Tracking label terminal based on ZETA technology

Abstract

The invention discloses a tracking tag terminal based on a ZETA technology, and relates to the technical field of ZETA, and the tracking tag terminal comprises a tag initialization module which is used for initializing the tag terminal and configuring a multi-dimensional sensor and a communication module; the data acquisition module is used for acquiring multi-dimensional data in real time according to a preset time interval; the data fusion module is used for fusing the multi-dimensional data; the identity authentication module is used for distributing a unique identity identifier for each label and carrying out identity authentication on the labels through an identity authentication mechanism based on a public key infrastructure; the positioning module is used for accurately positioning the label by using an ultra-wideband technology, and the positioning module is combined with a signal arrival time and time difference algorithm and cooperation of a plurality of base stations to calculate the accurate position of the label; and the data transmission module is used for uploading the multi-dimensional data to a base station or a cloud platform through a ZETA protocol. Through an ultra-wideband technology and a ZETA protocol, high-precision positioning and real-time tracking capabilities are provided, and meanwhile, low-power-consumption and efficient data transmission is realized.

Description

Technical Field

[0001] The present invention relates to the field of ZETA technology, in particular to a tracking label terminal based on ZETA technology. Background Art

[0002] With the development of Internet of Things technology, tag tracking systems are increasingly being used in logistics, industry, smart home, and smart transportation. Although traditional wireless communication technologies (such as Wi-Fi and Bluetooth) have certain positioning and tracking capabilities within a short distance, they are insufficient in positioning accuracy, transmission stability, and power consumption control, and are difficult to meet the application requirements of high precision and high reliability. Especially in complex environments, existing wireless communication technologies cannot provide sufficient guarantees in terms of data transmission stability and real-time performance.

[0003] In order to overcome the above problems, ZETA technology has emerged as a low-power wide area network (LPWAN) technology. The ZETA protocol can achieve efficient communication in scenarios where large-scale devices are connected over long distances through optimized data transmission mechanisms and low power consumption. Combining the low power consumption characteristics of the ZETA protocol with the high-precision measurement capabilities of multi-dimensional sensors can provide devices with more accurate data support and positioning services, especially in applications such as real-time tracking and asset management, which has great potential.

[0004] However, the existing tracking system based on ZETA technology still faces certain challenges in data fusion, identity authentication, security, positioning accuracy, etc. For example, how to ensure the accuracy of multi-dimensional data, how to efficiently locate tags, and how to ensure the security of data transmission are still bottlenecks in the current system design. In addition, how to improve the deep analysis capabilities of data and optimize system performance through intelligent analysis algorithms is also an urgent problem to be solved. Summary of the invention

[0005] Based on the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a tracking label terminal based on ZETA technology to solve the above-mentioned technical problems.

[0006] To achieve the above object, the present invention provides the following technical solutions: 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 collect multi-dimensional data in real time through a multi-dimensional sensor according to a preset time interval;

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

[0010] The identity authentication module is used to assign a unique identity identifier to each tag, ensure the uniqueness of the tag in the network, and authenticate the tag through an identity authentication mechanism based on a 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 and the cooperation of multiple base stations to calculate the precise position of the tag;

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

[0013] The present 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 configures a ZETA low-power wide area network protocol to ensure stable data communication between the tag and a base station or a cloud platform.

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

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

[0016] The present invention is further configured 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 through the triangulation positioning algorithm to optimize the positioning results and improve the positioning accuracy.

[0018] The present invention is further configured such that the signal arrival time algorithm measures the propagation time of the signal from the base station to the tag and calculates the distance in combination with the known signal propagation speed, and the time difference algorithm measures the time difference between different base stations receiving the tag signal and calculates the relative distance between the tag and each base station. Through the strategy of multi-base station collaboration, multiple base stations are used to jointly calculate the position of the tag to improve the positioning accuracy.

[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 through a triangulation positioning algorithm. In two-dimensional space, the position of the tag is solved by a geometric method through the signal time difference of at least three base stations. In three-dimensional space, the position of the tag is solved by a geometric method through the signal time difference 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 delays and increase data transmission rates; the data transmission module uses 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.

[0021] The present invention is further configured to also include an intelligent analysis module, which is used to perform real-time analysis and processing on the data collected by the sensor, and the analysis and processing content 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 to perform data pattern recognition and anomaly detection, and further combines a convolutional neural network to perform advanced feature extraction to enhance the intelligence and predictive ability of the label 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 redundant system or backup mechanism is used to ensure the continuous operation of the terminal. At the same time, a self-recovery algorithm and a local repair strategy are used to reduce the impact of the fault on the terminal, thereby ensuring the high availability of the terminal.

[0024] The present invention provides 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; the tag initialization module is used to initialize the tag terminal and configure a multidimensional sensor and a communication module; the data acquisition module is used to collect multidimensional data in real time through a multidimensional sensor at a preset time interval; the data fusion module is used to fuse the multidimensional data collected by the multidimensional sensor; 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 through an identity authentication mechanism based on a public key infrastructure; the positioning module is used to accurately locate the tag using ultra-wideband technology, and the positioning module combines the signal arrival time and time difference algorithm and the cooperation of multiple base stations to calculate the precise position of the tag; the data transmission module is used to upload the collected and processed multidimensional sensor data to a base station or a cloud platform through a ZETA protocol, and the beneficial effects produced include:

[0025] 1. High-precision positioning and real-time tracking capabilities: Ultra-wideband technology combined with signal arrival time and time difference algorithm, multi-base station collaboration and triangulation positioning algorithm can achieve high-precision tag positioning. This technology not only supports precise 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 application scenarios with high accuracy requirements;

[0026] 2. Low power consumption, efficient data transmission and security guarantee: By adopting the ZETA protocol for data transmission, it has the advantage of low power consumption wide area network communication, can support long-term stable operation of large-scale equipment, and greatly extend the working life of the tag. In addition, the AES encryption, hash algorithm and TLS / SSL protocol in the data transmission process effectively ensure the security and integrity of the data, prevent man-in-the-middle attacks and replay attacks, and ensure the security of data transmission.

[0027] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0029] Figure 1 The flowchart of a tracking tag terminal based on ZETA technology is shown as an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will describe the embodiments of the present invention 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 contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways 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, 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 illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0032] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

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

[0034] Tag initialization module, data acquisition module, data fusion module, identity authentication module, positioning module and 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 collect multi-dimensional data in real time through a multi-dimensional sensor according to a preset time interval;

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

[0038] The identity authentication module is used to assign a unique identity identifier to each tag, ensure the uniqueness of the tag in the network, and authenticate the tag through an identity authentication mechanism based on a 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 and the cooperation of multiple base stations to calculate the precise position of the tag;

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

[0041] The present invention is further configured such that the tag initialization module configures the sensor calibration algorithm to ensure the accuracy and reliability of each sensor; the tag initialization module configures the ZETA low-power wide area network protocol to ensure the stability of data communication between the tag and the base station or the cloud platform. Specifically, in the tag initialization module, the sensor calibration algorithm is an important means to ensure the accuracy and reliability of each sensor. Each sensor may have a certain measurement error or be affected by the external environment (such as temperature, humidity, electromagnetic interference, etc.) when it leaves the factory. Therefore, during the system initialization phase, the sensor needs to be calibrated to improve the accuracy and consistency of the data. The purpose of calibration is to compensate for the error of the sensor, eliminate or reduce the interference of influencing factors, and ensure that the signal output by the sensor is more accurate. During the calibration process, it is necessary to collect a set of reference data under known conditions, such as known temperature values, known location information, etc.; through the collected data, an algorithm (such as least squares method, maximum likelihood estimation, etc.) is used to establish a sensor error model. These algorithms will estimate the sensor deviation based on known reference data; compensate and adjust the sensor output in real time according to the error model to eliminate the deviation and improve measurement accuracy; during operation, the sensor may be affected by environmental changes, so the calibration algorithm also needs to be adaptive and able to adjust the calibration parameters in real time according to environmental changes to maintain the long-term stable performance of the sensor; 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 further includes performing denoising, interpolation and standardization processing on the collected multidimensional data, and using a Kalman filter algorithm to eliminate environmental interference or measurement errors;

[0043] Data format conversion and timing alignment are performed to ensure data consistency and timeliness. Specifically, the extended part of the data acquisition module involves preprocessing the collected multidimensional data, including denoising, interpolation, standardization, data format conversion and timing alignment, etc., to ensure the accuracy and consistency of subsequent data processing. Specifically, the above steps can effectively eliminate environmental noise, fill in data gaps, and make data from different sources consistent in format and time scale. During the data acquisition process, the sensor signal may be affected by environmental noise, errors of the sensor itself or other external interference, resulting in certain deviations or noise in the collected data. Therefore, after data acquisition, these data need to be denoised first. The goal of denoising is to extract the effective components of the signal while eliminating useless noise to ensure 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 information and observation information. In the present invention, Kalman filtering is used to eliminate environmental interference or measurement errors. The Kalman filter algorithm corrects the data in each iteration by combining the current observation value and the predicted value, thereby obtaining a more accurate signal. During the data acquisition process, data loss may occur due to sensor failure, transmission problems, etc. In this case, interpolation technology can infer the missing parts through existing data. The goal of interpolation is to ensure the integrity of the data, so that the time intervals or spatial intervals between all data points are consistent, and avoid inaccuracies caused by missing data. Interpolation methods include linear interpolation: connecting the values ​​between missing data points with straight lines according to the trend of data points; spline interpolation: generating smooth interpolation curves through polynomial fitting, which is 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 these data are usually different. The purpose of standardization is to convert data of different dimensions into a unified standard form so that each data dimension has the same measurement scale, which is convenient for subsequent analysis and processing; in the process of multidimensional data acquisition, data from different sensors may have different formats (such as different units, data structures, etc.), and the acquisition time may also be different. Therefore, data format conversion and timing alignment must be performed to ensure that all data are consistent in time and space. Timing alignment refers to adjusting the timestamps of different sensor data through interpolation or synchronization algorithms so that data from different sensors can be compared and analyzed within the same time window.

[0044] The present invention is further configured 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 through the triangulation positioning algorithm to optimize the positioning results and improve the positioning accuracy.

[0046] The present invention is further configured such that the signal arrival time algorithm measures the propagation time of the signal from the base station to the tag and calculates the distance in combination with the known signal propagation speed, and the time difference algorithm measures the time difference of the tag signal received by different base stations to infer the relative distance between the tag and each base station, and uses a multi-base station collaborative strategy to use multiple base stations to jointly calculate the position of the tag to improve the positioning accuracy; specifically, in the present invention, the combination of the signal arrival time (TOA) algorithm and the time difference (TDOA) algorithm utilizes the time characteristics of the signal propagation between the base station and the tag, calculates the distance between the tag and the base station through different algorithms, and finally achieves high-precision positioning. Specifically, the signal arrival time algorithm measures the propagation time of the signal from the base station to the tag, while the time difference algorithm calculates the relative position of the tag by the time difference of the signals between multiple base stations; the core principle of the signal arrival time 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 propagation speed of the signal can be estimated under known environmental conditions (usually the speed of light or the propagation speed of electromagnetic waves in the air). By measuring the propagation time of the signal from the base station to the 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 from the signal being transmitted by the tag to the signal being received by each base station. The time difference between the tag signals received by different base stations can be used to calculate the relative distance from the tag to different base stations, thereby determining the specific location of the tag. Since the distances between multiple base stations are known, the location of the tag can be inferred by measuring the time difference of the signals and using triangulation positioning algorithms or other geometric calculation methods; multi-base station collaborative positioning refers to the collaboration of multiple base stations, combining the signal arrival time (TOA) and time difference (TDOA) data obtained by each base station, and using certain algorithms (such as triangulation positioning algorithms, least squares methods, etc.) to calculate the location of the tag. Multi-base station collaboration can significantly improve positioning accuracy because it reduces the impact of positioning errors of a single base station and improves the reliability of position calculation;

[0047] 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 through a triangulation positioning algorithm. In two-dimensional space, the position of the tag is solved by a geometric method through the signal time difference of at least three base stations. In three-dimensional space, the position of the tag is solved by a geometric method through the signal time difference of at least four base stations. Specifically, in two-dimensional space, at least three base station time difference data are required to determine the precise position of the tag. Through the triangulation positioning method, based on the known base station position and the time difference of signal propagation, the position of the tag on the two-dimensional plane can be determined by using a geometric method. Using the triangulation method, the relative distance to each base station is obtained by calculating the signal arrival time difference. Then, according to the principle of geometry, three points are used to determine a unique point on a plane, that is, the position of the tag; in three-dimensional space, at least four base station time difference data are required to calculate the position of the tag. Through the time difference algorithm, the distance from the tag to each base station is calculated, and then the three-dimensional geometric method is used to further determine the position of the tag; through the time difference data of at least four base stations, the position of the tag can be determined in three-dimensional space. Through the three-dimensional geometric algorithm, the three-dimensional coordinates of the tag are accurately calculated by combining the known base station position and the distance data from the tag to each base station.

[0048] The present invention is further configured that the data transmission module uses data compression and dynamic channel allocation to optimize the data transmission process, reduce delays and increase data transmission rate; the data transmission module uses 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, and prevents 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 amount of data, thereby reducing the bandwidth and time required for transmission. Transmitting data in a smaller volume through a compression algorithm can significantly improve the data transmission rate, especially in the case of limited bandwidth or tight network resources, compression technology is particularly important; after data collection, the data transmission module first compresses the collected data. The compressed data volume is small, which can effectively reduce the required transmission time and bandwidth, and optimize transmission efficiency. This can be achieved through a variety of compression algorithms, such as lossless compression algorithms or compression technologies in specific fields; 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 can be reduced, and a higher data transmission rate can be ensured; the data transmission module monitors the network environment (such as bandwidth, signal quality, latency, etc.) and dynamically adjusts the channel allocation according to the network status. The adjustment may include selecting a more suitable frequency band, optimizing the modulation and demodulation mode, or allocating data loads between multiple channels to ensure the optimal transmission path and rate; in the data transmission process, security is crucial, especially when sensitive data or identity information is involved. In order to ensure that the data is not tampered with, leaked or attacked by a man-in-the-middle during transmission, the present invention adopts security measures such as AES encryption, hash algorithm and TLS / SSL protocol; Advanced Encryption Standard (AES) is a symmetric key encryption algorithm that is widely used to protect the confidentiality of data. In data transmission, AES encryption converts the original data into incomprehensible ciphertext by using an encryption key, and only the authorized party can decrypt and restore the data; in the data transmission module, AES encryption is used to encrypt the collected and compressed data. Even if the encrypted data is intercepted during transmission, it cannot be read or tampered by illegal visitors, ensuring the confidentiality of the data; the hash algorithm is a method of converting input data (such as messages or files) into a "digest" or "hash value" of fixed length. In data transmission, the hash algorithm 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 hash value of the data.After 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 damaged 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 identity authentication in Internet communications. The TLS / SSL protocol uses encryption technology to ensure the confidentiality and integrity of data and prevents man-in-the-middle attacks and replay attacks; the data transmission module uses the TLS / SSL protocol to encrypt the data transmission process during communication, and verifies the identities of both parties in communication through digital certificates to ensure data security. In addition, the TLS / SSL protocol provides an integrity verification function to ensure that the data has not been tampered with during transmission, and prevents attackers from using previous communication data to attack through a replay protection mechanism.

[0049] The present invention is further configured to further include an intelligent analysis module, which is used to perform real-time analysis and processing of data collected by sensors, and the analysis and processing content includes time series analysis, anomaly detection and trend prediction. The present invention is further configured that the intelligent analysis module uses the ARIMA model and the support vector machine algorithm to perform data pattern recognition and anomaly detection, and further combines the convolutional neural network to perform advanced feature extraction to enhance the intelligence and prediction ability of the label data. Specifically, time series analysis is a technology for analyzing the time sequence characteristics in data, which can help identify time-related patterns such as trends and seasonal changes from historical data; anomaly detection detects possible faults or abnormal behaviors in the system by identifying outliers in the collected data, such as sensor failures, data collection anomalies, etc.; trend prediction uses existing data to predict future trends, usually through statistical models and machine learning methods; ARIMA (AutoRegressive Integrated Moving Average) is a statistical model for time series prediction. The ARIMA model is suitable for processing time series data with certain trends and seasonality, and can predict future trends through autocorrelation, moving average and differential processing in historical data; the ARIMA model first establishes a time series model based on the data collected by the sensor, removes the trend in the data by analyzing the autocorrelation and differential of the data, and smoothes the data fluctuations through moving average. Finally, the ARIMA model is used to predict future data to help analyze the future change trend of the equipment or system; support vector machine (SVM) is a machine learning algorithm based on statistical learning theory, which is widely used in classification and regression problems. In the present invention, SVM is mainly used for anomaly detection and data pattern recognition, which can effectively distinguish normal data from abnormal data from complex data; support vector machine classifies data by finding the best hyperplane. In anomaly detection, SVM distinguishes normal data from abnormal data by constructing a decision boundary. For anomaly detection, SVM automatically identifies abnormal patterns in 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 the present invention, CNN is used for high-level feature extraction, which can automatically learn and extract potential useful features from sensor data, thereby enhancing the intelligence and predictive ability of the data; the convolutional neural network automatically extracts hierarchical features from the raw sensor data through multiple convolutional layers, pooling layers and fully connected layers. In time series data or other forms of sensor data, CNN can identify potential patterns and convert them into useful features for subsequent anomaly detection or trend prediction.

[0050] The present invention is further configured to include a fault detection module, which monitors the running state of the tag in real time by introducing a fault detection algorithm. When a fault is detected, the terminal is ensured to continue to run through a redundant system or a backup mechanism. At the same time, a self-recovery algorithm and a local repair strategy are used to reduce the impact of the fault on the terminal and ensure the high availability of the terminal. Specifically, the fault detection module is used to monitor the running state of the tag in real time, and take corresponding measures when a fault is detected to ensure the high availability of the system. The fault detection module includes the following key functions: the fault detection algorithm is used to monitor the running state of the tag in real time, including battery status, communication stability, sensor accuracy, etc. Through real-time monitoring of data, the system can detect potential faults in time; redundant system or backup mechanism, when a fault occurs, the system will ensure that the terminal device can continue to run through redundant design or backup mechanism. The redundant system ensures that the system can still run when a component fails by using spare equipment or components. When a fault occurs, the system can automatically repair it through a self-recovery algorithm, or use a local repair strategy to reduce the impact of the fault. This can help the device quickly restore normal working state and reduce downtime.

[0051] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of 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, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). 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 contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0052] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific 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 refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0054] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0055] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

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

[0057] In the several embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

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

[0059] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0060] If the functions are implemented in the form of 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 the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0061] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A tracking label terminal based on ZETA technology, characterized in that: include: Tag initialization module, data acquisition module, data fusion module, identity authentication module, positioning module and data transmission 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 collect multi-dimensional data in real time through a multi-dimensional sensor according to a preset time interval; The data fusion module is used to fuse the multidimensional data collected by the multidimensional sensor; The identity authentication module is used to assign a unique identity identifier to each tag, ensure the uniqueness of the tag in the network, and authenticate the tag through an identity authentication mechanism based on a 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 and the cooperation of multiple base stations to calculate the precise position of the tag; The data transmission module is used to upload the collected and processed multi-dimensional sensor data to the base station or cloud platform through the ZETA protocol.

2. A tracking tag terminal based on ZETA technology according to claim 1, characterized in that: The tag initialization module configures the sensor calibration algorithm 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. The 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 collected multidimensional data, and uses a Kalman filter algorithm to eliminate environmental interference or measurement errors; Perform data format conversion and timing alignment to ensure data consistency and timeliness.

4. The tracking tag terminal based on ZETA technology according to claim 1, characterized in 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; The two-dimensional or three-dimensional coordinates of the tag are determined through the triangulation positioning algorithm to optimize the positioning results and improve the positioning accuracy.

5. The tracking label terminal based on ZETA technology according to claim 4, characterized in that: The signal arrival time algorithm measures the propagation time of the signal from the base station to the tag and calculates the distance based on the known signal propagation speed. The time difference algorithm measures the time difference between different base stations receiving the tag signal and calculates the relative distance between the tag and each base station. Through the multi-base station collaboration strategy, multiple base stations are used to jointly calculate the location of the tag to improve the 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 through a triangulation positioning algorithm. In two-dimensional space, the position of the tag is solved by a geometric method through the signal time difference of at least three base stations. In three-dimensional space, the position of the tag is solved by a geometric method through the signal time difference of at least four base stations.

6. The tracking tag terminal based on ZETA technology according to claim 1, characterized in that: The data transmission module uses data compression and dynamic channel allocation to optimize the data transmission process, reduce delays and increase data transmission rates; the data transmission module uses AES encryption and hash algorithms to ensure the security and integrity of data during transmission, and uses TLS / SSL protocols to ensure security during data transmission to prevent man-in-the-middle attacks and replay attacks.

7. The 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 on the data collected by the sensor. The analysis and processing content includes time series analysis, anomaly detection and trend prediction.

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

9. The tracking tag terminal based on ZETA technology according to claim 1, characterized in that: It also includes 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 redundant system or backup mechanism is used to ensure the continuous operation of the terminal. At the same time, a self-recovery algorithm and a local repair strategy are used to reduce the impact of the fault on the terminal and ensure the high availability of the terminal.

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