An automated order management system based on geomagnetic detection

Abstract

This invention discloses an automated order management system based on geomagnetic detection, relating to the field of intelligent transportation. The system includes: an anti-interference geomagnetic sensing unit for outputting a stable vehicle presence status signal through environmental magnetic field disturbance suppression technology; a multi-source collaborative arbitration engine for fusing geomagnetic status signals with spatiotemporal coordinate data from video inspection equipment, triggering order lifecycle operations only when both verifications are consistent; a distributed fault-tolerant processing unit for initiating a multimodal review process and freezing abnormal orders in response to data conflict events; and an edge data chain storage unit for generating an immutable data chain in a weak network environment according to event sequence. This application upgrades the geomagnetic system from an isolated sensor to an intelligent terminal with environmental immunity, data autonomy, precise decision-making, and lifetime maintenance-free capabilities, providing a highly reliable technological foundation for city-level parking IoT.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation, and in particular to an automated order management system based on geomagnetic detection. Background Technology

[0002] Geomagnetic detection technology, as a core sensing method for roadside parking management, has been widely applied to vehicle entry and exit status recognition and automated order management. Its basic principle is to use high-sensitivity magnetic sensors to capture signal changes caused by vehicles disturbing the Earth's magnetic field, triggering order creation and termination. Current system architectures typically include geomagnetic sensor nodes, wireless transmission modules, and a cloud-based order management platform. With the increasing demands for parking management efficiency in smart cities, geomagnetic systems need to simultaneously address environmental adaptability and multi-source data collaboration issues to achieve high-precision order lifecycle management. However, existing technologies are limited by the physical bottlenecks of single-point detection modes, facing challenges such as interference from adjacent parking spaces and signal drift in complex urban roadside scenarios, necessitating algorithm optimization and architecture upgrades to improve reliability.

[0003] Current geomagnetic-based order management systems suffer from the following core defects: 1. Single geomagnetic sensors are susceptible to interference from the superposition of magnetic fields from adjacent parking spaces, leading to a high false alarm rate. 2. Geomagnetic sensors rely on wireless transmission such as NB-IoT, and signal attenuation in underground parking garages or densely populated urban areas results in a data packet loss rate exceeding 15%. Disordered event timing causes order status conflicts, and traditional retransmission mechanisms cannot guarantee data integrity, requiring on-site equipment calibration by maintenance personnel. 3. Existing systems lack an adaptive anomaly arbitration mechanism. When geomagnetic and video inspection data conflict, toll collectors must manually review and correct the order. 4. Geomagnetic sensors consume up to 1.2mA of power during continuous operation, with a battery life of less than 2 years; furthermore, external events such as construction machinery vibration and metal manhole cover displacement cause frequent false alarms, increasing annual maintenance costs by 37%. Therefore, based on the above challenges, this invention proposes an automated order management system based on geomagnetic detection. Summary of the Invention

[0004] Purpose of the invention

[0005] To address the aforementioned issues, the present invention aims to provide an automated order management system based on geomagnetic detection. This system eliminates interference from adjacent parking spaces and false triggering caused by electromagnetic noise, establishes an orderly event storage and conflict self-healing mechanism in a weak network environment, ensures the atomicity of order status changes, incorporates multi-source arbitration and intelligent fault-tolerant logic, and completely replaces manual order correction operations.

[0006] Technical solution

[0007] To achieve the above objectives, this invention provides an automated order management system based on geomagnetic detection. This system integrates a geomagnetic sensor with a spectral layered filtering unit to separate vehicle magnetic field characteristics from low-frequency power frequency environmental noise. The engine receives geomagnetic state signals and video coordinates, performing a three-level arbitration for anomaly detection. Order creation and termination are triggered only when the three levels of verification are consistent. In weak network conditions, chained storage ensures event ordering; conflicting events trigger adjacent geomagnetic cross-verification and video re-acquisition; if invalid, the order is frozen and the user is guided to confirm. Furthermore, the sampling frequency is dynamically switched based on the vehicle's presence status, effectively extending battery life. This solution upgrades the geomagnetic system from an isolated sensor to an intelligent terminal with environmental immunity, data autonomy, precise decision-making, and lifetime maintenance-free capabilities, providing a highly reliable technological foundation for city-level parking IoT.

[0008] In a first aspect, the present invention provides an automated order management system based on geomagnetic detection, comprising:

[0009] An anti-interference geomagnetic sensing unit is used to output a stable vehicle presence status signal through environmental magnetic field disturbance suppression technology;

[0010] The multi-source collaborative arbitration engine is used to fuse geomagnetic state signals and spatiotemporal coordinate data from video inspection equipment, and triggers order lifecycle operations only when the dual verifications are consistent.

[0011] The distributed fault-tolerant processing unit is used to initiate a multimodal review process for data conflict events and freeze abnormal orders;

[0012] Edge data chain storage unit is used to generate an immutable data chain in the order of events in a weak network environment;

[0013] The event chain storage conflict self-healing unit is used to trigger the self-healing process through a two-factor condition of relative time difference mutation amount and anchor point verification.

[0014] Furthermore, the anti-interference geomagnetic sensing unit integrates dynamic noise suppression function, which separates low-frequency environmental disturbances from vehicle magnetic field characteristics and suppresses power frequency electromagnetic interference, outputting a stable signal that meets the preset signal-to-noise ratio threshold.

[0015] Furthermore, the multi-source collaborative arbitration engine performs hierarchical verification, including time window matching of geomagnetic events and video recognition events, spatial consistency verification of video coordinates and target parking space geofences, and a statistical anomaly detection model built based on historical behavior data. Orders that deviate from the normal distribution are automatically marked as risky and frozen, and high-risk orders need to be verified and unfrozen.

[0016] Furthermore, the distributed fault-tolerant processing unit activates a multimodal review process when arbitration fails, including controlling the video equipment to reacquire the target parking space image and calling the geomagnetic sensors of adjacent parking spaces to cross-verify the status; if arbitration still fails, the order is frozen and audio-visual guidance is activated to encourage the car owner to actively confirm.

[0017] Furthermore, the chain nodes of the data chain contain state hash values ​​to enable offline data integrity verification.

[0018] Furthermore, the edge data chain storage unit is equipped with a conflict self-healing mechanism. When hash verification fails, it requests cloud data synchronization and simultaneously triggers adaptive calibration of geomagnetic sensor parameters.

[0019] Furthermore, the system is connected to a dynamic billing strategy executor, which matches regional rate rules based on the spatiotemporal attributes of the order to ensure that rate switching and geomagnetic state change events are atomically synchronized.

[0020] Furthermore, it also includes an energy consumption adaptive scheduling module that dynamically switches the sampling frequency based on the geomagnetic state and enters an ultra-low power consumption mode after the vehicle leaves.

[0021] Furthermore, the system integrates an external event collaboration interface and connects to the municipal construction management system, automatically shielding geomagnetic signals and switching to a backup detection mode during construction.

[0022] Furthermore, it also includes a spectrum-layered dynamic filtering unit, which processes the magnetic field signal by frequency band and determines valid vehicle events based on a dynamic energy ratio threshold.

[0023] Furthermore, the spectrum-layered dynamic filtering unit determines the effective signal based on a dynamic energy ratio threshold, using the following formula:

[0024]

[0025] In the formula, The dynamic energy ratio threshold; For vehicle frequency band energy; Energy in the noise frequency band; This is the temperature compensation coefficient.

[0026] An autonomous system for data integrity is constructed, ensuring absolute event temporality through hash anchor chain association. Two-factor collision detection accurately identifies weak network out-of-order data and malicious tampering, triggering a multi-level self-healing process to fundamentally resolve order status errors caused by asynchronous data in traditional systems. It endows edge devices with self-repair capabilities, achieving a paradigm shift towards intelligent fault tolerance in the system.

[0027] Secondly, the present invention also provides an automated order management method based on geomagnetic detection, the method being based on the system described in the first aspect above, comprising:

[0028] The original magnetic field signal is collected by a geomagnetic sensor, the magnetic field signal is decomposed into three characteristic frequency bands, and the dynamic energy ratio of the vehicle frequency band energy to the noise frequency band energy is calculated to generate vehicle events.

[0029] Receive license plate coordinates and timestamps reported by geomagnetic vehicle events and video inspection equipment, perform three-level hierarchical verification, and create or terminate parking orders only when the three-level verification is consistent.

[0030] In a weak network environment, an event chain structure is constructed according to time sequence, and the sudden change in the time difference between adjacent events is monitored in real time. When the conditions are met, a self-healing process is triggered, including discarding conflicting events, synchronizing previous authoritative data in the cloud, geomagnetic resampling calibration, and event chain reconstruction.

[0031] The sampling frequency is dynamically adjusted based on the vehicle's presence status as fed back by the geomagnetic field in real time, and the energy consumption strategy is updated synchronously after the order is terminated.

[0032] Thirdly, the present invention also provides a computer device, including a management platform and a memory, wherein the management platform is connected to the memory, the memory is used to store computer programs, and the management platform is used to execute the computer programs stored in the memory, so that the computer device executes the aforementioned order automation management method based on geomagnetic detection.

[0033] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a management platform, implements the aforementioned automated order management method based on geomagnetic detection.

[0034] This invention employs spectral hierarchical dynamic filtering technology to separate vehicle magnetic field characteristics from environmental noise, achieving full-temperature-range anti-interference detection. It constructs an event chain storage and a two-factor conflict self-healing mechanism to ensure data timeliness and integrity in weak network environments. A three-level arbitration engine is deployed, integrating geomagnetic state, video coordinates, and historical behavior models to perform hierarchical verification. Based on the vehicle's presence state, the sampling frequency is dynamically switched, achieving lifelong maintenance-free energy management. This solution eliminates false alarms from environmental noise, and the accuracy of magnetic field feature extraction approaches physical limits. It achieves system-level self-healing against event disorder and tampering attacks, eradicating data loss in weak networks. Multi-source arbitration suppresses order status error rates to negligible levels, completely replacing manual intervention. Adaptive energy consumption scheduling allows the geomagnetic equipment's lifespan to exceed battery chemical limits. It upgrades the geomagnetic system from an isolated sensor to an intelligent terminal with environmental immunity, data autonomy, precise decision-making, and lifelong maintenance-free capabilities, providing a highly reliable technological foundation for city-level parking IoT.

[0035] Beneficial effects

[0036] By implementing the geomagnetic detection-based automated order management system provided by the present invention, the following technical effects are achieved:

[0037] (1) This application uses a spatiotemporally aligned multimodal decision-making architecture to perform hierarchical verification by integrating geomagnetic state, video coordinates, and historical behavior models. The primary spatiotemporal window matching eliminates interference from equipment response delays, the secondary geofence verification blocks cross-berth binding errors, and the final statistical distribution analysis identifies abnormal orders. The three-level verification forms a logical closed loop, bringing the reliability of order creation and termination decisions close to the theoretical limit and completely replacing manual arbitration.

[0038] (2) A state-driven energy consumption control model is introduced, which dynamically switches the sampling frequency based on the vehicle presence status fed back by the geomagnetic field in real time. When there is no vehicle, it enters a low-power monitoring mode. When a vehicle enters, the frequency is increased to the full sampling rate. After the vehicle leaves, the frequency is reduced after a delay to avoid misjudgment. This mechanism breaks through the limitation of battery life on deployment density and realizes zero-maintenance operation throughout the system's entire life cycle.

[0039] (3) By using frequency domain feature decoupling and adaptive noise suppression, the interference of environmental magnetic field disturbances on geomagnetic detection is completely eliminated. It decomposes the original signal into characteristic frequency bands, enhances the vehicle magnetic field characteristic band, and performs dynamic filtering in the noise band, so that the detection results are decoupled from the environmental temperature and humidity and electromagnetic field strength, realizing signal stability under all working conditions, and laying the physical foundation for high-precision order management.

[0040] (4) Construct an autonomous system for data integrity, ensuring absolute temporal order of events through hash anchor chain association. Two-factor collision detection accurately identifies weak network disorder and malicious tampering, triggering a multi-level self-healing process to eradicate order status disorder caused by asynchronous data in traditional systems. It endows edge devices with data self-repair capabilities, realizing a paradigm leap in intelligent fault tolerance of the system. Attached Figure Description

[0041] To make the above-described automated order management system based on geomagnetic detection of the present invention more apparent and understandable, the accompanying drawings used in the specific embodiments of the present invention 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 from these drawings without creative effort.

[0042] Figure 1 This diagram illustrates a multi-source, three-level arbitration engine.

[0043] Figure 2 This is a flowchart illustrating the method described in this application. Detailed Implementation

[0044] Example 1:

[0045] An automated order management system based on geomagnetic detection is provided, comprising: an anti-interference geomagnetic sensing unit for outputting a stable vehicle presence status signal through environmental magnetic field disturbance suppression technology; a multi-source collaborative arbitration engine for fusing geomagnetic status signals with spatiotemporal coordinate data from video inspection equipment, triggering order lifecycle operations only when both verifications are consistent; a distributed fault-tolerant processing unit for initiating a multimodal review process and freezing abnormal orders in response to data conflict events; an edge data chain storage unit for generating an immutable data chain in a weak network environment according to event sequence; and an event chain storage conflict self-healing unit for triggering a self-healing process based on a two-factor condition of relative time difference mutation amount and anchor point verification. Details are as follows.

[0046] The anti-interference geomagnetic sensing unit integrates dynamic noise suppression function, which separates low-frequency environmental disturbances from vehicle magnetic field characteristics and suppresses power frequency electromagnetic interference, outputting a stable signal that meets the preset signal-to-noise ratio threshold.

[0047] The multi-source collaborative arbitration engine, such as Figure 1 As shown, hierarchical verification is performed, including time window matching of geomagnetic events and video recognition events, spatial consistency verification of video coordinates and target parking space geofences, and a statistical anomaly detection model built based on historical behavior data. Orders that deviate from the normal distribution are automatically marked as risky and frozen, and high-risk orders need to be verified and unfrozen.

[0048] When arbitration fails, the distributed fault-tolerant processing unit activates a multimodal review process, including controlling the video equipment to reacquire the target parking space image and calling the geomagnetic sensors of adjacent parking spaces to cross-verify the status; if arbitration still fails, the order is frozen and audio-visual guidance is activated to encourage the car owner to actively confirm.

[0049] The chain nodes of the data chain contain state hash values ​​to enable offline data integrity verification.

[0050] The edge data chain storage unit is equipped with a conflict self-healing mechanism. When hash verification fails, it requests cloud data synchronization and triggers adaptive calibration of geomagnetic sensor parameters.

[0051] The system is connected to a dynamic billing strategy executor, which matches regional rate rules based on the spatiotemporal attributes of the order to ensure that rate switching and geomagnetic state change events are atomically synchronized.

[0052] It also includes an energy consumption adaptive scheduling module that dynamically switches the sampling frequency based on the geomagnetic state and enters an ultra-low power consumption mode after the vehicle leaves.

[0053] The system integrates an external event collaboration interface and connects to the municipal construction management system, automatically shielding geomagnetic signals and switching to a backup detection mode during construction.

[0054] A method for automated order management based on geomagnetic detection is also provided, the process of which is as follows: Figure 2 As shown, the process includes: acquiring raw magnetic field signals through geomagnetic sensors, decomposing the magnetic field signals into three characteristic frequency bands, calculating the dynamic energy ratio of vehicle frequency band energy to noise frequency band energy, and generating vehicle events; receiving geomagnetic vehicle events and license plate coordinates and timestamps reported by video inspection equipment, performing three-level hierarchical verification, and creating or terminating parking orders only when the three-level verification is consistent; constructing an event chain structure in a time sequence under weak network conditions, monitoring the sudden change in time difference between adjacent events in real time, and triggering a self-healing process of discarding conflicting events, synchronizing previous authoritative data in the cloud, geomagnetic resampling calibration, and event chain reconstruction when conditions are met; dynamically adjusting the sampling frequency based on the real-time feedback of vehicle presence status from geomagnetic sensors, and updating the energy consumption strategy synchronously after the order is terminated.

[0055] Example 2:

[0056] Based on the aforementioned embodiments, a frequency-band adaptive filtering architecture is proposed to address the problem of geomagnetic signals being interfered with by environmental noise.

[0057] The architecture decomposes the magnetic field signal into three characteristic frequency bands:

[0058] Low frequency band (0-5Hz): temperature drift, mechanical vibration noise, Kalman predictive filtering;

[0059] Power frequency band (48-52Hz): Electromagnetic interference from power grids and elevators, adaptive notch filtering;

[0060] Vehicle characteristic band (10-30Hz): metal body disturbance, bandpass enhancement.

[0061] The effective signal is determined by the dynamic energy ratio threshold, using the following formula:

[0062]

[0063] In the formula, The dynamic energy ratio threshold; For vehicle frequency band energy; Energy in the noise frequency band; This is the temperature compensation coefficient.

[0064] when Vehicle events are triggered in real time to avoid misjudgments caused by fixed thresholds.

[0065] The geomagnetic sensor integrates three parallel ADC sampling channels;

[0066] The low-frequency noise suppression algorithm is as follows:

[0067]

[0068] In the formula, for The optimal estimate of the state at time step; This is the state transition matrix; for State estimate at time; The Kalman gain matrix; for Actual measured value from the time sensor; This is the observation matrix.

[0069] The formula for power frequency notch filtering is:

[0070]

[0071] In the formula, for Time-based filtering output; for Input signals at all times; The center frequency of the notch filter; For discrete time points The original magnetic field signal sampling value; For a moment.

[0072] Verification shows that, while achieving an average error similar to the above embodiments, the false alarm rate decreased from 7.2% to 0.5%, and the impact of temperature drift was reduced by 98%. The results indicate that this mechanism completely eliminates false triggering caused by power frequency electromagnetic interference and low-frequency mechanical vibration, achieving industrial-grade reliability in geomagnetic signal output stability; it maintains the accuracy of vehicle magnetic field feature extraction under extreme temperature scenarios, overcoming signal attenuation caused by thermal drift in traditional solutions; and it dynamically adjusts sampling power consumption based on the frequency band energy ratio threshold, enabling the geomagnetic sensor to enter a near-zero power consumption mode when there is no vehicle.

[0073] Example 3:

[0074] Based on the aforementioned embodiments, in order to solve the problems of data loss and time sequence disorder in weak network environments, an event chain storage conflict self-healing mechanism is constructed based on an ordered event chain of cryptographic anchors.

[0075] Each event contains a triplet of state, timestamp, and hash anchor;

[0076] The hash anchor is generated by encrypting the content of the preceding event:

[0077]

[0078] In the formula, The current event hash anchor; The state of the preceding event; For the timestamp of the preceding event; This serves as the hash anchor for the preceding event.

[0079] Conflict identification through relative time difference mutation detection:

[0080]

[0081] In the formula, This represents the change in time difference; This is the timestamp of the current event. This is the timestamp of the preceding event.

[0082] When both conditions are met and The self-healing process is triggered at certain times.

[0083] The storage structure is as follows:

[0084] initial time ;

[0085] Subsequent events .

[0086] For example, the event sequence is:

[0087] ;

[0088] ;

[0089] ;

[0090] Conflicts occurred in a weak network environment:

[0091] .

[0092] Change in time difference:

[0093] ;

[0094] Hash verification:

[0095] expected ;

[0096] Actual reception ;

[0097] Triggering condition judgment:

[0098] ;

[0099] Only record the anomaly; do not trigger self-healing.

[0100] The effect of the event chain storage conflict self-healing mechanism is shown in Table 1.

[0101] Table 1. Summary of the effects of the event chaining storage conflict self-healing mechanism

[0102] Test Scenario Traditional solution Event chain storage conflict self-healing mechanism Effect Single-event hash error Order loss rate: 18.7% Order loss rate: 0.2% Reduced by 99% Continuous timestamp jump Data corruption rate: 42.3% The self-healing success rate is 98.5%. Correction efficiency increased by 36 times Out-of-order packets in weak networks Human intervention frequency: 3.2 times / day 100% automatic repair rate Zero human intervention Malicious clock tampering The rate of fee evasion is 12.1%. Attack interception rate: 99.9% Economic efficiency increased by 22%.

[0103] As shown in the experimental table, the dual-condition triggering mechanism avoids misjudgment based on a single anomaly, the geomagnetic resampling calibration time is less than 200ms, and the chain structure ensures that events are tamper-proof. The chain association of hash anchors ensures absolute order of events, eradicating the chronic problems of out-of-order and lost data packets in weak network environments; two-factor collision detection accurately identifies malicious tampering and equipment failure, and the self-healing process automatically restores data consistency; the multi-level self-healing strategy replaces manual on-site intervention, achieving a lifetime maintenance-free system.

[0104] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable non-transitory storage media containing computer-usable program code.

[0105] The present invention can provide computer program instructions to a management platform of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing equipment to produce a machine, such that the instructions executed by the management platform of the computer or other programmable data processing equipment produce means for implementing the system.

[0106] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that perform the functions of the system.

[0107] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions of the system.

Claims

1. An automated order management system based on geomagnetic detection, characterized in that, include: An anti-interference geomagnetic sensing unit is used to output a stable vehicle presence status signal through environmental magnetic field disturbance suppression technology; The multi-source collaborative arbitration engine is used to fuse geomagnetic state signals and spatiotemporal coordinate data from video inspection equipment, and triggers order lifecycle operations only when the dual verifications are consistent. The distributed fault-tolerant processing unit is used to initiate a multimodal review process for data conflict events and freeze abnormal orders; Edge data chain storage unit is used to generate an immutable data chain in the order of events in a weak network environment; The event chain storage conflict self-healing unit is used to trigger the self-healing process through a two-factor condition of relative time difference mutation amount and anchor point verification. The self-healing process includes discarding conflicting events, synchronizing previous authoritative data in the cloud, geomagnetic resampling calibration, and event chain reconstruction. The relative time difference mutation amount is used to identify conflicts by calculating the change in the time difference between adjacent events. The anchor point verification generates the current event hash anchor point based on the previous event status, timestamp, and hash anchor point. The spectrum-layered dynamic filtering unit is used to process the magnetic field signal by frequency band, decompose the magnetic field signal into three characteristic frequency bands, including the low frequency band, the power frequency band and the vehicle characteristic band, and determine the valid vehicle event based on the dynamic energy ratio threshold. The dynamic energy ratio threshold is calculated by multiplying the ratio of the energy of the vehicle frequency band to the energy of the noise frequency band by a temperature compensation coefficient, so as to dynamically adjust the judgment threshold.

2. The system according to claim 1, characterized in that: The anti-interference geomagnetic sensing unit integrates dynamic noise suppression function, which separates low-frequency environmental disturbances from vehicle magnetic field characteristics and suppresses power frequency electromagnetic interference, outputting a stable signal that meets the preset signal-to-noise ratio threshold.

3. The system according to claim 1, characterized in that: The multi-source collaborative arbitration engine performs hierarchical verification, including time window matching of geomagnetic events and video recognition events, spatial consistency verification of video coordinates and target parking space geofences, and a statistical anomaly detection model built based on historical behavior data, which automatically marks and freezes orders that deviate from the normal distribution.

4. The system according to claim 1, characterized in that: The distributed fault-tolerant processing unit activates the multimodal review process when arbitration fails, including controlling the video equipment to reacquire the target parking space image and calling the geomagnetic sensors of adjacent parking spaces to cross-verify the status. If arbitration is still not possible, the order will be frozen and audio-visual guidance will be activated to encourage the car owner to confirm.

5. The system according to claim 1, characterized in that: The chain nodes of the data chain contain state hash values ​​to enable offline data integrity verification.

6. The system according to claim 5, characterized in that: The edge data chain storage unit is equipped with a conflict self-healing mechanism. When hash verification fails, it requests cloud data synchronization and triggers adaptive calibration of geomagnetic sensor parameters.

7. The system according to claim 1, characterized in that: The system is connected to a dynamic billing strategy executor, which matches regional rate rules based on the spatiotemporal attributes of the order.

8. The system according to claim 1, characterized in that: The spectrum-layered dynamic filtering unit determines the effective signal by using a dynamic energy ratio threshold, as shown in the following formula: In the formula, The dynamic energy ratio threshold; For vehicle frequency band energy; Energy in the noise frequency band; This is the temperature compensation coefficient.

9. The system according to claim 1, characterized in that: It also includes an energy consumption adaptive scheduling module that dynamically switches the sampling frequency based on the geomagnetic state and enters an ultra-low power consumption mode after the vehicle leaves.

10. An automated order management method based on geomagnetic detection, characterized in that: The method is implemented based on the system described in any one of claims 1-9: The method includes: The original magnetic field signal is collected by a geomagnetic sensor, the magnetic field signal is decomposed into three characteristic frequency bands, and the dynamic energy ratio of the vehicle frequency band energy to the noise frequency band energy is calculated to generate vehicle events. Receive license plate coordinates and timestamps reported by geomagnetic vehicle events and video inspection equipment, perform three-level hierarchical verification, and create or terminate parking orders only when the three-level verification is consistent. In a weak network environment, an event chain structure is constructed according to time sequence, and the sudden change in the time difference between adjacent events is monitored in real time. When the conditions are met, a self-healing process is triggered, including discarding conflicting events, synchronizing previous authoritative data in the cloud, geomagnetic resampling calibration, and event chain reconstruction. The sampling frequency is dynamically adjusted based on the vehicle's presence status as fed back by the geomagnetic field in real time, and the energy consumption strategy is updated synchronously after the order is terminated.

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