A magnetic field data monitoring and analyzing method of a passive tag, a passive tag, a device, and a product

Abstract

The application provides a magnetic field data monitoring and analyzing method of a passive tag, the passive tag, equipment and products, and relates to the field of logistics. Through a radio frequency energy power supply mechanism, a threshold voltage starting monitoring mechanism and a magnetic field threshold value triggering sampling mechanism, the passive tag can intelligently perceive magnetic field abnormal events without a battery, has a long service life and no maintenance cost, and overcomes the defects of high cost, battery replacement, complex maintenance and inadaptability to complex environments of an active sensing tag. The passive tag can still maintain listening in a low energy environment, consumes energy for collection when an abnormal event occurs, and ensures that the energy is used for valuable event sampling. The method enables the passive tag to not only provide static information such as cargo information and shelf information, but also lays a data foundation for background intelligent analysis of environmental abnormal events such as illegal interference, dumping, collision and illegal opening during the logistics transportation or storage process.

Description

Technical Field

[0001] This application relates to the field of logistics, and in particular to a method for monitoring and analyzing magnetic field data of a passive tag, as well as the passive tag, equipment, and product. Background Technology

[0002] Active logistics tags have problems such as requiring regular battery replacements, high operating costs, complex maintenance, and inability to adapt to complex environments, making them unsuitable for large-scale daily transportation.

[0003] During the logistics process, goods may encounter abnormal environmental events such as unauthorized interference, dumping, collisions, and unauthorized unsealing during transportation. Traditional barcodes or ordinary RFID tags can only record static information such as cargo information and shelf information, and cannot detect dynamic abnormal environmental events during transportation or storage. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, equipment, medium, and product for monitoring and analyzing magnetic field data using passive tags, which can effectively identify abnormal events during transportation.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for monitoring magnetic field data using a passive tag, comprising: The magnetometer chip monitors the energy storage voltage obtained from radio frequency energy conversion; When the energy storage voltage reaches the first threshold voltage, the comparison module is activated; the comparison module is used to monitor the surrounding magnetic field; when the change in magnetic field exceeds the magnetic field threshold, the acquisition module is activated; the acquisition module is used to acquire magnetic field data. The storage module stores the magnetic field data, which represents an abnormal event in the dynamic environment.

[0006] Optionally, the method for monitoring the magnetic field data of the passive tag further includes: Receive a forced wake-up command; The acquisition module is activated, and the acquisition module acquires the magnetic field data, which is also used as the node magnetic field. Store the magnetic field data.

[0007] Optionally, the method for monitoring the magnetic field data of the passive tag further includes: The magnetic field data is encrypted using the AES-128 algorithm through an encryption module, and the data layer ciphertext is output. The data layer ciphertext is encapsulated using a lightweight XOR operation and an integrity check value based on a key hash algorithm is attached.

[0008] Optionally, the comparison module is used to monitor the surrounding magnetic field; when the change in magnetic field exceeds a magnetic field threshold, the acquisition module is activated; the acquisition module's steps for acquiring magnetic field data include: Within the sliding window period, calculate the standard deviation and peak-to-peak value of the magnetic field strength; When the standard deviation is greater than the first magnetic field threshold and the peak-to-peak value is greater than the second magnetic field threshold, the acquisition module in the magnetometer chip is activated to acquire the magnetic field data.

[0009] Optionally, the method for monitoring the magnetic field data of the passive tag further includes: The magnetometer chip monitors the magnetic field data; When unauthorized access to the magnetic field data is detected, random data is written to overwrite the magnetic field data.

[0010] Optionally, the steps following storing the magnetic field data further include: The acquisition module is controlled to enter a sleep state, and the comparison module monitors the surrounding magnetic field.

[0011] Secondly, this application provides a method for analyzing magnetic field data, including: Obtain the magnetic field data described in any of the above steps; Feature extraction is performed on the magnetic field data to generate multidimensional magnetic field features; the magnetic field features include triaxial magnetic field strength, direction vector, and spectrum. Based on the magnetic field characteristics and the pre-stored vector model, the three-axis magnetic field strength change rate, the three-axis direction vector change rate, and the spectrum change rate are calculated. The abnormal event is matched by combining the three-axis magnetic field strength change rate, the three-axis direction vector change rate, and the spectrum change rate.

[0012] Thirdly, this application provides a passive tag, characterized in that it integrates a magnetometer chip, the magnetometer chip including a comparison module, a data acquisition module, and a storage module; The magnetometer chip monitors the energy storage voltage obtained from radio frequency energy conversion; The comparison module is activated when the energy storage voltage reaches a first threshold voltage; the comparison module is used to monitor the surrounding magnetic field. The acquisition module is activated when the change in magnetic field exceeds a magnetic field threshold; the acquisition module is used to acquire magnetic field data. The storage module stores the magnetic field data, which represents an abnormal event in the dynamic environment.

[0013] Fourthly, this application provides a processing apparatus, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the passive tag magnetic field data monitoring method described in any one of the first aspects or the steps of the magnetic field data analysis method described in the second aspect.

[0014] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the passive tag magnetic field data monitoring method described in any one of the first aspects or the steps of the magnetic field data analysis method described in the second aspect.

[0015] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method, passive tag, device, and product for monitoring and analyzing magnetic field data using passive tags. Through a radio frequency power supply mechanism, a threshold voltage-triggered monitoring mechanism, and a magnetic field threshold-triggered sampling mechanism, the passive tag can intelligently sense abnormal magnetic field events without batteries. It boasts a long lifespan and zero maintenance costs, overcoming the shortcomings of active sensing tags, such as high cost, the need for battery replacement, complex maintenance, and inability to adapt to complex environments. The passive tag can maintain listening even in low-energy environments, consuming energy to collect data when anomalies occur, ensuring that energy is used for valuable event sampling. This method enables passive tags not only to provide static information such as cargo and shelf information, but also to lay the data foundation for intelligent background analysis of environmental anomalies such as illegal interference, tipping, collisions, and unauthorized unsealing during logistics transportation or storage. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a method for monitoring magnetic field data of a passive tag, provided in an embodiment of this application; Figure 2 This is a schematic diagram of radio frequency energy flow; Figure 3 A schematic diagram illustrating the positional relationship between a passive tag and a metal adapter structure provided in an embodiment of this application; Figure 4 A schematic diagram illustrating the process of passive tag and reader interacting with magnetic field data according to an embodiment of this application; Figure 5Magnetic field data collected for embodiments of this application; Figure 6 A schematic diagram of the rate of change of triaxial magnetic field strength provided in another embodiment of this application; Figure 7 A schematic diagram of the rate of change of a three-axis direction vector is provided for another embodiment of this application; Figure 8 A schematic diagram of the rate of change of spectrum provided in another embodiment of this application; Figure 9 A graph showing the change in magnetic field strength when a magnet approaches a passive tag, as provided in another embodiment of this application; Figure 10 A diagram showing the change in magnetic field gradient when a magnet approaches a passive tag, provided in another embodiment of this application; Figure 11 A schematic diagram illustrating the short-term fluctuation of the magnetic field when a passive tag is struck by another object, as provided in another embodiment of this application; Figure 12 A schematic diagram illustrating the standard deviation fluctuation of the magnetic field strength when a passive tag is struck by another object, as provided in another embodiment of this application. Figure 13 The passive tag provided in another embodiment of this application was subjected to changes in magnetic field strength during an abnormal environmental event when it was tipped over; Figure 14 A schematic diagram showing the result of calculating the tilt angle based on the change in magnetic field strength when a passive tag is subjected to an abnormal environmental event during tilting, as provided in another embodiment of this application; Figure 15 A schematic diagram of the initial attitude vector of a passive tag when it is not tipped over, provided as another embodiment of this application; Figure 16 A schematic diagram of the attitude vector of a passive tag after it is tipped over, provided in another embodiment of this application, when an abnormal environmental event occurs during tipping. Figure 17 A schematic diagram of adaptive calibration using historical data to adaptively calibrate metal eddy current interference, provided for another embodiment of this application; Figure 18 This is a schematic diagram of error analysis for adaptive calibration of metal eddy current interference using historical data, provided as another embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Please see Figure 1 In a first aspect, this application provides a method for monitoring magnetic field data using a passive tag, comprising: The S1 magnetometer chip monitors the energy storage voltage obtained from the conversion of radio frequency energy.

[0021] Please see Figure 2 In this step, radio frequency (RF) energy is emitted by the reader / writer, received by the antenna module, and then converted into direct current (DC) by the rectifier module. The DC power is then regulated by the power management module to output a 3.3V energy storage voltage. The antenna module uses a UHF band (860-960 MHz) microstrip antenna with 50Ω impedance matching. The rectifier module uses a Schottky diode (e.g., SS14) bridge rectifier + filter capacitor + inductor (L1) to efficiently convert the RF signal into DC power. The power management module uses an AMS1117 low-dropout regulator (LDO) to output a stable 3.3V voltage; a supercapacitor is used as an instantaneous energy buffer and energy storage unit to ensure stable power supply when the magnetometer chip is woken up.

[0022] Please see Figure 3 In some embodiments, the passive tag also has an external metal adapter structure, including a metal isolation layer (nickel-copper alloy / aluminum alloy) and an anti-metal encapsulation layer (foam / ceramic). Through electromagnetic shielding and impedance matching networks, the passive tag can operate stably even when placed within 5cm of a metal surface. For example, the passive tag can be placed on a metal shelf. The antenna is positioned close to the metal adapter structure, while the main control chip and magnetometer chip are located on the side of the antenna away from the metal adapter structure. The main control chip can integrate an encryption module and a storage module.

[0023] When the energy storage voltage reaches the first threshold voltage, the comparison module is activated; the comparison module is used to monitor the surrounding magnetic field; when the change in magnetic field exceeds the magnetic field threshold, the acquisition module is activated; the acquisition module is used to acquire magnetic field data.

[0024] In this step, when the energy storage voltage that the power management module can output reaches the first threshold voltage (3.3V), the comparison module of the magnetometer chip is activated to monitor the surrounding magnetic field.

[0025] The magnetometer chip monitors the current magnetic field and compares it with a pre-stored vector model. When the detected change in the magnetic field exceeds a magnetic field threshold, the acquisition module in the magnetometer chip is activated to collect magnetic field data. Please refer to [link to relevant documentation]. Figure 5 .

[0026] The vector model can be a baseline vector model. The baseline vector model is established by collecting magnetic field data for specific time periods (including 10 seconds, 15 seconds, 20 seconds, 25 seconds, and 30 seconds) under a known safe environment, creating a baseline vector model B0=(Bx0,By0,Bz0,T0), which is stored in the storage area of ​​the storage module. This storage area can be an OTP (One-Time Programmable) area. The characteristics of this storage area determine that the baseline model cannot be erased or modified by any electrical operation after being written, thus providing an immutable and reliable baseline for subsequent physical state anomaly monitoring. The vector model can also be a secondary vector model. The secondary vector model is the magnetic field data that needs frequent updating and reading from the last collection, stored in the EEPROM area. The secondary vector model can also be magnetic field data collected for specific time periods (including 10 seconds, 15 seconds, 20 seconds, 25 seconds, and 30 seconds) under a known safe environment, stored in the EEPROM area; there are no restrictions on this. Bx0 represents the X-axis magnetic field data, By0 represents the Y-axis magnetic field data, Bz0 represents the Z-axis magnetic field data, and T0 represents the temperature in degrees Celsius.

[0027] It should be noted that the magnetic field in this step is generated by the magnetic seal strip, and the magnetometer chip records the magnetic field generated by the magnetic seal strip to establish a vector model. When an abnormal environmental event occurs, the magnetic field generated by the magnetic seal strip is often disturbed, resulting in drastic changes. Upon detecting this change, the comparison module activates the acquisition module to sample and record the changed magnetic field as magnetic field data. The magnetic field data includes the changed X-axis magnetic field data, Y-axis magnetic field data, Z-axis magnetic field data, and the temperature at that moment. After analysis by the background system, the environmental abnormal events that occurred during this period can be determined.

[0028] Magnetic field thresholds can be multidimensional thresholds: A sudden increase in magnetic field strength of >100μT and a gradient of >50μT / cm on one axis corresponds to illegal interference, such as a magnet being brought close to a passive tag.

[0029] The triaxial magnetic field fluctuates violently for a short time (<1s) (standard deviation >80μT). This threshold standard corresponds to a collision, such as when a passive tag is hit by another object.

[0030] A reversal of the magnetic field direction or a sudden drop in intensity (greater than 200 μT) corresponds to unauthorized unsealing, such as the removal of the magnetic seal.

[0031] The change in the direction of the three-axis magnetic field vector is used to calculate the flip angle of the passive tag. For example, when the flip angle is greater than or equal to 70°, this threshold corresponds to an abnormal event in the tilting environment, such as goods tipping over or tilting.

[0032] Spectral characteristics are used to identify vibrations at specific frequencies. The spectral rate of change is calculated by performing a Fourier Transform (FFT) on a magnetic field sequence over a period of time, analyzing its frequency components, and identifying vibrations at specific frequencies (such as engine operation) or distinguishing between random noise and periodic interference, such as slight disturbances caused by minor vibrations in the surrounding environment (construction nearby) or transportation (car engine running). For example, the spectral characteristics of the magnetic field under a particular vibration are recorded only once; in subsequent recordings, this vibration is considered an interference term and is not recorded again. The threshold for the spectral rate of change can be set to 30 μT; vibrations with a value less than 30 μT are considered interference terms and are not recorded.

[0033] It should be noted that the magnetic field threshold can be flexibly adjusted according to the environment.

[0034] For example, the following standards can also be used when monitoring collisions: S21 calculates the standard deviation and peak-to-peak value of the magnetic field strength within the sliding window period.

[0035] Calculate the standard deviation and peak-to-peak value of the magnetic field strength within one second.

[0036] S22 When the standard deviation is greater than the first magnetic field threshold and the peak-to-peak value is greater than the second magnetic field threshold, the acquisition module in the magnetometer chip is activated to acquire magnetic field data.

[0037] When the standard deviation is greater than 80 μT and the peak-to-peak value is greater than 300 μT, an abnormal environmental event such as a collision is highly probable. At this point, the acquisition module is activated to record the magnetic field data. It should be noted that the sensitivity of the magnetometer chip can be adjusted by regulating the magnetic field threshold. A higher magnetic field threshold makes the acquisition module less likely to be woken up, resulting in more energy-efficient passive tags. Conversely, a lower magnetic field threshold makes the acquisition module easier to wake up, allowing the passive tags to record more changes and providing a richer data base. The first magnetic field threshold ranges from 70 μT to 90 μT, including 70 μT, 75 μT, 80 μT, 85 μT, and 90 μT. The second magnetic field threshold ranges from 290 μT to 310 μT, including 290 μT, 295 μT, 300 μT, 305 μT, and 310 μT.

[0038] Using a dual-threshold standard for a single event can more accurately identify the types of abnormal environmental events.

[0039] The S3 storage module stores magnetic field data.

[0040] It should be noted that magnetic field data can also include shelf information, cargo information, and the acquisition time of the vector model. In some scenarios with high security requirements, it can also include information such as tag keys and tag unique TIDs.

[0041] The S4 encryption module uses the AES-128 algorithm to encrypt the magnetic field data and outputs ciphertext in the data layer.

[0042] When generating tag keys, a unique tag key K_tag = HKDF(RootKey, TID, "RFID-TAG", 128bit) is derived from the root key and the tag's unique TID (96 bits) using the HKDF algorithm. The backend system distributes the tag key via a secure channel (TLS), and passive tags subsequently store the tag key in the storage area. The magnetic field data is encrypted using the AES-128 algorithm using the tag key, outputting ciphertext at the data layer. This step implements the "one tag, one key" concept in the logistics tag scenario. The tag internally maintains a 16-bit monotonically increasing counter, incrementing the serial number by 1 for each successful read / write operation.

[0043] The magnetic field data is encrypted, and the magnetic field data includes (Bx, By, Bz, temperature) totaling 64 bits. The AES-128-CTR algorithm mode is used, and the unique tag TID and the acquisition time of the vector model are connected in parallel. At this time, the output data layer ciphertext is (Bx, By, Bz, temperature) || unique tag TID || acquisition time of the vector model, totaling 80 bits.

[0044] S5 encapsulates the data layer ciphertext using a lightweight XOR operation and adds an integrity check value based on a key hash algorithm.

[0045] The data layer ciphertext is encapsulated by a lightweight XOR operation using a unique tag key to generate HMAC-SHA256 hash ciphertext, and the first 32 bits are extracted as the checksum.

[0046] Optionally, in some embodiments, the method for monitoring the magnetic field data of passive tags further includes: The magnetometer chip monitors magnetic field data.

[0047] When unauthorized access to the magnetic field data is detected, random data is written to overwrite the magnetic field data.

[0048] Erases all keys and temporary data in RAM within 0-10ms, and continuously writes random data to the storage area three times within 10-50ms (compliant with DoD 5220.22-M standard). First time: filled with all 0xFF; second time: filled with all 0x55; third time: filled with all 0xAA.

[0049] In some embodiments, after 50ms: a command is issued to blow the internal physical fuse (Poly-Fuse), permanently disabling access.

[0050] In some embodiments, when unauthorized access to magnetic field data is detected, a fuse-breaking command is sent to the power management module.

[0051] At the hardware level, this is achieved by using the transient energy (>3.3V) stored in the supercapacitor. When unauthorized access is encountered, the voltage is instantly boosted to 5V, which breaks down the floating gate transistor in the storage area, thus achieving physical erasure.

[0052] Please see Figure 4 The hardware design incorporates the AES128 / RSA algorithm within the encryption module, with the key stored in the secure OTP / EEPROM area of ​​the encryption module. The magnetometer chip and encryption module work together to monitor physical state changes, triggering data erasure or communication disabling upon detection of unpacking / flipping; combined with protection mechanisms to prevent unauthorized reading. A TLS / SSL encrypted channel is embedded above the EPC Gen2 protocol layer, with bidirectional authentication between the reader / writer and the passive tag to prevent man-in-the-middle attacks.

[0053] Optionally, the method for monitoring magnetic field data using passive tags also includes: Receive forced wake-up commands. For example, receive custom commands sent from the outside (such as the `0xE0` command).

[0054] The acquisition module is activated, and it collects magnetic field data. This magnetic field data is also used as the nodal magnetic field data; the acquisition module is then awakened to complete real-time measurements.

[0055] Store magnetic field data. Store the magnetic field data in the storage area.

[0056] This step is applicable in critical logistics nodes (such as warehousing, transit, and handover) where it's necessary to proactively check the current status of goods (e.g., whether they are tilted, whether the environmental magnetic field is normal). During deployment or maintenance, a forced wake-up command can be sent to test the tag's sensing function without artificially creating an "impact" or "strong magnetic field" to trigger it. Before deployment in a new environment, this mode can be used to collect the background magnetic field data to assess whether the environment is suitable for deployment or if the magnetic field threshold needs adjustment. Before signing the handover document, both parties can use a handheld reader to proactively read the current status (e.g., tilt angle) of the passive tag as an electronic certificate of the goods' integrity. The magnetic field data recorded at this time can be used as the node's magnetic field as a handover certificate.

[0057] This invention provides a complete solution that combines "unattended anomaly monitoring" and "on-demand proactive status verification" by integrating an event-triggered automatic monitoring mode with an instruction-triggered proactive query mode. These two modes complement each other in terms of energy utilization, data properties (passive or proactive collection), and application scenarios, enabling the passive tag to flexibly adapt to various complex logistics management needs, from automated monitoring to manual intervention at critical nodes.

[0058] Optionally, the steps following storing the magnetic field data may further include: The control acquisition module enters sleep mode, while the comparison module monitors the surrounding magnetic field.

[0059] By forcing the acquisition module to sleep while keeping the comparison module active after each data acquisition task is completed, this invention significantly reduces the static power consumption of passive tags. This ensures that, with limited radio frequency energy harvesting capabilities, passive tags can achieve a long standby monitoring lifespan and can be quickly woken up by abnormal environmental events or wake-up commands, thus solving the energy bottleneck problem of passive tags.

[0060] Secondly, this application provides a method for analyzing magnetic field data, including: B1 obtains magnetic field data from any embodiment of the first aspect.

[0061] In this step, a reader is used to transfer the magnetic field data (such as...) Figure 5 (As shown) Upload to the backend system.

[0062] B2 extracts features from magnetic field data to generate multidimensional magnetic field features; the magnetic field features include triaxial magnetic field strength, direction vector, and spectrum.

[0063] B3 calculates the triaxial magnetic field strength change rate (e.g., based on magnetic field characteristics and a pre-stored vector model) Figure 6 ), the rate of change of the three-axis direction vector (e.g. Figure 7 ), spectral change rate (e.g.) Figure 8 ).

[0064] The triaxial magnetic field strength change rate is calculated by dividing the difference between the current sampled value and the vector model by the time interval. It is used to characterize the drastic change in magnetic field strength. It is used to monitor sudden events, such as unauthorized interference or collisions. For example, a magnet approaching a passive tag or a passive tag being struck by another object. The triaxial direction vector change rate is calculated by calculating the angle θ between magnetic field vectors at adjacent moments: θ = arccos((B1·B2) / (|B1||B2|)), where B1·B2 is the dot product (inner product) of the two magnetic field vectors, and |B1| and |B2| are the magnitudes of the two magnetic field vectors, respectively. It is used to characterize changes in the posture of tags, such as flipping or tipping. For example, goods tipping over or tilting. The spectral change rate is calculated by performing a Fourier transform (FFT) on the magnetic field sequence over a period of time, analyzing its frequency components, identifying vibrations of specific frequencies (such as engine operation), or distinguishing between random noise and periodic interference, such as slight vibrations in the surrounding environment (construction nearby) or slight disturbances during transportation (car engine operation).

[0065] B4 combines the triaxial magnetic field strength change rate, triaxial direction vector change rate, and spectral change rate to match abnormal events.

[0066] Magnetic field thresholds can be multidimensional thresholds: Please see Figure 9 and Figure 10 A sudden increase in magnetic field strength of >100μT and a gradient of >50μT / cm on one axis corresponds to illegal interference, such as a magnet being brought close to a passive tag.

[0067] Please see Figure 11 and Figure 12 The triaxial magnetic field fluctuates violently for a short time (<1s) (standard deviation >80μT). This threshold standard corresponds to a collision, such as when a passive tag is hit by another object.

[0068] A reversal of the magnetic field direction or a sudden drop in intensity (greater than 200 μT) corresponds to unauthorized unsealing, such as the removal of the magnetic seal.

[0069] Please see Figure 13 , Figure 14 , Figure 15 , Figure 16 The change in the direction of the three-axis magnetic field vector is used to calculate the flip angle of the passive tag. For example, when the flip angle is greater than or equal to 70°, this threshold corresponds to an abnormal event in the tilting environment, such as goods tipping over or tilting.

[0070] Please see Figure 8 In the middle, vibrations recorded when the horizontal axis is 3, vibrations with a spectral change rate of less than 20μT are considered as interference terms and are no longer recorded.

[0071] It should be noted that the magnetic field threshold can be flexibly adjusted according to the environment.

[0072] In metallic environments, magnetic field drift caused by eddy current interference can be compensated based on historical data.

[0073] In some embodiments, the magnetic field data analysis method further includes security verification of the magnetic field data. When a hash value mismatch is detected, it is determined that the data has been tampered with, and the corresponding logistics tag is added to a blacklist, prohibiting subsequent access. When a CRC (Cyclic Redundancy Check) verification failure is detected, it is determined that there is a transmission error, and two retries are allowed. If it continues to fail, it is marked as "suspicious". When a serial number rollback is detected, it is determined to be a replay attack, and the session key is immediately revoked. The backend system has a separate isolated area, and the data of suspicious logistics tags is manually verified.

[0074] Thirdly, this application provides a passive tag, characterized in that it integrates a magnetometer chip, the magnetometer chip including a comparison module, a data acquisition module, and a storage module; The magnetometer chip monitors the energy storage voltage obtained from radio frequency energy conversion; The comparison module is activated when the energy storage voltage reaches a first threshold voltage; the comparison module is used to monitor the surrounding magnetic field. The acquisition module is activated when the change in magnetic field exceeds a magnetic field threshold; the acquisition module is used to acquire magnetic field data. A storage module stores the magnetic field data, which represents an abnormal event in a dynamic environment.

[0075] Fourthly, this application provides a processing apparatus, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the passive tag magnetic field data monitoring method described in any one of the first aspects or the steps of the magnetic field data analysis method described in the second aspect.

[0076] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the passive tag magnetic field data monitoring method described in any one of the first aspects or the steps of the magnetic field data analysis method described in the second aspect.

[0077] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0078] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0079] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0080] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for monitoring magnetic field data using a passive tag, characterized in that, The method for monitoring the magnetic field data of the passive tag includes: The magnetometer chip monitors the energy storage voltage obtained from radio frequency energy conversion; When the energy storage voltage reaches the first threshold voltage, the comparison module is activated; the comparison module is used to monitor the surrounding magnetic field; when the change in magnetic field exceeds the magnetic field threshold, the acquisition module is activated; the acquisition module is used to acquire magnetic field data. The storage module stores the magnetic field data, which represents an abnormal event in the dynamic environment.

2. The method for monitoring magnetic field data of a passive tag according to claim 1, characterized in that, The method for monitoring the magnetic field data of the passive tag also includes: Receive a forced wake-up command; The acquisition module is activated, and the acquisition module acquires the magnetic field data, which is also used as the node magnetic field. Store the magnetic field data.

3. The method for monitoring magnetic field data of a passive tag according to claim 1, characterized in that, The method for monitoring the magnetic field data of the passive tag also includes: The magnetic field data is encrypted using the AES-128 algorithm through an encryption module, and the data layer ciphertext is output. The data layer ciphertext is encapsulated using a lightweight XOR operation and an integrity check value based on a key hash algorithm is attached.

4. The method for monitoring magnetic field data of a passive tag according to claim 1, characterized in that, The comparison module is used to monitor the surrounding magnetic field; when the change in magnetic field exceeds the magnetic field threshold, the acquisition module is activated. The steps for acquiring magnetic field data using the acquisition module include: Within the sliding window period, calculate the standard deviation and peak-to-peak value of the magnetic field strength; When the standard deviation is greater than the first magnetic field threshold and the peak-to-peak value is greater than the second magnetic field threshold, the acquisition module in the magnetometer chip is activated to acquire the magnetic field data.

5. The method for monitoring magnetic field data of a passive tag according to claim 1, characterized in that, The method for monitoring the magnetic field data of the passive tag also includes: The magnetometer chip monitors the magnetic field data; When unauthorized access to the magnetic field data is detected, random data is written to overwrite the magnetic field data.

6. The method for monitoring magnetic field data of a passive tag according to claim 1, characterized in that, The steps following storing the magnetic field data also include: The acquisition module is controlled to enter a sleep state, and the comparison module monitors the surrounding magnetic field.

7. A method for analyzing magnetic field data, characterized in that, include: Obtain the magnetic field data as described in any one of claims 1-6; Feature extraction is performed on the magnetic field data to generate multidimensional magnetic field features; the magnetic field features include triaxial magnetic field strength, direction vector, and spectrum. Based on the magnetic field characteristics and the pre-stored vector model, the three-axis magnetic field strength change rate, the three-axis direction vector change rate, and the spectrum change rate are calculated. The abnormal event is matched by combining the three-axis magnetic field strength change rate, the three-axis direction vector change rate, and the spectrum change rate.

8. A passive tag, characterized in that, An integrated magnetometer chip, the magnetometer chip including a comparison module, a data acquisition module, and a storage module; The magnetometer chip monitors the energy storage voltage obtained from radio frequency energy conversion; The comparison module is activated when the energy storage voltage reaches a first threshold voltage; the comparison module is used to monitor the surrounding magnetic field. The acquisition module is activated when the change in magnetic field exceeds a magnetic field threshold; the acquisition module is used to acquire magnetic field data. The storage module stores the magnetic field data, which represents an abnormal event in the dynamic environment.

9. A processing apparatus, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the passive tag magnetic field data monitoring method according to any one of claims 1-6 or the steps of the magnetic field data analysis method according to claim 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the passive tag magnetic field data monitoring method according to any one of claims 1-6 or the steps of the magnetic field data analysis method according to claim 7.

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