A tracking tag terminal based on ZETA technology

Abstract

This invention discloses a tracking tag terminal based on ZETA technology, belonging to the field of ZETA technology. It includes a tag initialization module for initializing the tag terminal and configuring a multi-dimensional sensor and communication module; a data acquisition module for real-time acquisition of multi-dimensional data at preset time intervals; a data fusion module for fusing the multi-dimensional data; an authentication module for assigning a unique identifier to each tag and authenticating the tag's identity through a public key infrastructure-based authentication mechanism; a positioning module for accurately locating the tag using ultra-wideband technology, combining signal arrival time and time difference algorithms with the cooperation of multiple base stations to calculate the tag's precise location; and a data transmission module for uploading the multi-dimensional data to a base station or cloud platform via the ZETA protocol. Through ultra-wideband technology and the ZETA protocol, it provides high-precision positioning and real-time tracking capabilities while achieving low-power, high-efficiency data transmission.

Description

Technical Field

[0001] This invention relates to the field of ZETA technology, specifically to a tracking tag terminal based on ZETA technology. Background Technology

[0002] With the development of IoT technology, tag tracking systems are being used more and more widely in logistics, industry, smart homes, and intelligent transportation. While traditional wireless communication technologies (such as Wi-Fi and Bluetooth) offer some positioning and tracking capabilities over short distances, they suffer from shortcomings in positioning accuracy, transmission stability, and power consumption control, making it difficult to meet the demands of high-precision and high-reliability applications. Especially in complex environments, existing wireless communication technologies cannot provide sufficient guarantees in terms of data transmission stability and real-time performance.

[0003] To overcome the aforementioned problems, ZETA technology, a low-power wide-area network (LPWAN) technology, has emerged. Through its optimized data transmission mechanism and low-power characteristics, the ZETA protocol enables efficient communication in scenarios involving long-distance, large-scale device access. Combining the low-power characteristics of the ZETA protocol with the high-precision measurement capabilities of multi-dimensional sensors, it can provide devices with more accurate data support and positioning services, showing great potential, particularly in applications such as real-time tracking and asset management.

[0004] However, existing tracking systems based on ZETA technology still face challenges in areas such as data fusion, identity authentication, security, and positioning accuracy. For example, ensuring the accuracy of multidimensional data, achieving efficient tag positioning, and guaranteeing data transmission security remain bottlenecks in current system design. Furthermore, improving the depth analysis capabilities of data and optimizing system performance through intelligent analysis algorithms are also urgent issues to be addressed. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a tracking tag terminal based on ZETA technology to solve the above-mentioned technical problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a tracking tag terminal based on ZETA technology, comprising: a tag initialization module, a data acquisition module, a data fusion module, an identity authentication module, a positioning module, and a data transmission module;

[0007] The tag initialization module is used to initialize the tag terminal and configure the multi-dimensional sensor and communication module;

[0008] The data acquisition module is used to acquire multidimensional data in real time through a multidimensional sensor at preset time intervals;

[0009] The data fusion module is used to fuse multidimensional data collected by multidimensional sensors;

[0010] The identity authentication module is used to assign a unique identity identifier to each tag to ensure the uniqueness of the tag in the network, and to authenticate the tag's identity through an authentication mechanism based on public key infrastructure;

[0011] The positioning module is used to accurately locate the tag using ultra-wideband technology. The positioning module combines the signal arrival time and time difference algorithm with the cooperation of multiple base stations to calculate the precise location of the tag.

[0012] The data transmission module is used to upload the collected and processed multidimensional sensor data to the base station or cloud platform via the ZETA protocol.

[0013] The present invention is further configured such that the tag initialization module configures the calibration algorithm of the sensor to ensure the accuracy and reliability of each sensor; the tag initialization module configures the ZETA low-power wide area network protocol to ensure stable data communication between the tag and the base station or cloud platform.

[0014] The present invention is further configured such that the data acquisition module includes denoising, interpolation, and standardization processing of the acquired multidimensional data, and using a Kalman filter algorithm to eliminate environmental interference or measurement errors;

[0015] Perform data format conversion and time sequence alignment to ensure data consistency and timeliness.

[0016] The present invention is further configured such that the positioning module uses ultra-wideband technology and signal arrival time and time difference algorithm to locate the tag through cooperation between multiple base stations;

[0017] The two-dimensional or three-dimensional coordinates of the tag are determined by the triangulation algorithm, which optimizes the positioning results and improves the positioning accuracy.

[0018] The present invention is further configured such that the signal arrival time algorithm calculates the distance by measuring the propagation time of the signal from the base station to the tag and combining it with the known signal propagation speed; the time difference algorithm calculates the relative distance between the tag and each base station by measuring the time difference between the tag signals received by different base stations; and the positioning accuracy is improved by using a multi-base station collaborative strategy to jointly calculate the tag's position using multiple base stations.

[0019] The signal arrival time and time difference data obtained from the base station are used to determine the two-dimensional or three-dimensional coordinates of the tag using a triangulation algorithm. In two-dimensional space, the tag's position is solved using a geometric method based on the signal time differences of at least three base stations. In three-dimensional space, the tag's position is solved using a geometric method based on the signal time differences of at least four base stations.

[0020] The present invention is further configured such that the data transmission module uses data compression and dynamic channel allocation to optimize the data transmission process, reduce latency and improve data transmission rate; the data transmission module uses AES encryption and hash algorithm to ensure the security and integrity of data during transmission, and uses TLS / SSL protocol to ensure the security of data transmission, preventing man-in-the-middle attacks and replay attacks.

[0021] The present invention is further configured to include an intelligent analysis module, which is used to perform real-time analysis and processing on the data collected by the sensor. The analysis and processing includes time series analysis, anomaly detection, and trend prediction.

[0022] The present invention is further configured such that the intelligent analysis module uses the ARIMA model and support vector machine algorithm for data pattern recognition and anomaly detection, and further combines convolutional neural network for advanced feature extraction to enhance the intelligence and predictive ability of the labeled data.

[0023] The present invention is further configured to include a fault detection module, which monitors the operating status of the tag in real time by introducing a fault detection algorithm. When a fault is detected, the terminal continues to operate through a redundant system or backup mechanism. At the same time, the fault is reduced by using a self-recovery algorithm and a local repair strategy to ensure the high availability of the terminal.

[0024] This invention provides a tracking tag terminal based on ZETA technology, including a tag initialization module, a data acquisition module, a data fusion module, an identity authentication module, a positioning module, and a data transmission module. The tag initialization module initializes the tag terminal and configures a multi-dimensional sensor and a communication module. The data acquisition module collects multi-dimensional data in real time using the multi-dimensional sensor at preset time intervals. The data fusion module fuses the multi-dimensional data collected by the multi-dimensional sensor. The identity authentication module assigns a unique identifier to each tag to ensure its uniqueness in the network and authenticates the tag's identity through a public key infrastructure-based authentication mechanism. The positioning module uses ultra-wideband technology to accurately locate the tag, combining time of arrival (TOA) and time difference algorithms with the cooperation of multiple base stations to calculate the tag's precise location. The data transmission module uploads the collected and processed multi-dimensional sensor data to a base station or cloud platform via the ZETA protocol. The resulting benefits include:

[0025] 1. High-precision positioning and real-time tracking capabilities: By combining ultra-wideband technology with signal arrival time and time difference algorithms, and employing multi-base station cooperation and triangulation algorithms, high-precision tag positioning can be achieved. This technology not only supports accurate positioning in two-dimensional and three-dimensional space, but also significantly improves positioning accuracy and real-time performance in complex environments by optimizing positioning results, meeting the needs of application scenarios with high accuracy requirements;

[0026] 2. Low-power, high-efficiency data transmission and security: By adopting the ZETA protocol for data transmission, it possesses the advantages of low-power wide-area network communication, enabling stable operation of large-scale devices for extended periods and significantly extending the tag's lifespan. Furthermore, AES encryption, hash algorithms, and TLS / SSL protocols during data transmission effectively ensure data security and integrity, preventing man-in-the-middle attacks and replay attacks, thus guaranteeing data transmission security.

[0027] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0029] Figure 1 The flowchart illustrates a tracking tag terminal based on ZETA technology, which is an exemplary embodiment of the present invention. Detailed Implementation

[0030] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0031] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0032] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0033] A tracking tag terminal based on ZETA technology, such as Figure 1 As shown, it includes:

[0034] The system includes a tag initialization module, a data acquisition module, a data fusion module, an identity authentication module, a positioning module, and a data transmission module.

[0035] The tag initialization module is used to initialize the tag terminal and configure the multi-dimensional sensor and communication module;

[0036] The data acquisition module is used to acquire multidimensional data in real time through a multidimensional sensor at preset time intervals;

[0037] The data fusion module is used to fuse multidimensional data collected by multidimensional sensors;

[0038] The identity authentication module is used to assign a unique identity identifier to each tag to ensure the uniqueness of the tag in the network, and to authenticate the tag's identity through an authentication mechanism based on public key infrastructure;

[0039] The positioning module is used to accurately locate the tag using ultra-wideband technology. The positioning module combines the signal arrival time and time difference algorithm with the cooperation of multiple base stations to calculate the precise location of the tag.

[0040] The data transmission module is used to upload the collected and processed multidimensional sensor data to the base station or cloud platform via the ZETA protocol.

[0041] The invention is further configured such that the tag initialization module configures a sensor calibration algorithm to ensure the accuracy and reliability of each sensor; the tag initialization module also configures the ZETA low-power wide-area network protocol to ensure stable data communication between the tag and the base station or cloud platform. Specifically, in the tag initialization module, the sensor calibration algorithm is a crucial means of ensuring the accuracy and reliability of each sensor. Each sensor may have certain measurement errors at the time of manufacture or be affected by external environmental factors (such as temperature, humidity, electromagnetic interference, etc.). Therefore, during the system initialization phase, the sensors need to be calibrated to improve the accuracy and consistency of the data. The purpose of calibration is to compensate for sensor errors, eliminate or reduce interference from influencing factors, and ensure that the sensor output signal is more accurate. During the calibration process, a set of reference data under known conditions needs to be collected, such as known temperature values ​​and known location information. Based on the collected data, an algorithm (such as least squares method, maximum likelihood estimation, etc.) is used to establish a sensor error model. These algorithms estimate the sensor's deviation based on known reference data; they compensate and adjust the sensor's output in real time according to the error model, thereby eliminating deviation and improving measurement accuracy; during operation, the sensor may be affected by environmental changes, so the calibration algorithm also needs to be adaptive, able to adjust the calibration parameters in real time according to environmental changes, and maintain the sensor's long-term stable performance; through the calibration algorithm, the system can ensure that the output data of each sensor is accurate and reliable, laying the foundation for subsequent data fusion and processing.

[0042] The present invention is further configured such that the data acquisition module includes denoising, interpolation, and standardization processing of the acquired multidimensional data, and using a Kalman filter algorithm to eliminate environmental interference or measurement errors;

[0043] Data format conversion and time-series alignment are performed to ensure data consistency and timeliness. Specifically, the extended part of the data acquisition module involves preprocessing the acquired multidimensional data, including denoising, interpolation, standardization, data format conversion, and time-series alignment, to ensure the accuracy and consistency of subsequent data processing. In particular, the above steps can effectively eliminate environmental noise, fill in data gaps, and ensure that data from different sources are consistent in format and time scale. During data acquisition, sensor signals may be affected by environmental noise, sensor errors, or other external interference, resulting in certain deviations or noise in the acquired data. Therefore, denoising processing is required after data acquisition. The goal of denoising is to extract the effective components of the signal while eliminating useless noise, ensuring the accuracy of subsequent analysis and processing. Kalman filtering is an adaptive filter that can dynamically estimate the true state of the system based on known state and observation information. In this invention, Kalman filtering is used to eliminate environmental interference or measurement errors. The Kalman filtering algorithm corrects the data in each iteration by combining the current observation and prediction values, thereby obtaining a more accurate signal. During data acquisition, data gaps may occur due to sensor failures, transmission problems, etc. In this context, interpolation techniques can infer missing data from existing data. The goal of interpolation is to ensure data integrity, ensuring consistent time or spatial intervals between all data points and avoiding inaccuracies caused by missing data. Interpolation methods include linear interpolation: connecting missing data points with straight lines based on their trends; and spline interpolation: generating smooth interpolation curves through polynomial fitting, suitable for nonlinear data. Since multidimensional sensor data comes from different types of sensors (such as temperature, acceleration, humidity, etc.), the dimensions and ranges of this data are often different. Standardization aims to convert data with different dimensions into a unified standard form, ensuring that each data dimension has the same measurement scale, facilitating subsequent analysis and processing. During multidimensional data acquisition, data from different sensors may have different formats (such as different units, data structures, etc.), and the acquisition time may also differ. Therefore, data format conversion and time-series alignment are necessary to ensure consistency in both time and space. Time-series alignment refers to adjusting the timestamps of different sensor data through interpolation or synchronization algorithms, allowing data from different sensors to be compared and analyzed within the same time window.

[0044] The present invention is further configured such that the positioning module uses ultra-wideband technology and signal arrival time and time difference algorithm to locate the tag through cooperation between multiple base stations;

[0045] The two-dimensional or three-dimensional coordinates of the tag are determined by the triangulation algorithm, which optimizes the positioning results and improves the positioning accuracy.

[0046] This invention further specifies that the Time of Arrival (TOA) algorithm calculates the distance by measuring the propagation time of the signal from the base station to the tag and combining it with the known signal propagation speed. The Time Difference of Arrival (TDOA) algorithm calculates the relative distance between the tag and each base station by measuring the time difference between the received signals from different base stations. Through a multi-base station collaborative strategy, multiple base stations are used to jointly calculate the tag's position, improving positioning accuracy. Specifically, in this invention, the combination of the TOA and TDOA algorithms utilizes the time characteristics of signal propagation between the base station and the tag, using different algorithms to calculate the distance between the tag and the base station, ultimately achieving high-precision positioning. Specifically, the TOA algorithm measures the propagation time of the signal from the base station to the tag, while the TDOA algorithm calculates the relative position of the tag using the time difference between signals from multiple base stations. The core principle of the TOA algorithm is based on the physical properties of electromagnetic wave propagation. The propagation time of the signal from the base station to the tag is known, and the signal propagation speed can be estimated under known environmental conditions (usually the speed of light or the speed of electromagnetic waves in air). By measuring the propagation time of a signal from a base station to a tag, the distance between the tag and the base station can be calculated. The core principle of the time difference algorithm is to measure the time difference between the signal transmitted from the tag and the signal received by each base station. The time difference between the received tag signals by different base stations can be used to calculate the relative distance from the tag to different base stations, thereby determining the tag's specific location. Since the distances between multiple base stations are known, the tag's location can be deduced by measuring the signal time difference and using triangulation algorithms or other geometric calculation methods. Multi-base station cooperative positioning refers to the collaborative work of multiple base stations, integrating the signal arrival time (TOA) and time difference of arrival (TDOA) data obtained from each base station, and using certain algorithms (such as triangulation algorithms, least squares methods, etc.) to calculate the tag's location. Multi-base station cooperation can significantly improve positioning accuracy because it reduces the impact of positioning errors from a single base station and improves the reliability of location calculation.

[0047] Signal arrival time and time difference data obtained from base stations are used to determine the tag's two-dimensional or three-dimensional coordinates using a triangulation algorithm. In two-dimensional space, the tag's position is determined geometrically using the signal time differences from at least three base stations. In three-dimensional space, the tag's position is determined geometrically using the signal time differences from at least four base stations. Specifically, in two-dimensional space, time difference data from at least three base stations is needed to determine the tag's precise location. Using triangulation, based on known base station locations and signal propagation time differences, the tag's position on the two-dimensional plane can be determined geometrically. The relative distance to each base station is obtained by calculating the signal arrival time difference using triangulation. Then, according to geometric principles, a unique point on the plane is determined using three points, i.e., the tag's position. In three-dimensional space, time difference data from at least four base stations is needed to calculate the tag's position. The distance from the tag to each base station is calculated using a time difference algorithm, and then the tag's position is further determined using a three-dimensional geometric method. The tag's position in three-dimensional space can be determined using time difference data from at least four base stations. Finally, using a three-dimensional geometric algorithm, combined with known base station locations and distance data from the tag to each base station, the tag's three-dimensional coordinates are accurately calculated.

[0048] The invention is further configured such that the data transmission module uses data compression and dynamic channel allocation to optimize the data transmission process, reducing latency and increasing data transmission rate; the data transmission module employs AES encryption and hash algorithms to ensure the security and integrity of data during transmission, and uses TLS / SSL protocols to ensure the security of data transmission, preventing man-in-the-middle attacks and replay attacks. Specifically, data compression refers to compressing the data to be transmitted during data transmission to reduce the data volume, thereby reducing the bandwidth and time required for transmission. Transmitting data in a smaller volume using compression algorithms can significantly improve the data transmission rate, especially important when bandwidth is limited or network resources are scarce; after data acquisition, the data transmission module first compresses the acquired data. The compressed data volume is smaller, effectively reducing the required transmission time and bandwidth, and optimizing transmission efficiency. This can be achieved through various compression algorithms, such as lossless compression algorithms or domain-specific compression techniques; dynamic channel allocation technology refers to dynamically adjusting and allocating communication channels according to real-time network conditions, data traffic, and environmental changes to ensure the efficiency and stability of data transmission. By flexibly adjusting channel allocation, network congestion can be avoided, latency reduced, and higher data transmission rates ensured. The data transmission module monitors the network environment (such as bandwidth, signal quality, latency, etc.) and dynamically adjusts channel allocation based on network conditions. This adjustment may include selecting a more suitable frequency band, optimizing modulation and demodulation methods, or distributing data load across multiple channels to ensure optimal transmission paths and rates. Security is paramount during data transmission, especially when sensitive data or identity information is involved. To ensure data is not tampered with, leaked, or subjected to man-in-the-middle attacks during transmission, this invention employs security measures such as AES encryption, hash algorithms, and TLS / SSL protocols. Advanced Encryption Standard (AES) is a symmetric-key encryption algorithm widely used to protect data confidentiality. In data transmission, AES encryption uses an encryption key to convert the original data into incomprehensible ciphertext, which only authorized parties can decrypt and restore. In the data transmission module, AES encryption is used to encrypt the collected and compressed data. Encrypted data, even if intercepted during transmission, cannot be read or tampered with by unauthorized users, ensuring data confidentiality. A hash algorithm is a method of converting input data (such as messages or files) into a fixed-length "digest" or "hash value." In data transmission, hash algorithms can be used to ensure data integrity. By calculating the hash value of the data and verifying it, the receiver can verify whether the data has been tampered with during transmission. When sending data, the data transmission module performs a hash operation on the data content to generate the data's hash value.Upon receiving the data, the receiver also calculates the hash value of the data and compares it with the received hash value to ensure that the data has not been tampered with or corrupted during transmission. TLS (Transport Layer Security) and SSL (Secure Sockets Layer) are protocols designed to ensure the security of computer network communications and are commonly used for encryption and authentication in internet communications. TLS / SSL protocols ensure the confidentiality and integrity of data through encryption technology and prevent man-in-the-middle attacks and replay attacks. The data transmission module uses TLS / SSL protocols to encrypt the data transmission process during communication and verifies the identities of both communicating parties through digital certificates, thereby ensuring data security. Furthermore, TLS / SSL protocols provide integrity verification functions to ensure that data has not been tampered with during transmission and prevent attackers from using previous communication data for attacks through replay protection mechanisms.

[0049] The invention is further configured to include an intelligent analysis module, which is used to perform real-time analysis and processing of data collected by sensors. The analysis and processing include time series analysis, anomaly detection, and trend prediction. The invention is further configured to use the ARIMA model and support vector machine algorithm for data pattern recognition and anomaly detection, and further combine convolutional neural networks for advanced feature extraction, enhancing the intelligence and predictive ability of the labeled data. Specifically, time series analysis is a technique for analyzing the temporal sequence characteristics of data, which can help identify time-related patterns such as trends and seasonal changes from historical data; anomaly detection identifies outliers in the collected data to detect potential faults or abnormal behaviors in the system, such as sensor malfunctions and abnormal data acquisition; trend prediction uses existing data to predict future trends, typically through statistical models and machine learning methods; ARIMA (AutoRegressive Integrated Moving Average) is a statistical model used for time series forecasting. The ARIMA model is suitable for processing time series data with certain trends and seasonality. It can predict future trends by processing autocorrelation, moving averages, and differencing in historical data. The ARIMA model first builds a time series model based on sensor-collected data, removes trends by analyzing autocorrelation and differencing, and smooths data fluctuations using moving averages. Finally, it uses the ARIMA model to predict future data, helping to analyze future trends of equipment or systems. Support Vector Machine (SVM) is a machine learning algorithm based on statistical learning theory, widely used in classification and regression problems. In this invention, SVM is mainly used for anomaly detection and data pattern recognition, effectively distinguishing normal from abnormal data from complex data. SVM classifies data by finding the optimal hyperplane. In anomaly detection, SVM distinguishes normal from abnormal data by constructing a decision boundary. For anomaly detection, SVM automatically identifies abnormal patterns in the data, such as equipment failure, sensor problems, or external interference, by learning data features. Convolutional Neural Network (CNN) is a deep learning model widely used in image processing and feature extraction. In this invention, CNNs are used for advanced feature extraction, enabling them to automatically learn and extract potentially useful features from sensor data, thereby enhancing the intelligence and predictive power of the data. Convolutional neural networks automatically extract hierarchical features from raw sensor data through multiple convolutional layers, pooling layers, and fully connected layers. In time-series data or other forms of sensor data, CNNs can identify potential patterns and transform them into useful features for subsequent anomaly detection or trend prediction.

[0050] The invention is further configured to include a fault detection module. This module, by introducing a fault detection algorithm, monitors the tag's operating status in real time. When a fault is detected, it ensures the terminal's continuous operation through a redundant system or backup mechanism. Simultaneously, it uses a self-recovery algorithm and local repair strategies to reduce the impact of the fault on the terminal, ensuring high availability. Specifically, the fault detection module monitors the tag's operating status in real time and takes corresponding measures when a fault is detected to ensure high system availability. The fault detection module includes the following key functions: a fault detection algorithm for real-time monitoring of the tag's operating status, including battery status, communication stability, and sensor accuracy. Through real-time data monitoring, the system can promptly detect potential faults; a redundant system or backup mechanism ensures the terminal device can continue operating when a fault occurs. The redundant system uses backup equipment or components to ensure the system continues to operate even if a component fails. When a fault occurs, the system can automatically repair itself using a self-recovery algorithm or use a local repair strategy to reduce the impact of the fault. This helps the device quickly return to normal operating status and reduces downtime.

[0051] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0052] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0053] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0054] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0055] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0056] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0057] In the several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0058] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0059] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0060] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0061] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A tracking tag terminal based on ZETA technology, characterized in that, include: The system includes a tag initialization module, a data acquisition module, a data fusion module, an identity authentication module, a positioning module, a data transmission module, and a fault detection module. The tag initialization module is used to initialize the tag terminal and configure the multi-dimensional sensor and communication module; The data acquisition module is used to acquire multidimensional data in real time through a multidimensional sensor at preset time intervals; The data fusion module is used to fuse multidimensional data collected by multidimensional sensors; The identity authentication module is used to assign a unique identity identifier to each tag to ensure the uniqueness of the tag in the network, and to authenticate the tag's identity through an authentication mechanism based on public key infrastructure; The positioning module is used to accurately locate the tag using ultra-wideband technology. The positioning module combines the signal arrival time and time difference algorithm with the cooperation of multiple base stations to calculate the precise location of the tag. The data transmission module is used to upload the collected and processed multidimensional sensor data to the base station or cloud platform via the ZETA protocol. The data transmission module uses data compression and dynamic channel allocation to optimize the data transmission process, reduce latency and improve data transmission rate. The data transmission module uses AES encryption and hash algorithm to ensure the security and integrity of data during transmission, and uses TLS / SSL protocol to ensure the security of data transmission and prevent man-in-the-middle attacks and replay attacks. The fault detection module introduces a fault detection algorithm to monitor the tag's operating status in real time. When a fault is detected, it ensures the continuous operation of the terminal through a redundant system or backup mechanism. At the same time, it uses a self-recovery algorithm and a local repair strategy to reduce the impact of the fault on the terminal and ensure the high availability of the terminal.

2. A tracking tag terminal based on ZETA technology according to claim 1, characterized in that, The tag initialization module configures the calibration algorithm for the sensors to ensure the accuracy and reliability of each sensor; the tag initialization module configures the ZETA low-power wide area network protocol to ensure stable data communication between the tag and the base station or cloud platform.

3. A tracking tag terminal based on ZETA technology according to claim 1, characterized in that, The data acquisition module also includes denoising, interpolation, and standardization of the acquired multidimensional data, and uses the Kalman filter algorithm to eliminate environmental interference or measurement errors. Perform data format conversion and time sequence alignment to ensure data consistency and timeliness.

4. A tracking tag terminal based on ZETA technology according to claim 1, characterized in that, The positioning module uses ultra-wideband technology and time of arrival (TOA) and time difference of arrival (TDOA) algorithms to locate the tag through cooperation between multiple base stations. The two-dimensional or three-dimensional coordinates of the tag are determined by the triangulation algorithm, which optimizes the positioning results and improves the positioning accuracy.

5. A tracking tag terminal based on ZETA technology according to claim 4, characterized in that, The signal arrival time algorithm calculates the distance by measuring the propagation time of the signal from the base station to the tag and combining it with the known signal propagation speed. The time difference algorithm calculates the relative distance between the tag and each base station by measuring the time difference between the tag signals received by different base stations. Through a multi-base station collaboration strategy, multiple base stations are used to jointly calculate the location of the tag, thereby improving positioning accuracy. The signal arrival time and time difference data obtained from the base station are used to determine the two-dimensional or three-dimensional coordinates of the tag using a triangulation algorithm. In two-dimensional space, the tag's position is solved using a geometric method based on the signal time differences of at least three base stations. In three-dimensional space, the tag's position is solved using a geometric method based on the signal time differences of at least four base stations.

6. A tracking tag terminal based on ZETA technology according to claim 1, characterized in that, It also includes an intelligent analysis module, which is used to perform real-time analysis and processing of the data collected by the sensors. The analysis and processing includes time series analysis, anomaly detection, and trend prediction.

7. A tracking tag terminal based on ZETA technology according to claim 6, characterized in that, The intelligent analysis module uses the ARIMA model and support vector machine algorithm for data pattern recognition and anomaly detection, and further combines convolutional neural networks for advanced feature extraction to enhance the intelligence and predictive ability of the labeled data.

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