A collaborative positioning system based on 5G communication network

Abstract

The present invention discloses a collaborative positioning system based on a 5G communication network, which relates to the field of mobile communication technology, including receiving multi-source data from 5G base stations, GPS satellites, Wi-Fi hotspots and Bluetooth beacons, and performing preliminary cleaning; transmitting data with precise timestamps to edge computing nodes, analyzing the user's movement pattern in real time, and predicting the user's possible next location; based on current environmental conditions, signal quality, and predicted user behavior, the system automatically selects the most appropriate positioning technology for positioning prediction; based on the automatically selected positioning prediction technology, generates the final positioning result, and feeds it back to the user device through the 5G network; continuously monitors the positioning effect and environmental changes, collects user feedback, and performs system optimization. Compared with single-technology positioning methods, the present invention has significant creativity and novelty in terms of signal processing efficiency, positioning accuracy, response speed, and energy consumption management.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technologies, and in particular to a collaborative positioning system based on a 5G communication network. Background Art

[0002] With the full commercialization of the fifth generation of mobile communication technology (5G), positioning technology in the field of wireless communications has ushered in unprecedented innovation opportunities. The high-speed data transmission capability of 5G networks has laid a solid foundation for the realization of high-precision, real-time positioning services. Traditional positioning technology mainly relies on the global navigation satellite system, but in indoor environments, urban canyons or complex electromagnetic environments, the signal is easily interfered, resulting in limited positioning accuracy and stability.

[0003] Although 5G networks have brought significant performance improvements to positioning technology, existing technologies still face certain challenges. Since the coverage and signal quality of 5G networks vary between different geographical regions, how to maintain high-precision positioning under various environmental conditions is an obstacle that is difficult to overcome with existing technologies. In addition, traditional positioning systems often lack a deep understanding of user behavior patterns, making it difficult to provide personalized and predictive positioning services. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a collaborative positioning system based on a 5G communication network to solve the problems of insufficient positioning accuracy and response speed caused by signal attenuation, multipath effect and time synchronization error in existing positioning technologies under changing environmental conditions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a collaborative positioning system based on a 5G communication network, which includes a multimodal data acquisition and preprocessing module: receiving multi-source data from 5G base stations, GPS satellites, Wi-Fi hotspots, and Bluetooth beacons, and performing preliminary cleaning;

[0008] High-precision time synchronization module: Utilizes the low latency characteristics of the 5G network to perform high-precision time synchronization with the base station and perform time stamping;

[0009] Real-time analysis module on edge computing nodes: transmits data with precise timestamps to edge computing nodes, analyzes user movement patterns in real time, and predicts the user's possible next location;

[0010] Intelligent switching and positioning prediction module: Based on the current environmental conditions, signal quality, and predicted user behavior, the system automatically selects the most appropriate positioning technology for positioning prediction;

[0011] Positioning result generation and feedback module: Based on the automatically selected positioning prediction technology, it generates the final positioning result and feeds it back to the user device through the 5G network;

[0012] Continuous monitoring and system optimization module: continuously monitors positioning effects and environmental changes, collects user feedback, and performs system optimization.

[0013] As a preferred solution of the collaborative positioning system based on the 5G communication network of the present invention, wherein: the receiving of multi-source data includes collecting signal strength and signal arrival time from the 5G base station;

[0014] Collect pseudorange and carrier phase data from GPS satellites;

[0015] Collect received signal strength indicators from Wi-Fi hotspots and Bluetooth beacons;

[0016] The system's built-in accelerometer and gyroscope sensors continuously monitor the device's motion status.

[0017] As a preferred solution of the collaborative positioning system based on the 5G communication network of the present invention, wherein: the preliminary cleaning includes using statistical methods to identify and remove data points that are obviously deviated from the normal range;

[0018] Evaluate signal quality based on signal strength and signal stability, and discard low-quality data;

[0019] Use interpolation methods to fill missing data.

[0020] As a preferred solution of the collaborative positioning system based on the 5G communication network of the present invention, wherein: utilizing the low latency characteristics of the 5G network to perform high-precision time synchronization with the base station and perform time marking includes the following steps:

[0021] Select PTP as the time synchronization protocol and initialize it;

[0022] The system starts exchanging PTP messages with the base station, including:

[0023] The base station, as the master clock, sends a Sync message containing the timestamp of the sending time;

[0024] After the base station sends the Sync message, it continues to send the Follow Up message containing the precise sending time;

[0025] The system acts as a slave clock and sends a Delay Request message to the base station to request delay information;

[0026] After receiving the Delay Request, the base station immediately sends a DelayResponse message containing the timestamps of receiving and sending;

[0027] Calculate the time offset between the system and the base station based on the timestamp of the PTP message;

[0028] After completing time synchronization, all collected data are time-stamped;

[0029] The system establishes a time-stamp database and stores each data point with an accurate time stamp in the time-stamp database.

[0030] As a preferred solution of the collaborative positioning system based on the 5G communication network of the present invention, wherein: transmitting data with precise timestamps to edge computing nodes, analyzing the user's movement pattern in real time, and predicting the user's possible next location include the following steps:

[0031] Extract data points with precise timestamps from the time database and upload them to the edge computing node;

[0032] The quality of the data points on the edge computing nodes is evaluated. The weights of different data sources are dynamically adjusted according to the signal strength, noise level, and time consistency to reflect the quality of different data sources. The expression is:

[0033]

[0034] Among them, w p represents the weight of the pth data source, σ p represents the noise level, σ avg represents the average noise level of all data sources, Δt p represents the time consistency error, Δt avg represents the average time consistency error of all data sources, σ q represents the noise standard deviation of the qth data source, and N represents the total number of data sources.

[0035] Convolutional neural networks are used to process the spatial distribution of signal strengths from different signal sources, and long short-term memory networks are used to capture the temporal changes of signals.

[0036] The edge node analyzes the user's mobility pattern in real time, uses the long short-term memory network model to capture the user's movement trajectory characteristics, and predicts the user's next location. The expression is:

[0037]

[0038] Among them, p next represents the predicted next position, t 0 represents the lower limit of integration at a time point, ta represents the integral upper limit of the time point, i represents the index of the historical position point, M represents the number of historical position points, and a i (t) represents the acceleration at the i-th historical position point changing with time t, t i represents the timestamp of the i-th historical location point, and τ represents the time window width.

[0039] As a preferred solution of the collaborative positioning system based on the 5G communication network of the present invention, the system automatically selects the most appropriate positioning technology for positioning prediction based on the current environmental conditions, signal quality, and predicted user behavior, including the following steps:

[0040] Considering the current environmental conditions, signal quality, and predicted user behavior, a comprehensive evaluation function is designed to evaluate the applicability of each positioning technology and select the most appropriate positioning technology for positioning prediction. The expression is:

[0041]

[0042] Among them, F(T) represents the comprehensive evaluation function for different positioning technologies, T represents the type of positioning technology, S k represents the strength of the kth signal, E k represents the degree of environmental influence on the k-th signal, η represents the environmental factor adjustment coefficient, β represents a constant, and P represents the modulus of the user behavior parameter vector.

[0043] As a preferred solution of the collaborative positioning system based on the 5G communication network of the present invention, generating the final positioning result based on the automatically selected positioning prediction technology and feeding it back to the user equipment through the 5G network includes the following steps:

[0044] Using the selected positioning technology, select the best positioning technology for prediction;

[0045] According to the results predicted by the optimal positioning technology and the current environmental conditions, the final positioning result is calculated, and the expression is:

[0046]

[0047] Among them, x final represents the final optimal positioning estimation result, η represents the environmental factor adjustment coefficient, and e represents the environmental vector;

[0048] Receive real-time positioning results from the intelligent positioning system, convert the positioning result information into a unified data format and encapsulate it into a data packet;

[0049] Compress the data packet using a data compression algorithm, and encrypt the compressed data packet using an advanced encryption standard;

[0050] Embed MD5 hash checksum in the encrypted data packet and establish a retransmission mechanism. If the receiving end detects data corruption, it will request to resend the damaged data packet through the reverse channel of the 5G network until the complete and intact data is received.

[0051] In 5G networks, dedicated low-latency channels are reserved for positioning data packets;

[0052] Send encrypted and compressed data packets via the high-speed data link layer protocol of the 5G network;

[0053] The location application on the user's device receives the data packet and decrypts the data packet using the shared key or private key;

[0054] Use MD5 checksum to verify the integrity of the data packet. If the data packet is damaged, a retransmission request is triggered.

[0055] Decompress the decrypted data packet and restore the original data structure;

[0056] The positioning application parses the data in JSON format, extracts the positioning results and predicted positions, and updates the user interface to display the current and predicted position information.

[0057] As a preferred solution of the collaborative positioning system based on the 5G communication network of the present invention, continuously monitoring the positioning effect and environmental changes, collecting user feedback, and optimizing the system include the following steps:

[0058] The system continuously collects real-time data streams and gathers user feedback through mobile app interfaces, email surveys, and social media listening channels;

[0059] Apply natural language processing technology to conduct sentiment analysis on user feedback and identify key factors of positive and negative reviews;

[0060] Use cluster analysis to identify the behavior patterns of user groups and understand the differences in system performance requirements of different user groups;

[0061] Develop adaptive filters to correct positioning errors;

[0062] Adjust the parameters of the positioning algorithm based on the results of real-time data analysis;

[0063] Adopt dynamic power control strategy to intelligently adjust the working state of sensors and processors according to the activity level of the device to extend battery life;

[0064] Based on user feedback and behavior pattern analysis, optimize user interface design, simplify operation processes, and improve user satisfaction.

[0065] The beneficial effects of the present invention are as follows: by integrating multi-source signals and performing data preprocessing, signal quality and data integrity are improved. High-precision time synchronization is achieved by utilizing the low latency characteristics of 5G, thereby enhancing positioning accuracy. Edge computing analyzes user mobility patterns in real time, dynamically adjusts data source weights based on signal strength and time consistency, and improves prediction accuracy. The intelligent switching mechanism automatically selects the best positioning technology based on environmental conditions and signal quality, ensuring the flexibility and reliability of positioning. Ultimately, the system efficiently feeds back positioning results through the 5G network, continuously optimizes, and uses natural language processing and cluster analysis to enhance user experience, achieving a comprehensive improvement in positioning accuracy, response speed, and user satisfaction, and is particularly suitable for complex and changing environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0067] Figure 1 This is a flow chart of the collaborative positioning system based on 5G communication network in Example 1.

[0068] Figure 2 This is a flow chart of high-precision time synchronization in Example 1. DETAILED DESCRIPTION

[0069] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0070] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0071] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0072] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a collaborative positioning system based on a 5G communication network, comprising the following steps:

[0073] S1. Receiving multi-source data includes collecting signal strength and signal arrival time from 5G base stations; collecting pseudorange and carrier phase data from GPS satellites; collecting received signal strength indications from Wi-Fi hotspots and Bluetooth beacons; and the system's built-in accelerometer and gyroscope sensors continuously monitor the device's motion status.

[0074] S2. Preliminary cleaning includes using statistical methods to identify and remove data points that are significantly deviated from the normal range; evaluating signal quality based on signal strength and signal stability, and eliminating low-quality data; and using interpolation methods to fill in missing data.

[0075] S3. Using the low latency characteristics of the 5G network, high-precision time synchronization with the base station and time stamping include the following steps:

[0076] PTP (Precision Time Protocol) is selected as the time synchronization protocol and initialized, including configuring PTP parameters such as master clock ID, domain number, priority, etc. to ensure compatibility with the PTP configuration of the base station; the system and the base station start PTP message exchange, including: the base station, as the master clock, sends a Sync message containing the timestamp of the transmission; after the base station sends the Sync message, it continues to send a Follow Up message containing the precise transmission time; the system, as the slave clock, sends a DelayRequest message to the base station to request delay information; after receiving the Delay Request, the base station immediately sends a Delay Response message containing the reception and transmission timestamps; based on the timestamp of the PTP message, the time offset between the system and the base station is calculated; after completing the time synchronization, all collected data are time-stamped; the system establishes a time-stamp database and stores each data point with a precise time stamp in the time-stamp database.

[0077] Furthermore, the expression for calculating the time offset between the system and the base station is:

[0078]

[0079] Where Δt is the time offset between the system and the base station, t 1 The timestamp of the base station sending the Sync message, t 2 The timestamp of the system receiving the Sync message, t 3 The timestamp when the system sends the Delay Request message, t 4 is the timestamp of the system receiving the DelayResponse message, δ f is the system clock frequency deviation factor, which indicates the frequency deviation between the system clock and the ideal clock, usually less than 1, γ nis the network jitter coefficient, which indicates the random variation of network transmission delay.

[0080] Furthermore, in order to further improve the accuracy and stability of time synchronization, the system will implement the following optimization strategies, including:

[0081] Multi-base station synchronization: The system exchanges PTP messages with multiple auxiliary base stations, reduces the impact of single point failures through cross-validation and averaging, and improves the redundancy and robustness of time synchronization;

[0082] Dynamic frequency adjustment: The system dynamically adjusts the frequency of PTP message exchange according to network conditions and positioning requirements. In highly dynamic environments or when positioning accuracy is extremely high, the message exchange frequency is increased to maintain higher time synchronization accuracy.

[0083] Adaptive correction mechanism: The system uses an adaptive correction algorithm to predict future offset trends based on historical time offset data, make fine adjustments in advance, and reduce the lag of time stamping.

[0084] Furthermore, after the data time stamping is completed, the system will perform a series of post-processing operations, including:

[0085] Timestamp verification: Verify the accuracy and consistency of timestamps by periodically comparing with base stations or other reliable time sources;

[0086] Outlier detection and correction: Use statistical methods to detect outliers in time stamps. For example, by calculating the distribution of time stamps, identify and correct those time stamps that are significantly deviated from the normal range to avoid adverse effects on positioning results.

[0087] Time series smoothing: Apply time series analysis techniques, such as moving average or exponential smoothing, to smooth the time-stamped series, reduce random fluctuations, and improve the stability of the time-stamps.

[0088] S4, passing the data with precise timestamps to the edge computing node, analyzing the user's movement pattern in real time, and predicting the user's possible next location includes the following steps:

[0089] Extract data points with precise timestamps from the time database and upload them to the edge computing node; perform quality assessment on the data points on the edge computing node, and dynamically adjust the weights of different data sources based on signal strength, noise level, and time consistency to reflect the quality of different data sources. The expression is:

[0090]

[0091] Among them, w p represents the weight of the pth data source, σ prepresents the noise level, σ avg represents the average noise level of all data sources, Δt p represents the time consistency error, Δt avg represents the average time consistency error of all data sources, σ q represents the noise standard deviation of the qth data source, and N represents the total number of data sources.

[0092] Convolutional neural networks are used to process the spatial distribution of signal strengths of different signal sources, and long short-term memory networks capture the law of signal changes over time; edge nodes analyze the user's mobility pattern in real time, and use the long short-term memory network model to capture the user's mobility trajectory characteristics and predict the user's next location. The expression is:

[0093]

[0094] Among them, p next represents the predicted next position, t 0 represents the lower limit of integration at a time point, t a represents the integral upper limit of the time point, i represents the index of the historical position point, M represents the number of historical position points, and a i (t) represents the acceleration at the i-th historical position point changing with time t, t i represents the timestamp of the i-th historical position point, and τ represents the width of the time window, which determines the impact range of the historical position point on the prediction.

[0095] S5. Based on the current environmental conditions, signal quality, and predicted user behavior, the system automatically selects the most appropriate positioning technology for positioning prediction, including the following steps:

[0096] First, define the environmental condition parameters, including signal shielding degree O, multipath effect M, and indoor / outdoor mark I. O and M can be obtained through environmental feature analysis, and I can be determined by detecting Wi-Fi hotspot density or GPS signal strength. Each parameter is normalized to the [0, 1] interval, where 0 represents the worst condition and 1 represents the best condition.

[0097] Next, define signal quality parameters, including Wi-Fi signal strength, Bluetooth signal strength B, and GPS signal strength G. Normalize the signal strength to the interval [0, 1], where 0 represents the weakest signal and 1 represents the strongest signal.

[0098] Furthermore, the predicted user behavior parameter P is defined as a vector, including the user's movement speed V, direction change rate D, and dwell time T. These parameters are also normalized to the interval [0, 1], where 0 represents stillness or no direction change, and 1 represents high-speed movement or frequent direction changes.

[0099] Considering the current environmental conditions, signal quality, and predicted user behavior, a comprehensive evaluation function is designed to evaluate the applicability of each positioning technology and select the most appropriate positioning technology for positioning prediction. The expression is:

[0100]

[0101] Among them, F(T) represents the comprehensive evaluation function for different positioning technologies, T represents the type of positioning technology, T∈{1,2,3} corresponds to Wi-Fi positioning, Bluetooth positioning and GPS positioning respectively, S k represents the strength of the kth signal, E k It represents the degree of environmental influence on the k-th signal, η represents the environmental factor adjustment coefficient, which is used to quantify the influence of environmental factors on positioning, β represents a constant, which is used to adjust the influence of user behavior on technology selection, and P is the modulus of the user behavior parameter vector, which represents the activity of user behavior.

[0102] S6. Based on the automatically selected positioning prediction technology, generating the final positioning result and feeding it back to the user equipment through the 5G network includes the following steps:

[0103] Use the selected positioning technology to select the optimal positioning technology for prediction; calculate the final positioning result based on the prediction result of the optimal positioning technology and the current environmental conditions. The expression is:

[0104]

[0105] Among them, x final represents the final optimal positioning estimation result, η represents the environmental factor adjustment coefficient, which is used to quantify the impact of environmental factors on positioning, and e represents the environmental vector, including signal shielding, multipath effect, etc.;

[0106] Receive real-time positioning results from the intelligent positioning system, including current coordinates, predicted position, signal source strength, environmental parameters (such as signal shielding degree, multipath effect index, indoor / outdoor signs), positioning timestamp, etc., convert the positioning result information into a unified data format and encapsulate it into a data packet; use data compression algorithms (such as LZ77, Huffman coding or the more advanced LZMA) to compress the data packet, and use encryption algorithms such as Advanced Encryption Standard (AES) or RSA to encrypt the compressed data packet; embed CRC (cyclic redundancy check) or MD5 hash check code in the encrypted data packet and establish a retransmission mechanism. If the receiving end detects data damage, it will request to resend the damaged data packet through the reverse channel of the 5G network until the complete and intact data is received; in the 5G network, for positioning A dedicated low-latency channel is reserved for data packets to ensure that the data packets can reach the user device with minimal delay, which usually involves dynamic spectrum access technology and multiple access technology, such as orthogonal frequency division multiple access (OFDMA); encrypted and compressed data packets are sent through the high-speed data link layer protocol of the 5G network (such as LTE or NR), and the high bandwidth and low latency characteristics of the 5G network are used to achieve efficient transmission; the positioning application on the user device receives the data packet and decrypts the data packet using a shared key or a private key; the integrity of the data packet is verified using a CRC or MD5 checksum, and a retransmission request is triggered if the data packet is damaged; the decrypted data packet is decompressed to restore the original data structure; the positioning application parses data in JSON or XML format, extracts the positioning results and predicted location, and updates the user interface to display the current and predicted location information.

[0107] S7, continuously monitor positioning results and environmental changes, collect user feedback, and optimize the system, including the following steps:

[0108] The system continuously collects real-time data streams and collects user feedback through mobile application interfaces, email surveys, and social media monitoring channels, including positioning accuracy, system response speed, and overall user experience; applies natural language processing (NLP) technology to perform sentiment analysis on user feedback and identify key factors for positive and negative reviews; uses cluster analysis to identify the behavioral patterns of user groups and understand the differences in the requirements of different user groups for system performance; develops adaptive filters (such as Kalman filters) to correct positioning errors; adjusts the parameters of the positioning algorithm based on the results of real-time data analysis; adopts a dynamic power control strategy to intelligently adjust the working status of sensors and processors according to the device activity level to extend battery life; optimizes the user interface design based on user feedback and behavioral pattern analysis, simplifies the operation process, and improves user satisfaction.

[0109] In summary, the present invention improves signal quality and data integrity by integrating multi-source signals and performing data preprocessing. High-precision time synchronization is achieved by utilizing the low latency characteristics of 5G, thereby enhancing positioning accuracy. Edge computing analyzes user mobility patterns in real time, dynamically adjusts data source weights based on signal strength and time consistency, and improves prediction accuracy. The intelligent switching mechanism automatically selects the best positioning technology based on environmental conditions and signal quality, ensuring the flexibility and reliability of positioning. Ultimately, the system efficiently feeds back positioning results through the 5G network, continuously optimizes, and uses natural language processing and cluster analysis to improve user experience, achieving a comprehensive improvement in positioning accuracy, response speed, and user satisfaction, which is particularly suitable for complex and changing environments.

[0110] Example 2

[0111] Referring to Table 1, which is the second embodiment of the present invention, in order to further verify the advancement of the present invention, experimental simulation data of a collaborative positioning system based on a 5G communication network is provided.

[0112] The system deploys 5G base stations, GPS satellite receivers, Wi-Fi hotspots, Bluetooth beacons, and mobile devices with built-in accelerometers and gyroscopes.

[0113] During the data collection phase, the system ran continuously for 24 hours, collecting data on signal strength, signal arrival time, pseudorange, carrier phase, received signal strength indication (RSSI), and device motion status. The signal processing phase included data preprocessing, time synchronization, dynamic weight allocation, and prediction algorithm training. Finally, the positioning accuracy evaluation phase compared the system's performance in different environments and compared it with traditional positioning technologies.

[0114] The details are shown in Table 1:

[0115] Table 1 Positioning system performance index record table

[0116]

[0117]

[0118] By conducting an in-depth analysis of the data in the above table, we can clearly see the advantages of the present invention under various environmental conditions. First, the system achieved the lowest time synchronization error (0.3μs) and position prediction error (2.1m) in the 5G-dominated environment of blocks A and D. Even in underground parking lot C with weak signals, the automatic selection of Bluetooth positioning technology keeps the positioning error within a reasonable range (3.2m).

[0119] The average signal strength reflects the quality of signal reception. The average signal strength of 5G in blocks A and D is -75dBm and -78dBm, which is significantly better than other technologies. In addition, power consumption data shows that the system maintains low energy consumption while efficiently positioning, averaging 120-130mAh / hour, which has little impact on the battery life of mobile devices.

[0120] The high scores of user satisfaction (90%-92%) indicate that the system not only provides accurate positioning services, but also provides a good user experience, especially in terms of response speed, with the average positioning time delay being only 11-15ms.

[0121] To sum up, the 5G collaborative positioning system of the present invention has significant creativity and novelty in terms of signal processing efficiency, positioning accuracy, response speed and energy consumption management compared with the single technology positioning method. Especially in the complex and changeable urban environment, the system can automatically adapt to and select the most suitable positioning technology, greatly improving the flexibility and reliability of positioning.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A collaborative positioning system based on a 5G communication network, characterized in that: include, Multimodal data acquisition and preprocessing module: receives multi-source data from 5G base stations, GPS satellites, Wi-Fi hotspots, and Bluetooth beacons, and performs preliminary cleaning; High-precision time synchronization module: Utilizes the low latency characteristics of the 5G network to perform high-precision time synchronization with the base station and perform time stamping; Real-time analysis module on edge computing nodes: transmits data with precise timestamps to edge computing nodes, analyzes user movement patterns in real time, and predicts the user's possible next location; Intelligent switching and positioning prediction module: Based on the current environmental conditions, signal quality, and predicted user behavior, the system automatically selects the most appropriate positioning technology for positioning prediction; Positioning result generation and feedback module: Based on the automatically selected positioning prediction technology, it generates the final positioning result and feeds it back to the user device through the 5G network; Continuous monitoring and system optimization module: continuously monitors positioning effects and environmental changes, collects user feedback, and performs system optimization; The data with precise timestamps is passed to the edge computing node to analyze the user's movement pattern in real time and predict the user's possible next location. The following steps are involved: Extract data points with precise timestamps from the time database and upload them to the edge computing node; The quality of the data points on the edge computing nodes is evaluated. The weights of different data sources are dynamically adjusted according to the signal strength, noise level, and time consistency to reflect the quality of different data sources. The expression is: Among them, w p represents the weight of the pth data source, σ p represents the noise level, σ avg represents the average noise level of all data sources, Δt p represents the time consistency error, Δt avg represents the average time consistency error of all data sources, σ q represents the noise standard deviation of the qth data source, and N represents the total number of data sources; Convolutional neural networks are used to process the spatial distribution of signal strengths from different signal sources, and long short-term memory networks are used to capture the temporal changes of signals. The edge node analyzes the user's mobility pattern in real time, uses the long short-term memory network model to capture the user's movement trajectory characteristics, and predicts the user's next location. The expression is: Among them, p next represents the next predicted position, t0 represents the lower limit of the integral at the time point, and t a represents the integral upper limit of the time point, i represents the index of the historical position point, M represents the number of historical position points, and a i (t) represents the acceleration at the i-th historical position point changing with time t, t i represents the timestamp of the i-th historical location point, and τ represents the time window width; Based on current environmental conditions, signal quality, and predicted user behavior, the system automatically selects the most appropriate positioning technology for positioning prediction, which includes the following steps: Considering the current environmental conditions, signal quality, and predicted user behavior, a comprehensive evaluation function is designed to evaluate the applicability of each positioning technology and select the most appropriate positioning technology for positioning prediction. The expression is: Among them, F(T) represents the comprehensive evaluation function for different positioning technologies, T represents the type of positioning technology, T∈{1, 2, 3} corresponds to Wi-Fi positioning, Bluetooth positioning and GPS positioning respectively, S k represents the strength of the kth signal, E k represents the degree of environmental influence on the kth signal, β represents a constant used to adjust the influence of user behavior on technology selection, P represents the modulus of the user behavior parameter vector, V represents the user's movement speed, and D represents the user's direction change rate.

2. The collaborative positioning system based on the 5G communication network according to claim 1, characterized in that: The receiving multi-source data includes collecting signal strength and signal arrival time from 5G base stations; Collect pseudorange and carrier phase data from GPS satellites; Collect received signal strength indicators from Wi-Fi hotspots and Bluetooth beacons; The system's built-in accelerometer and gyroscope sensors continuously monitor the device's motion status.

3. The collaborative positioning system based on the 5G communication network as claimed in claim 1, characterized in that: The preliminary cleaning includes using statistical methods to identify and remove data points that significantly deviate from the normal range; Evaluate signal quality based on signal strength and signal stability, and discard low-quality data; Use interpolation methods to fill missing data.

4. The collaborative positioning system based on the 5G communication network as claimed in claim 3, characterized in that: Taking advantage of the low latency characteristics of the 5G network, high-precision time synchronization with the base station and time stamping include the following steps: Select PTP as the time synchronization protocol and initialize it; The system starts exchanging PTP messages with the base station, including: The base station, as the master clock, sends a Sync message containing the timestamp of the sending time; After the base station sends the Sync message, it continues to send the Follow Up message containing the precise sending time; The system acts as a slave clock and sends a Delay Request message to the base station to request delay information; After receiving the Delay Request, the base station immediately sends a Delay Response message containing the timestamps of receiving and sending; Calculate the time offset between the system and the base station based on the timestamp of the PTP message; After completing time synchronization, all collected data are time-stamped; The system establishes a time-stamp database and stores each data point with an accurate time stamp in the time-stamp database.

5. The collaborative positioning system based on the 5G communication network as claimed in claim 4, characterized in that: Generating the final positioning result based on the automatically selected positioning prediction technology and feeding it back to the user equipment through the 5G network includes the following steps: Using the selected positioning technology, select the best positioning technology for prediction; Calculate the final positioning result based on the results predicted by the optimal positioning technology and the current environmental conditions; Receive real-time positioning results from the intelligent positioning system, convert the positioning result information into a unified data format and encapsulate it into a data packet; Compress the data packet using a data compression algorithm, and encrypt the compressed data packet using an advanced encryption standard; Embed MD5 hash checksum in the encrypted data packet and establish a retransmission mechanism. If the receiving end detects data corruption, it will request to resend the damaged data packet through the reverse channel of the 5G network until the complete and intact data is received. In 5G networks, dedicated low-latency channels are reserved for positioning data packets; Send encrypted and compressed data packets via the high-speed data link layer protocol of the 5G network; The location application on the user's device receives the data packet and decrypts the data packet using the shared key or private key; Use MD5 checksum to verify the integrity of the data packet. If the data packet is damaged, a retransmission request is triggered. Decompress the decrypted data packet and restore the original data structure; The positioning application parses the data in JSON format, extracts the positioning results and predicted positions, and updates the user interface to display the current and predicted position information.

6. The collaborative positioning system based on the 5G communication network as claimed in claim 5, characterized in that: Continuously monitor positioning results and environmental changes, collect user feedback, and optimize the system, including the following steps: The system continuously collects real-time data streams and gathers user feedback through mobile app interfaces, email surveys, and social media listening channels; Apply natural language processing technology to conduct sentiment analysis on user feedback and identify key factors of positive and negative reviews; Use cluster analysis to identify the behavior patterns of user groups and understand the differences in system performance requirements of different user groups; Develop adaptive filters to correct positioning errors; Adjust the parameters of the positioning algorithm based on the results of real-time data analysis; Adopt dynamic power control strategy to intelligently adjust the working state of sensors and processors according to the activity level of the device to extend battery life; Based on user feedback and behavior pattern analysis, optimize user interface design, simplify operation processes, and improve user satisfaction.

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