetooth sniffing data from the same mobile device, and combines multi-point observation and multi-frequency signal characteristics to complete location estimation and output the positioning result of the mobile device. The positioning result includes real-time coordinates and trajectory information. The cloud processing platform is also used to generate a dynamic heat map based on the positioning results of multiple mobile devices and display it on the user interface to support historical trajectory query and regional population density statistical analysis.

[0025] like Figure 2 As shown, the distributed sniffing terminal includes: Calculation module (i.e.) Figure 2 The CM4 computing module (main controller) is used to filter, format and package data from different sniffing modules, assign a local timestamp and terminal identifier to each sniffed data, perform lightweight preprocessing on the data, and encrypt the data. Extended interfaces (e.g.) Figure 2 The USB 3.0 Hub (also known as a USB 3.0 hub) is used to connect WiFi sniffing modules, Bluetooth sniffing modules, and communication modules. The WiFi sniffing module (dual-band 2.4 / 5GHz, i.e., supports 2.4GHz and 5GHz frequency bands and works in listening mode) is used to capture wireless data frames generated by surrounding mobile devices and extract WiFi sniffing data for positioning. The WiFi sniffing data includes device identification information, received signal strength RSSI, channel or frequency band information, frame type and timing information. A Bluetooth sniffing module (dual-band 2.4 / 5GHz) is used to scan BLE broadcast signals and acquire Bluetooth sniffing data for supplementary positioning. The Bluetooth sniffing data includes device identification information and received signal strength RSSI. Communication modules (e.g.) Figure 2 The distributed sniffing terminal uses a 4G / 5G communication module (or a wide-area backhaul link with equivalent functionality) to process the collected raw or semi-structured sniffing data through security mechanisms and upload it to the cloud processing platform in real time. In case of network anomalies, the distributed sniffing terminal may optionally have local caching and breakpoint resume capabilities to improve the overall reliability of the system.

[0026] Furthermore, to facilitate large-scale deployment and unified operation and maintenance, the real-time location monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing also includes: The terminal management and configuration module is used for terminal registration and authentication, key and certificate management, remote parameter transmission, health status monitoring, and upgrade maintenance. This module elevates the distributed sniffing terminals and cloud processing platform from a simple, loosely combined unit into a scalable, controllable, and sustainably evolving integrated system. In general, the terminal management section controls and manages multiple edge terminals, the configuration module sets basic parameters and user functions, and the distributed sniffing terminals typically function as peripherals to collect data. The cloud processing platform includes components such as the terminal management and configuration module, and also handles data processing.

[0027] The present invention provides a method for real-time location monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing, such as... Figure 3 As shown, the real-time location monitoring method for mobile devices based on multi-mode wireless sniffing and cloud computing is applied to a real-time location monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing. The real-time location monitoring method for mobile devices based on multi-mode wireless sniffing and cloud computing includes the following steps: Step S10: In monitoring mode, the distributed sniffing terminal collects sniffing data from surrounding mobile devices, encrypts the sniffing data, and uploads it to the cloud processing platform. The sniffing data includes identification information, signal strength, and channel-related information.

[0028] Specifically, in monitoring mode, the WiFi sniffing module captures wireless frames generated by surrounding mobile devices and extracts WiFi sniffing data for positioning; the Bluetooth sniffing module scans BLE (Bluetooth Low Energy) broadcast signals and obtains Bluetooth sniffing data for supplementary positioning; the computing module preprocesses and encrypts the WiFi sniffing data and the Bluetooth sniffing data before uploading them to the cloud processing platform; wherein, the WiFi sniffing data includes device identification information, Received Signal Strength Indication (RSSI), channel or frequency band information, frame type and timing information; the Bluetooth sniffing data includes device identification information and Received Signal Strength Indication (RSSI).

[0029] like Figure 4As shown, the software side of the distributed sniffing terminal uses "collection-lightweight processing-secure upload" as its main workflow. Its core objective is to ensure that data continuously, stably, and with low latency enters the cloud processing system under high-concurrency scenarios. The CM4 computing module of the distributed sniffing terminal filters, uniformly formats, and packages data from different sniffing modules, assigning a local timestamp and terminal identifier to each sniffed data. Then, necessary lightweight preprocessing is performed (for example, in actual collection, multiple sniffed data may be received at the same time; in this case, the data processing stage can extract the same timestamp and package them, so that multiple data at a certain time point only need to occupy the size of one timestamp byte, instead of retaining the timestamp for each data). Then, the data is encrypted, and finally, the encrypted data is uploaded to the cloud processing platform through an encrypted communication link. To avoid the terminal-side computing power becoming a system bottleneck, this invention centralizes the main computing tasks such as location fusion computing and thermal aggregation in the cloud, keeping the terminal side lightweight, thereby supporting large-scale deployment and unified management.

[0030] Typically, the sniffing data collected should contain multiple fields, as shown in Table 1: Table 1: Sniffing data containing multiple fields

[0031] However, in actual data collection, sometimes fields are missing, meaning that not all entries contain all fields. Therefore, filtering is required to remove useless entry data.

[0032] Step S20: The cloud processing platform performs unified access and time synchronization of the sniffing data from multiple terminals. Through a multi-mode heterogeneous data fusion and association mechanism, it matches WiFi sniffing data and Bluetooth sniffing data from the same mobile device, and combines multi-point observation and multi-frequency signal characteristics to complete location estimation and output the positioning result of the mobile device. The positioning result includes real-time coordinates and trajectory information.

[0033] The cloud processing platform handles concurrent data streams uploaded from multiple terminals, sequentially completing key steps such as cloud reception, data parsing and fusion, multi-sided positioning calculation, and heatmap generation (e.g., ...). Figure 4 (As shown).

[0034] Specifically, during the data acquisition phase, the cloud processing platform performs identity authentication and access control on the distributed sniffing terminals, verifies the integrity of the reported data and decrypts it, and writes the data into a scalable streaming processing link to adapt to the high-throughput data requirements generated by multiple terminals reporting simultaneously.

[0035] In the data fusion stage, the cloud processing platform corrects the clock deviation of different distributed sniffing terminals, so that sniffing data from different terminals are fused under a unified time reference, thereby providing a reliable time-consistency basis for subsequent multi-point positioning.

[0036] In the data fusion stage, the cloud processing platform also needs to match and fuse WiFi sniffing data and Bluetooth sniffing data from the same mobile device to generate a fused observation sequence for the same device object. This association process can comprehensively utilize constraints such as time proximity, observation consistency, signal strength change patterns, multi-band feature consistency, and terminal deployment information to improve association accuracy and provide more stable input for positioning calculation.

[0037] In the location estimation stage, the cloud processing platform performs location estimation based on the fused observation sequence, employing an improved multilateral positioning algorithm to calculate the real-time coordinates and trajectory information of the mobile device. The improved multilateral positioning algorithm uses multi-band WiFi signal strength information to enhance robustness to multipath and occlusion environments, and combines Bluetooth observations as auxiliary calibration or constraint information to enhance positioning stability and usability (e.g., Figure 5 (As shown).

[0038] Regarding the multi-location algorithm, the basic process is "RSSI → distance estimation → multi-location → least squares solution under error". The core idea is to measure the RSSI of the same mobile device at multiple sniffing terminals with known coordinates, and then convert the RSSI into a distance estimate from the terminal to the device using a path loss model. Then, distance estimation is performed with each sniffing terminal as the center. Ideally, multiple circles should intersect at a single point, which represents the estimated location of the mobile device. However, in real-world scenarios, RSSI measurements are affected by multipath propagation, occlusion, and non-line-of-sight (NLoS) conditions, leading to errors in distance estimation. The circles often do not intersect at a single point, but rather form convergence zones or remain completely separate. In such cases, optimization methods such as least squares are typically used to find the optimal location estimate that best meets all distance constraints. The derivation process is described below (the algorithm mainly consists of three parts: the RSSI to distance conversion formula, the geometric model for multi-directional positioning, and the least squares solution when errors exist): First, the RSSI to distance conversion is performed using the logarithmic distance path loss model; When the reference distance is taken At that time, the relationship between RSSI and distance is: ; in, This represents the reference received power (in dBm) at a reference distance of 1m. This represents the path loss index (which varies depending on the environment, taking different values ​​in open, indoor, and complex scenarios). This represents the distance (in meters) from the distributed sniffing terminal to the mobile device. Indicates a distance of The received signal strength value; The distance from the distributed sniffing terminal to the mobile device for: .

[0039] When reference distance Not 1 m At that time, the relationship between RSSI and distance is: ; in, RSSI ( d 0) indicates that at a distance of The received signal strength value.

[0040] Secondly, there is the geometric model for variable positioning, if there is The nth distributed sniffing terminal, known to be the nth i The coordinates of the distributed sniffing terminals are The distance from the distributed sniffing terminal to the mobile device, calculated using RSSI, is: The location of the mobile device is When there is no error, the following conditions must be met: ; In general, it refers to sniffing terminals. i Estimate the distance with the center of the circle as the center. The effect of multiple circles stacked with a radius (such as...) Figure 5 ).

[0041] Finally, using the linear least squares method, we subtract the quadratic terms pairwise from the circle equation to obtain the linear equation: ; The obtained linear equations are transformed into matrix form and solved to obtain the real-time coordinates and trajectory information of the mobile device.

[0042] Step S30: The cloud processing platform generates a dynamic heat map based on the positioning results of multiple mobile devices and displays it on the user interface to support historical trajectory query and regional population density statistical analysis.

[0043] Specifically, the cloud processing platform performs spatiotemporal aggregation and statistical analysis based on the positioning results of multiple mobile devices, generates dynamic heat maps for display on the user interface, and supports historical trajectory queries and regional population density statistical analysis, thereby forming a closed-loop output capability of "real-time positioning - heat map display - historical backtracking".

[0044] The system of this invention includes a distributed sniffing terminal and a cloud processing platform. The distributed sniffing terminal, with a computing module at its core, connects to at least two dual-band WiFi sniffing modules and a Bluetooth sniffing module via a high-speed expansion interface, and is configured with a cellular communication module. Each wireless sniffing module collects sniffing data such as identification information, signal strength, and channel-related information of surrounding mobile devices in monitoring mode. After encryption, this data is uploaded to the cloud processing platform in real time by the communication module. The cloud processing platform performs unified access and time synchronization of data reported by multiple terminals. Through a multi-mode heterogeneous data fusion and correlation mechanism, it matches WiFi and Bluetooth observations from the same mobile device, further combining multi-point observations and multi-band signal characteristics to complete location estimation, and outputs the real-time coordinates and trajectory information of the mobile devices. Based on the positioning results of a large number of mobile devices, the cloud processing platform generates a dynamic heat map and supports historical trajectory queries and regional population density statistical analysis.

[0045] This invention improves device recognition rate and positioning stability through multi-mode fusion of dual-band WiFi and Bluetooth collaborative sniffing; reduces bottlenecks on the acquisition side through high-density parallel sniffing and high-speed data channels, enabling continuous capture capability in high-concurrency scenarios; and decouples acquisition and computation through an edge-cloud collaborative architecture, with real-time processing and unified visualization centrally completed in the cloud, thereby achieving wide-coverage, low-latency real-time positioning monitoring of mobile devices under large-scale deployment conditions, and providing overall technical effects such as dynamic thermal display and trajectory analysis.

[0046] This invention utilizes a collaborative end-to-cloud system—a distributed multi-mode wireless sniffing terminal and a centralized cloud computing platform—to continuously collect wireless signals from mobile devices in indoor or semi-open spaces. High-throughput real-time processing and location analysis are then performed in the cloud, enabling stable real-time location results even under conditions of high-density crowds and multiple concurrent terminals. Simultaneously, it generates dynamic heatmaps and supports historical trajectory and regional density analysis. Compared to traditional solutions relying on a single WiFi or Bluetooth connection, this invention offers greater engineering feasibility in terms of coverage, concurrency capacity, real-time performance, and location stability, making it particularly suitable for continuous service scenarios requiring real-time monitoring, thermal visualization, and trajectory backtracking.

[0047] Beyond the overall effect, several key technical features of this invention directly bring attributable technical benefits. First, the sniffing terminal employs a multi-mode heterogeneous collaborative sniffing mechanism combining dual-band WiFi and Bluetooth, enabling the same mobile device to obtain redundant observations from different wireless modes and frequency bands within the same time window. Since indoor multipath propagation and obstruction can cause significant fluctuations in RSSI for a single frequency band, dual-band observation can statistically reduce the impact of single-frequency distortion on positioning, improving observation stability. Meanwhile, Bluetooth observation plays a supplementary identification and auxiliary constraint role in close-range or localized areas, which can be used to enhance device association reliability or assist in calibrating WiFi positioning results, thereby improving the continuity and robustness of positioning results and reducing the probability of jumps and drifts. These effects are technical gains directly brought about by multi-mode fusion and can improve the overall availability of the system in complex indoor environments.

[0048] Secondly, the sniffing terminal enables parallel access of multiple sniffing modules via a USB 3.0 hub or PCIe expansion, allowing multiple wireless modules to cover different frequency bands, channels, or scanning strategies in parallel, thereby improving the ability to capture effective wireless frames per unit time. In high-density terminal scenarios, passive sniffing data is characterized by high frequency and high redundancy. Insufficient throughput of the acquisition link can easily lead to missed captures, queuing, and latency accumulation, ultimately affecting the real-time performance of positioning and the accuracy of thermal statistics. This invention reduces the bottleneck on the acquisition side through high-speed bus and parallel sniffing of multiple modules, improves observation coverage and temporal continuity, and enables the cloud positioning engine to obtain more sufficient and uniform multi-point observation input, thus providing a more reliable data foundation for real-time positioning and thermal aggregation.

[0049] This invention employs an edge-cloud collaborative real-time processing architecture, limiting terminal-side functions to data acquisition, lightweight preprocessing, and secure uploading, while centralizing computationally intensive tasks such as data synchronization, cross-terminal correlation, fusion positioning, and thermal / trajectory analysis in the cloud. This design allows the sniffing terminal to avoid the computational and maintenance burdens associated with complex fusion calculations, thereby improving the stability of terminal deployment and reducing the impact of single-point failures on system performance. Simultaneously, the cloud processing platform can be scaled up on demand using scalable streaming processing, maintaining controllable throughput and latency as the number of sniffing terminals and the scale of target devices increase, and facilitating unified management, unified algorithm iteration, and unified policy upgrades. Therefore, this architecture directly enhances the system's real-time processing capabilities, operational consistency, and scalability under large-scale deployments.

[0050] Furthermore, this invention introduces a timestamp synchronization and cross-terminal alignment mechanism in the cloud, and completes the observation association of the same device based on multimodal data fusion. This allows observations from different sniffing terminals to be correctly aggregated under a unified time reference, reducing the risk of mislocation caused by incorrect association and mismatch. Based on this fused observation sequence, this invention employs an improved multilateral positioning algorithm for real-time coordinate estimation, and can utilize multi-band information and Bluetooth observations as auxiliary constraints, thereby maintaining good positioning stability and availability even under conditions of multipath, occlusion, and non-line-of-sight. Finally, the cloud performs spatial rasterization and time window aggregation based on the real-time coordinates of a large number of devices, enabling the output of dynamic heatmaps and supporting historical trajectory queries and regional density analysis. This forms a continuous output capability that can be used for operational analysis, security linkage, and emergency decision-making, enhancing the overall application value of the system.

[0051] Furthermore, the core idea of ​​this invention is to obtain redundant observations through multi-mode wireless sniffing and perform centralized fusion calculations in the cloud. Therefore, without deviating from the overall idea of ​​"multi-mode sniffing acquisition + cloud synchronous correlation positioning + thermal / trajectory output", there can be various implementation methods, alternative structures and equivalent modifications, all of which should fall within the scope of this invention.

[0052] Regarding the hardware implementation of the sniffing terminal, the core computing module is not limited to the Raspberry Pi CM4; other ARM platforms, x86 industrial PCs, edge gateways, or integrated SoC boards with comparable processing power can also be used. The access method for the wireless sniffing module is not limited to USB 3.0 or PCIe; it can be replaced with M.2, miniPCIe, or onboard multi-wireless chip solutions. Integrated multi-wireless transceiver boards can also be used to achieve multi-mode sniffing. The number of WiFi sniffing modules can be expanded according to coverage and throughput requirements, not limited to two, but can also be three or more to cover more channels or frequency bands in parallel. In addition to 2.4GHz and 5GHz, WiFi frequency bands can be expanded to new frequency bands such as 6GHz to enhance coverage and anti-interference capabilities. One or more Bluetooth sniffing modules can also be used to improve local identification and supplementary observation capabilities. Antenna types can include external antennas, directional antennas, or array antennas, and installation methods can include wall mounting, ceiling mounting, or surface mounting to adapt to the coverage requirements of different scenarios. To improve reliability, sniffing terminals can add local caching and breakpoint resume mechanisms, which can buffer briefly when the network is unstable and complete the retransmission after the link is restored, so as to avoid the loss of critical data.

[0053] Regarding data backhaul and security mechanisms, in addition to using 4G / 5G communication modules, this invention can also employ Ethernet, PoE Ethernet, enterprise private networks, WiFi backhaul, or multi-link redundancy to achieve data upload, and can strategically switch links based on link quality. Secure data transmission can be achieved through various equivalent methods, including but not limited to secure channel transmission, terminal authentication, data encryption, and integrity verification mechanisms. While meeting privacy and compliance requirements, device identifiers can be anonymized, hashed, or segmented on the terminal or cloud side, and a periodic update strategy can be adopted to reduce long-term traceability risks while maintaining the necessary correlation capabilities for location and heat map statistics. To reduce bandwidth consumption, the terminal side can optionally perform compression, batch packaging, differential reporting, or aggregation reporting by time window, with the cloud correspondingly performing unpacking and restoration processing, all achieving the same purpose and effect as this invention.

[0054] Regarding sniffing and data collection strategies, WiFi monitoring can employ fixed-channel monitoring, polling scanning, or a modular, group-based approach. This means different sniffing modules operate on different channels / frequency bands, or switch channels according to rules to balance coverage and capture efficiency. The content collected can also be limited based on business and compliance requirements. For example, only timestamps, RSSI, and channel / frequency band information necessary for positioning can be collected, or, provided the hardware supports it and compliance is met, richer physical layer features can be collected to enhance positioning. The degree of lightweight preprocessing on the terminal side can also be varied: in some implementations, the terminal only performs formatting and encrypted uploads; in others, the terminal can perform stronger initial deduplication, quality assessment, and noise filtering to reduce the cloud burden. These differences do not change the basic technical concept of this invention.

[0055] Regarding cloud-based association, positioning, and analysis algorithms, the time synchronization described in this invention can be achieved through network clock synchronization, cloud-based statistical correction, or a combination of these methods to improve cross-terminal alignment accuracy. Cross-modal association can employ rule-based methods based on time windows and observation consistency, or use probabilistic or learning models for association determination, all of which can achieve fusion of WiFi and Bluetooth observations for the same device. In terms of positioning algorithms, the "improved multilateral positioning algorithm" described in this invention can be implemented in different ways, such as weighted centroid or constraint optimization based on RSSI, multi-point regression estimation, filtering and smoothing combined with trajectory continuity constraints, or feasible region estimation combined with field map constraints. For multi-modal fusion strategies, different weights or calibration mechanisms can be set for dual-band WiFi and Bluetooth observations according to the scenario to enhance robustness and stability. Heat and trajectory generation can also be implemented in different ways, including rasterized aggregation, partitioned statistics, kernel density estimation, or map topology-based aggregation, and can achieve real-time heat and historical playback according to different time windows, all of which are equivalent methods to achieve the output objectives of this invention.

[0056] Regarding system output and integration, this invention can provide two-dimensional planar or floor-by-floor thermal displays, and can also output time-series thermal changes, regional density statistics, abnormal clustering, or restricted area alarms. External interfaces can utilize HTTP, message subscription, WebSocket push, or other equivalent communication methods to interface with security, operations, or third-party analysis systems, thereby meeting the integration needs of different business systems. All the aforementioned alternatives and variations can achieve the multi-mode sniffing, cloud-based fusion positioning, and thermal / trajectory output objectives described in this invention, and fall within the scope of this invention.

[0057] From an overall design perspective, the main objective of this invention is to enable comprehensive data collection, stable transmission, easy deployment, and convenient expansion in high-density scenarios. This requires making the sniffing capabilities into stackable modules, aggregating the data from multiple modules using sufficiently high-throughput hardware interconnects, and then stably sending the data out through independent backhaul links. Around this direction, there are various overall alternatives or variations, and competitors may implement "equivalent" solutions along these lines.

[0058] The first type of overall alternative solution is to change the "multiple sniffing modules + high-speed bus aggregation" structure from "external module parallel connection" to "internal integrated parallel connection". This means that instead of using a USB 3.0 hub / PCIe expansion to connect multiple independent sniffing modules, a single-board card or multi-chip integrated solution with multiple wireless RF channels can be used: multiple WiFi RF / baseband chips or multiple receiving links are directly placed on the same motherboard, and data aggregation is completed through high-speed interconnects within the board (e.g., onboard PCIe channels, SoC internal buses, or even FPGA / bridge chips). This implementation still achieves "parallel sniffing + high throughput", but the obvious structural features such as "USB 3.0 hub" or "external sniffing module" are no longer visible, representing a typical structural bypass. Therefore, the modified solution can specify that the parallel aggregation component can be not only a USB 3.0 hub, but also an onboard high-speed switching / bridging / aggregation circuit, or directly through multiple high-speed interfaces of the main control platform to achieve parallel access to multiple wireless transceiver units.

[0059] The second type of overall variation replaces the parallel approach of "at least two dual-band WiFi sniffing modules" with "parallel reception of a single module across multiple channels / antennas." This method might use a higher-specification WiFi hardware (e.g., a scheme with multiple receiver chains capable of simultaneous sampling on different channels / frequency bands, or a software-defined radio / wideband receiver front-end) to claim it doesn't use "multiple modules in parallel," thus circumventing the structural limitations of "multiple sniffing modules." Alternatively, it might employ a single network card but use a more aggressive scanning strategy to increase the capture rate, weakening the necessity of "parallel modules." Regarding this alternative approach, the description of the variation of this invention clarifies that multi-module parallelism and single-module multi-channel parallelism are equivalent structures for achieving "parallel sniffing / increasing capture rate." The key is not the number of modules, but the terminal's ability to operate multiple independent sniffing and receiving channels in parallel.

[0060] The third type of overall modification replaces the multi-mode combination of "dual-band WiFi + Bluetooth collaboration" with a combination of "multi-band WiFi (including 6GHz)" or "WiFi + other short-range radio frequencies." This means abandoning BLE sniffing and instead using WiFi 2.4 / 5 / 6GHz multi-band or introducing UWB, RFID, or even cellular signals to enhance identification and positioning stability, thus circumventing the structural limitations of the "Bluetooth sniffing module." Therefore, in the description of the modified solution, the "Bluetooth sniffing module" can be upgraded to a "short-range wireless sniffing module" or a "second wireless sniffing module," listing BLE as just one preferred implementation, while explaining its role in providing supplementary observation and enhanced identification / constraint capabilities. This is more conducive to covering situations where BLE is replaced by other short-range radio frequencies.

[0061] The fourth category of overall modifications focuses on "backhaul link and system deployment configuration." While 4G / 5G backhaul is currently used, it can be replaced by Ethernet, PoE, WiFi backhaul, Mesh self-organizing networks, wired fiber optics, or private networks, thus replacing the "independent cellular communication module." Furthermore, the backhaul can be made into a pluggable module (e.g., an M.2 cellular card / network port module option) to circumvent the limitation of this invention on "4G / 5G modules." Therefore, in the description of the modified scheme, the concept of the backhaul module can be abstracted as a "wide-area communication backhaul module or network interface module," emphasizing its key role of providing an independent and stable data uplink channel, with the specific medium being replaceable.

[0062] The fifth type of overall variation is a circumvention of "modularity" itself: by making all wireless modules non-pluggable onboard integrated units, the visual characteristics of "modular design" are reduced, or modularity is moved from electrical connections to the structural mounting layer (e.g., replaceable antenna arrays, replaceable RF front-ends), thus maintaining maintainability while circumventing the description of "pluggable sniffing modules." In this variation, modularity can be described as a structural concept of "replaceable / expandable functional units," including both electrically pluggable modules and onboard but replaceable daughterboards, RF front-end components, antenna components, etc., emphasizing that the purpose of modularity is to expand parallel sniffing capabilities and reduce maintenance costs.

[0063] The sixth overall alternative is to change the division from "lightweight terminal data collection and centralized cloud processing" to "centralized edge processing." Algorithmically, other solutions might aggregate data to a local edge server / gateway before uploading to the cloud, or simply complete positioning and thermal generation at the edge, uploading only the results. Structurally, they might add a "regional aggregation gateway," making the sniffing terminal a simpler RF front-end. This approach doesn't directly bypass the "parallel sniffing hardware structure" of the terminal in this invention, but it might bypass the "cloud platform" structural relationship mentioned in this invention. Therefore, in a modified solution, it can be described that the data processing device can be deployed on a cloud server, edge server, or local processing unit, and the sniffing terminal and processing device can be connected via network communication, thus avoiding tying the system structure to the "cloud."

[0064] In summary, this invention provides a method and system for real-time location monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing. The method includes: distributed sniffing terminals collecting sniffing data from surrounding mobile devices in monitoring mode, encrypting the sniffing data, and uploading it to a cloud processing platform. The sniffing data includes identification information, signal strength, and channel-related information. The cloud processing platform performs unified access and time synchronization of the sniffing data from multiple terminals. Through a multi-mode heterogeneous data fusion and association mechanism, it matches WiFi sniffing data and Bluetooth sniffing data from the same mobile device, and combines multi-point observation and multi-frequency signal characteristics to complete location estimation, outputting the location result of the mobile device. The location result includes real-time coordinates and trajectory information. Based on the location results of multiple mobile devices, the cloud processing platform generates a dynamic heat map and displays it on the user interface to support historical trajectory queries and regional population density statistical analysis. This invention improves device recognition rate and positioning stability through multi-mode fusion of dual-band WiFi and Bluetooth collaborative sniffing; reduces bottlenecks on the acquisition side through high-density parallel sniffing and high-speed data channels, enabling continuous capture capability in high-concurrency scenarios; and decouples acquisition and computation through an edge-cloud collaborative architecture, with real-time processing and unified visualization centrally completed in the cloud, thereby achieving wide-coverage, low-latency real-time positioning monitoring of mobile devices under large-scale deployment conditions, and providing dynamic thermal display and trajectory analysis.

[0065] 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, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0066] Of course, 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 (such as a processor, controller, etc.). The 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 computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0067] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for real-time positioning and monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing, characterized in that, The real-time location monitoring method for mobile devices based on multi-mode wireless sniffing and cloud computing is applied to a real-time location monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing. The real-time location monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing includes one or more distributed sniffing terminals and a cloud processing platform. The real-time location monitoring method for mobile devices based on multi-mode wireless sniffing and cloud computing includes: In monitoring mode, the distributed sniffing terminal collects sniffing data from surrounding mobile devices, encrypts the sniffing data, and uploads it to the cloud processing platform. The sniffing data includes identification information, signal strength, and channel-related information. The cloud processing platform performs unified access and time synchronization of the sniffing data from multiple terminals. Through a multi-mode heterogeneous data fusion and association mechanism, it matches WiFi sniffing data and Bluetooth sniffing data from the same mobile device, and combines multi-point observation and multi-frequency signal characteristics to complete location estimation and output the positioning result of the mobile device. The positioning result includes real-time coordinates and trajectory information. The cloud processing platform generates dynamic heatmaps based on the positioning results of multiple mobile devices and displays them on the user interface to support historical trajectory queries and regional population density statistical analysis.

2. The method for real-time positioning and monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing according to claim 1, characterized in that, The distributed sniffing terminal includes a WiFi sniffing module, a Bluetooth sniffing module, and a computing module; the sniffing data includes WiFi sniffing data and Bluetooth sniffing data.

3. The method for real-time positioning and monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing according to claim 2, characterized in that, In monitoring mode, the distributed sniffing terminal collects sniffing data from surrounding mobile devices, encrypts the sniffing data, and uploads it to the cloud processing platform, specifically including: In monitoring mode, the WiFi sniffing module captures wireless frames generated by surrounding mobile devices and extracts WiFi sniffing data for location purposes. The Bluetooth sniffing module scans the BLE broadcast signal and acquires Bluetooth sniffing data for supplementary positioning. The computing module performs data preprocessing and encryption on the WiFi sniffing data and the Bluetooth sniffing data before uploading them to the cloud processing platform. The WiFi sniffing data includes device identification information, received signal strength (RSSI), channel or frequency band information, frame type, and timing information; the Bluetooth sniffing data includes device identification information and received signal strength (RSSI).

4. The method for real-time positioning and monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing according to claim 3, characterized in that, The cloud processing platform performs unified access and time synchronization of the sniffing data from multiple terminals. Through a multi-mode heterogeneous data fusion and correlation mechanism, it matches WiFi sniffing data and Bluetooth sniffing data from the same mobile device, and combines multi-point observation and multi-frequency band signal characteristics to complete location estimation, outputting the positioning result of the mobile device. The positioning result includes real-time coordinates and trajectory information, specifically including: During the data acquisition phase, the cloud processing platform performs identity authentication and access control on the distributed sniffing terminals, verifies the integrity of the reported data and decrypts it, and writes the data into a scalable streaming processing link to adapt to the high-throughput data requirements generated by multiple terminals reporting simultaneously. In the data fusion stage, the cloud processing platform corrects the clock deviation of different distributed sniffing terminals, so that sniffing data from different terminals are fused under a unified time reference, and matches and fuses WiFi sniffing data and Bluetooth sniffing data from the same mobile device to generate a fused observation sequence for the same device object. In the location estimation stage, the cloud processing platform performs location estimation based on the fused observation sequence and uses an improved multilateral positioning algorithm to calculate the real-time coordinates and trajectory information of the mobile device.

5. The method for real-time positioning and monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing according to claim 4, characterized in that, The calculation of the real-time coordinates and trajectory information of the mobile device using the improved multilateral positioning algorithm specifically includes: The RSSI to distance conversion is performed using a logarithmic distance path loss model; When the reference distance is taken At that time, the relationship between RSSI and distance is as follows: ； in, This represents the reference received power at a reference distance of 1m. This represents the path loss index. This indicates the distance from the distributed sniffing terminal to the mobile device. Indicates a distance of The received signal strength value; The distance from the distributed sniffing terminal to the mobile device for: ； When reference distance Not 1 m At that time, the relationship between RSSI and distance is as follows: ； in, RSSI ( d 0) indicates that at a distance of The received signal strength value; If there is The nth distributed sniffing terminal, known to be the nth i The coordinates of the distributed sniffing terminals are The distance from the distributed sniffing terminal to the mobile device, calculated using RSSI, is: The location of the mobile device is When there is no error, the following conditions must be met: ； Using the linear least squares method, subtracting the quadratic terms pairwise from the circle equation yields the linear equation: ； The obtained linear equations are transformed into matrix form and solved to obtain the real-time coordinates and trajectory information of the mobile device.

6. The method for real-time positioning and monitoring of mobile devices based on multi-mode wireless sniffing and cloud computing according to claim 1, characterized in that, The cloud processing platform generates dynamic heatmaps based on the location results from multiple mobile devices and displays them on the user interface to support historical trajectory queries and regional population density statistical analysis, specifically: The cloud processing platform performs spatiotemporal aggregation and statistical analysis based on the positioning results of multiple mobile devices, generates dynamic heat maps for display on the user interface, and supports historical trajectory queries and regional population density statistical analysis.

7. A real-time positioning and monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing, characterized in that, The real-time positioning and monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing includes one or more distributed sniffing terminals and a cloud processing platform. The distributed sniffing terminal is used to collect sniffing data from surrounding mobile devices in monitoring mode, and encrypt the sniffing data before uploading it to the cloud processing platform. The sniffing data includes identification information, signal strength, and channel-related information. The cloud processing platform is used to uniformly access and synchronize the sniffing data from multiple terminals. Through a multi-mode heterogeneous data fusion and association mechanism, it matches WiFi sniffing data and Bluetooth sniffing data from the same mobile device, and combines multi-point observation and multi-frequency signal characteristics to complete location estimation and output the positioning result of the mobile device. The positioning result includes real-time coordinates and trajectory information. The cloud processing platform is also used to generate dynamic heat maps based on the positioning results of multiple mobile devices and display them on the user interface to support historical trajectory queries and regional population density statistical analysis.

8. The real-time positioning and monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing according to claim 7, characterized in that, The distributed sniffing terminal includes: The computing module is used to filter, format and package data from different sniffing modules, assign a local timestamp and terminal identifier to each sniffed data, perform lightweight preprocessing on the data, and encrypt the data. An expansion interface is provided for connecting WiFi sniffing modules, Bluetooth sniffing modules, and communication modules. WiFi sniffing module is used to capture wireless frames generated by surrounding mobile devices and extract WiFi sniffing data for positioning. Bluetooth sniffing module, used to scan BLE broadcast signals and acquire Bluetooth sniffing data for supplementary positioning; The communication module is used to process the collected raw or semi-structured sniffing data through security mechanisms and then upload it to the cloud processing platform in real time.

9. The real-time positioning and monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing according to claim 8, characterized in that, The WiFi sniffing data includes device identification information, received signal strength (RSSI), channel or frequency band information, frame type, and timing information; the Bluetooth sniffing data includes device identification information and received signal strength (RSSI).

10. The real-time positioning and monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing according to claim 7, characterized in that, The real-time location monitoring system for mobile devices based on multi-mode wireless sniffing and cloud computing also includes: The terminal management and configuration module is used for terminal registration and authentication, key and certificate management, remote parameter sending, health status monitoring, and upgrade maintenance.

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