Intelligent processing method for data of anti-lost device and anti-lost device

Abstract

The invention relates to the technical field of anti-lost devices, and provides an anti-lost device data intelligent processing method and an anti-lost device.By detecting the signal strength of a GPS signal and the signal strength of a WiFi signal of the logistics environment of an anti-lost article, the anti-lost article is loaded on a carrying device provided with the anti-lost device, a GPS positioning unit and a WiFi positioning unit are arranged in the anti-lost device, and the anti-lost article is loaded on the carrying device. The positioning and tracking unit is used for positioning and tracking the anti-lost article carried by the carrying device every time, and automatically selecting the positioning unit with high signal strength from the GPS positioning unit and the WiFi positioning unit to track and position the position of the anti-lost article according to the strength state of the signal strength of the GPS signal and the strength state of the signal strength of the WiFi signal; according to the method, the position data of the anti-lost article is acquired to perform noise filtering on the original article position data of the anti-lost article so as to obtain the denoised real-time article position data, and the current position state of the anti-lost article is preliminarily calculated according to the denoised real-time article position data so as to determine whether the position of the anti-lost article is abnormal or not, so that the positioning precision of the anti-lost device is improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of intelligent logistics, anti-loss devices and edge computing, and in particular to an intelligent processing method for anti-loss device data and an anti-loss device. Background Art

[0002] As a smart device that can be used for positioning and tracking, anti-lost devices have been widely used in smart logistics environments. For example, anti-lost devices can be embedded in carriers such as pallets to track and locate anti-lost items. However, most existing anti-lost devices rely on a single positioning method (such as simple GPS or WiFi). In the face of complex logistics environments, a single positioning method is easily affected by problems such as signal blocking, interference or signal loss, resulting in reduced positioning accuracy.

[0003] In summary, the existing logistics positioning technology of anti-lost devices has the technical problem of low positioning accuracy. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides an intelligent processing method for anti-loss device data and an anti-loss device to improve the positioning accuracy of the anti-loss device.

[0005] In a first aspect, the present invention provides an intelligent processing method for anti-loss device data, comprising: Detecting the signal strength of the GPS signal and the signal strength of the WiFi signal in the logistics environment of the anti-lost item; the anti-lost item is loaded on a carrier equipped with an anti-lost device, and the anti-lost device has a built-in GPS positioning unit and a WiFi positioning unit for locating and tracking the anti-lost item carried by the carrier each time; According to the signal strength of the GPS signal and the signal strength of the WiFi signal, the positioning unit with the stronger signal strength among the GPS positioning unit and the WiFi positioning unit is automatically selected to track and locate the position of the anti-lost item, so as to obtain the original item position data of the anti-lost item; After obtaining the original item location data, the original item location data is subjected to noise filtering to obtain denoised real-time item location data, and the current location status of the anti-lost item is preliminarily calculated based on the denoised real-time item location data to confirm whether there is any abnormality in the location of the anti-lost item.

[0006] In a second aspect, the present invention provides an anti-loss device, the data of which is processed using the above-mentioned intelligent processing method for anti-loss device data.

[0007] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides an intelligent processing method for data of an anti-lost device and an anti-lost device. By detecting the signal strength of a GPS signal and a WiFi signal of an anti-lost object logistics environment, the anti-lost object is loaded on a carrier provided with the anti-lost device. The anti-lost device has a built-in GPS positioning unit and a WiFi positioning unit for locating and tracking the anti-lost object carried by the carrier each time. According to the strong and weak states of the signal strength of the GPS signal and the strong and weak states of the signal strength of the WiFi signal, the positioning unit with a strong signal strength among the GPS positioning unit and the WiFi positioning unit is automatically selected to track and locate the position of the anti-lost object, so as to obtain the original object position data of the anti-lost object. After obtaining the original object position data, the original object position data is subjected to noise filtering to obtain the real-time object position data after denoising. According to the real-time object position data after denoising, the current position state of the anti-lost object is preliminarily calculated to confirm whether there is any abnormality in the position of the anti-lost object, thereby improving the positioning accuracy of the anti-lost device. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their description are used to explain the present invention and do not constitute an improper limitation of the present invention. Some specific embodiments of the present invention will be described in detail in an exemplary and non-restrictive manner with reference to the drawings. The same reference numerals in the drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the drawings: Figure 1 It is a flow chart of an intelligent processing method of anti-loss device data according to an embodiment of the present invention; Figure 2 It is a schematic diagram of the architecture of the intelligent management system of the anti-lost device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0009] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only an embodiment of a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present invention.

[0010] Embodiment 1 See also Figure 1-Figure 2This embodiment provides an intelligent processing method for anti-loss device data and an anti-loss device, and the data of the anti-loss device is processed using the intelligent processing method for the anti-loss device data. The intelligent processing method of the anti-lost device data can be run in the main control module of the anti-lost device, including steps S101, S102 and S103. By detecting the signal strength of the GPS signal and the signal strength of the WiFi signal in the logistics environment of the anti-lost item, the anti-lost item is loaded on a carrier provided with the anti-lost device. The anti-lost device has a built-in GPS positioning unit and a WiFi positioning unit for locating and tracking the anti-lost item carried by the carrier each time. According to the strong and weak states of the signal strength of the GPS signal and the strong and weak states of the signal strength of the WiFi signal, the positioning unit with a strong signal strength among the GPS positioning unit and the WiFi positioning unit is automatically selected to track and locate the position of the anti-lost item, so as to obtain the original item position data of the anti-lost item. After obtaining the original item position data, the original item position data is subjected to noise filtering to obtain the real-time item position data after denoising, and the current position state of the anti-lost item is preliminarily calculated according to the real-time item position data after denoising, so as to confirm whether there is any abnormality in the position of the anti-lost item, thereby improving the positioning accuracy of the anti-lost device.

[0011] Specifically, the intelligent processing method of the anti-loss device data may include the following steps: S101, detecting the signal strength of the GPS signal and the signal strength of the WiFi signal in the logistics environment of the anti-lost item; the anti-lost item is loaded on a carrier provided with an anti-lost device, and the anti-lost device has a built-in GPS positioning unit and a WiFi positioning unit for locating and tracking the anti-lost item carried by the carrier each time; S102, according to the strength of the GPS signal and the strength of the WiFi signal, automatically selecting a positioning unit with a stronger signal strength between the GPS positioning unit and the WiFi positioning unit to track and locate the position of the anti-lost object, so as to obtain the original object location data of the anti-lost object; S103. After obtaining the original item location data, perform noise filtering on the original item location data to obtain denoised real-time item location data, and preliminarily calculate the current location status of the anti-lost item based on the denoised real-time item location data to confirm whether there is any abnormality in the location of the anti-lost item.

[0012] It should be noted that the existing anti-lost device usually relies on a single positioning method (such as GPS or WiFi). This single method may be blocked, interfered or lost in a complex logistics environment, resulting in a decrease in positioning accuracy. In step S101, the signal strength of the GPS signal and the WiFi signal in the logistics environment where the anti-lost item is located are detected by the built-in GPS positioning unit and the WiFi module, so that the signal strength data of the two signal sources (GPS and WiFi) in the current environment can be obtained to prepare for the subsequent signal selection. In practice, the signal strength in the logistics environment is variable. By detecting the strength of the two signal sources respectively, a basis can be provided for selecting the best positioning source. In step S102, according to the signal strength, the positioning unit (GPS or WiFi) with a stronger signal is automatically selected for positioning, and the original position data of the item is obtained. Among them, according to the strength state of the signal strength of the GPS signal and the strength state of the signal strength of the WiFi signal, the positioning system (GPS or WiFi) with a stronger signal strength is automatically selected to ensure that the anti-lost device performs the positioning operation when the signal is the strongest, and reduce the positioning error caused by poor signal. In a complex logistics environment, the signal strength of GPS and WiFi often changes alternately. Therefore, step S102 analyzes the strength of the signal to avoid positioning errors caused by unstable signals, thereby improving positioning accuracy. In step S103, the original item location data is subjected to noise filtering to obtain real-time item location data after denoising, and the current location state of the anti-lost item is preliminarily calculated to determine whether there is a position abnormality. Among them, noise filtering can remove positioning errors caused by signal interference or other factors and improve positioning accuracy. By preliminarily calculating the location state of the item and determining whether the current location is abnormal, the monitoring effect of the anti-lost device can be optimized. The original location data is usually susceptible to environmental interference (such as reflected signals, multipath effects, etc.) and may generate noise. Noise filtering can ensure the reliability of positioning data. In a complex logistics environment, even if the signal strength is good, the quality of the signal (especially the GPS signal) may be disturbed, resulting in large errors in the location data. Noise filtering can remove unnecessary fluctuations by smoothing the data through algorithms (such as low-pass filtering).

[0013] In some preferred embodiments, when the initial calculation finds that the position of the anti-lost item is abnormal, the real-time item position data after denoising is transmitted to the edge computing node, and the current position state of the anti-lost item is calculated again by the edge computing node to confirm again whether the position of the anti-lost item is abnormal. It should be noted that in a complex logistics environment, the anti-lost device relies on multiple signal sources to locate items. Due to factors such as signal interference and occlusion, the anti-lost device may calculate an abnormal position (for example, position information with large errors). Therefore, after the initial judgment that the position of the item is abnormal, the data is transmitted to the edge computing node for re-verification to avoid false alarms or wrong judgments caused by the processing power or calculation accuracy limitations of a single computing device. In this embodiment, the anti-lost device tracks the position of the anti-lost item in real time through a built-in positioning unit (such as GPS, WiFi unit) and performs noise filtering on the position data. These processes can be used as the basic functions of the anti-lost device, responsible for real-time and local positioning tasks. The anti-lost device will perform preliminary abnormality calculations on the acquired denoised position data to determine whether there are obvious errors or abnormalities, thereby quickly identifying data with obviously unreasonable positions as a basis for subsequent confirmation. The edge computing node is responsible for receiving the denoised real-time location data from the anti-lost device and performing more complex calculations. Edge computing devices usually have stronger computing power than anti-lost devices and can perform more accurate anomaly detection and analysis. It is understandable that the hardware of the anti-lost device usually has a certain computing power, but its main purpose is to perform real-time positioning and basic signal processing. If all calculations rely on the anti-lost device, the response speed and accuracy of the system may be affected due to limited computing resources. Therefore, transferring the abnormal confirmation processing task to the edge computing node can reduce the computing pressure of the anti-lost device and improve the computing efficiency of the overall system. In this embodiment, by combining preliminary judgment (anti-lost device) and review calculation (edge ​​computing node), false alarms and missed alarms can be reduced. The anti-lost device is responsible for quickly detecting possible anomalies, while the edge computing node conducts a review to ensure that the system will only trigger an alarm if the location is indeed abnormal. It should be noted that in this embodiment, through the edge computing node, the system can complete part of the calculation locally, avoiding frequent sending of large amounts of data and requests to the cloud, thereby improving the response efficiency and bandwidth utilization of the system.

[0014] In some preferred embodiments, when the edge computing node confirms again that there is an abnormality in the location of the anti-lost item, the edge computing node sends an alarm of the abnormal location of the anti-lost item to the intelligent terminal responsible for monitoring the anti-lost item. It should be noted that in this embodiment, when the edge computing node confirms that the location is abnormal, it immediately notifies the intelligent terminal responsible for monitoring to ensure that the relevant personnel or system can know the abnormal situation of the item in real time, so as to respond quickly. Traditional alarm mechanisms usually rely on the cloud for processing, but this will increase the delay in information transmission. In this embodiment, the edge computing node directly sends an alarm to the intelligent terminal, reducing the intermediate links, speeding up the response speed and reducing the potential risks caused by delays. It is understandable that an intelligent terminal usually refers to a device equipped with a user interface, such as a smart phone, a tablet computer or a dedicated monitoring terminal. Through the operation interface of the intelligent terminal, users can see the specific content of the alarm, understand the abnormal location information, and can further view the relevant historical data or trends, which helps users make more accurate and rapid decisions.

[0015] In some preferred embodiments, after the edge computing node issues an alarm that the position of the anti-lost object is abnormal, the edge computing node directly issues a data transfer instruction and a data clearing instruction to the anti-lost device; the anti-lost device transmits the denoised real-time object location data to the data center for storage according to the data transfer instruction, and clears the denoised real-time object location data stored locally according to the data clearing instruction. It should be noted that the storage space of the anti-lost device is usually limited, especially for embedded devices. In this embodiment, when the edge computing node confirms that the position of the object is abnormal again and issues an alarm, the data at the time of the abnormality is saved, and if no abnormality occurs under normal circumstances, the anti-lost device will not need to save redundant data, so that the storage space of the anti-lost device can be used to store more important or more valuable data, avoiding the storage space being occupied by expired or unnecessary location information, and maintaining the normal working state of the anti-lost device. In abnormal circumstances (for example, when the object deviates from the predetermined route or the position error is too large), it is necessary to transfer the data to the data center, because the data has a higher reference value at this time and can be the basis for investigation and analysis. Moreover, if the anti-lost device needs to perform a transfer operation every time it generates data, it will increase the burden on the communication network and waste the storage space of the data center.

[0016] In some preferred embodiments, when the edge computing node confirms again that there is no abnormality in the location of the anti-lost item, the edge computing node requests the cloud to clear the denoised real-time item location data, and the cloud sends a data transfer instruction and a data clearing instruction to the anti-lost device according to the clearing request; the anti-lost device transmits the denoised real-time item location data to the data center for storage according to the data transfer instruction, and clears the denoised real-time item location data stored locally according to the data clearing instruction. It should be noted that when the edge computing node performs more complex calculations, it can combine multiple information sources for judgment and has more powerful computing power. This makes the judgment of the edge computing node more accurate, while the anti-lost device may produce preliminary calculation errors due to computing resource limitations or simple algorithms. In this embodiment, by handing over the authority of data clearing and transfer when the calculation results of different computing nodes are inconsistent to the cloud, the system can intelligently adjust the data processing logic based on the final judgment of the edge computing node to improve the overall fault tolerance. It should be noted that although the edge computing node reconfirms that there is no abnormality in the location of the anti-lost item, the denoised real-time item location data at this time is inconsistent with the result of the recalculation by the edge computing node and the initial calculation by the anti-lost device. It is necessary to transfer the data at this time to the data center, because the data at this time has a higher reference value and can serve as the basis for investigation and analysis.

[0017] In some preferred embodiments, when preliminary calculations reveal that there is no abnormality in the location of the anti-lost item, the locally stored real-time item location data after denoising is directly cleared, and the real-time item location data after denoising is no longer transmitted to the data center for storage. It should be noted that the location information of the item only needs to be stored and backed up for a long time when an abnormality occurs. For normal daily situations, storing this location information will take up storage space on the device. In this embodiment, by directly clearing the locally stored real-time item location data after denoising and avoiding transmission to the data center, the storage space of the anti-lost device can be effectively saved and the storage burden of invalid data can be reduced. In addition, this can also reduce the occupancy of network bandwidth, because irrelevant data is no longer uploaded to the data center, which can avoid unnecessary network traffic consumption.

[0018] In some preferred embodiments, when detecting the signal strength of the GPS signal and the signal strength of the WiFi signal in the anti-lost item logistics environment, it includes: collecting the strength data of the GPS signal and the WiFi signal in the anti-lost item logistics environment; calculating the strength value of the GPS signal and the WiFi signal in the anti-lost item logistics environment according to the strength data of the GPS signal and the WiFi signal. Further, the anti-lost device includes a GPS receiver, a WiFi module, a WiFi signal strength calculation module and a GPS signal strength calculation module; the GPS receiver collects the strength data of the GPS signal in the anti-lost item logistics environment and transmits it to the GPS signal strength calculation module, and the GPS signal strength calculation module calculates the strength value of the GPS signal in the anti-lost item logistics environment according to the received strength data of the GPS signal; the WiFi module collects the strength data of the WiFi signal in the anti-lost item logistics environment and transmits it to the WiFi signal strength calculation module, and the WiFi signal strength calculation module calculates the strength value of the WiFi signal in the anti-lost item logistics environment according to the received strength data of the WiFi signal. It should be noted that in this embodiment, signal acquisition and signal calculation are respectively assigned to special modules (such as WiFi signal strength calculation module and GPS signal strength calculation module). The modular design can effectively share the calculation tasks of different functions, ensure that each module focuses on its own function, avoid excessive occupation of hardware resources, and thus improve the operation efficiency of the overall system. The signal acquisition module is only responsible for obtaining signal data from the outside, and does not need to directly process complex calculation tasks. The calculation tasks are assigned to the special signal calculation module, making the calculation process more efficient and clear. The GPS signal strength calculation module independently processes the strength data of the GPS signal, and can obtain the GPS signal strength value more accurately according to specific algorithms (such as weighted average, filtering, etc.). The WiFi signal strength calculation module independently processes the WiFi signal strength data, which can ensure that the changes of the WiFi signal in different environments can also be properly processed, thereby improving the adaptability of the system to complex environments.

[0019] In some preferred embodiments, when automatically selecting a positioning unit with a strong signal strength to track and locate the position of the anti-lost item, it includes: comparing the strength value of the GPS signal with a preset GPS signal threshold, and comparing the strength value of the WiFi signal with a preset WiFi signal threshold. If the strength value of the GPS signal is lower than the GPS signal threshold, and the strength value of the WiFi signal is higher than the WiFi signal threshold, the WiFi positioning unit is automatically selected to track and locate the position of the anti-lost item; if the strength value of the GPS signal is higher than the GPS signal threshold, and the strength value of the WiFi signal is lower than the WiFi signal threshold, the GPS positioning unit is automatically selected to track and locate the position of the anti-lost item. It should be noted that in actual applications, the strength of GPS and WiFi signals will be affected by a complex logistics environment. When the signal strength is high, the positioning accuracy is usually higher, while when the signal is weak, the positioning accuracy will be affected, which may cause errors. In this embodiment, by comparing the GPS and WiFi signal strength values ​​with the preset thresholds to determine which positioning unit to use, it is helpful to select the best signal source for positioning, thereby ensuring higher positioning accuracy and reliability, and avoiding positioning failure or errors caused by a weak single signal source. Further, after comparing the strength value of the GPS signal with the preset GPS signal threshold, and comparing the strength value of the WiFi signal with the preset WiFi signal threshold, if the strength value of the GPS signal is lower than the GPS signal threshold and the strength value of the WiFi signal is lower than the WiFi signal threshold, or if the strength value of the GPS signal is higher than the GPS signal threshold and the strength value of the WiFi signal is higher than the WiFi signal threshold, the GPS positioning unit is selected by default to track and locate the position of the anti-lost item. It should be noted that when the strength value of the GPS signal and the strength value of the WiFi signal are both lower than the preset threshold, it means that the signal environment is poor. At this time, selecting the default use of the GPS positioning unit can ensure the continuity and stability of positioning. Even if the signal is weak, GPS usually relies on a wider base station or satellite network to provide more accurate positioning. When the strength of both GPS and WiFi signals is strong, the default use of the GPS positioning unit is selected, mainly based on the high positioning accuracy of GPS in most scenarios, and the GPS positioning unit can provide more accurate positioning data.

[0020] In some preferred embodiments, when the original item location data is subjected to noise filtering, the method includes: smoothing the original item location data by a low-pass filtering algorithm to obtain real-time item location data after denoising. It should be noted that GPS signals and WiFi signals are easily affected by environmental interference, resulting in unstable original data. Fluctuations or anomalies in these data may affect positioning accuracy. In this embodiment, by filtering noise, short-term data fluctuations caused by environmental factors, signal jitter, etc. can be eliminated, errors can be reduced, and the reliability of the final item location data can be improved. In addition, low-pass filtering is an algorithm with relatively low computational complexity and is suitable for implementation on resource-constrained devices. As an embedded device, the anti-lost device has relatively limited hardware resources (such as processor power, memory, power consumption, etc.). If an overly complex noise filtering algorithm is used, performance degradation or excessive energy consumption will result. The low-pass filtering algorithm has a small amount of computation and can run efficiently under the limited computing resources of the anti-lost device.

[0021] In a further preferred embodiment, when the current position state of the anti-lost item is preliminarily calculated based on the real-time item position data after denoising, it includes: analyzing the changes between the continuous position points in the real-time item position data after denoising, if the changes between the continuous position points exceed the set threshold, it is preliminarily determined that the position of the anti-lost item is abnormal, if the changes between the continuous position points do not exceed the set threshold, it is preliminarily determined that the position of the anti-lost item is not abnormal; when the current position state of the anti-lost item is recalculated by the edge computing node, it includes: using the Kalman filter algorithm to smooth the real-time item position data after denoising again to obtain the real-time item position data after smoothing again; according to the real-time item position data after smoothing again, calculate the current position trajectory of the anti-lost item, and optimize the current position trajectory of the anti-lost item using the particle filter algorithm to obtain the optimized position trajectory of the anti-lost item; compare the optimized position trajectory with the preset normal track to confirm again whether the position of the anti-lost item is abnormal. It should be noted that in this embodiment, a simple algorithm (based on the threshold judgment of the position point change) inside the anti-lost device is used to quickly determine whether the position of the anti-lost item is abnormal. The algorithm is a lightweight calculation step that can respond quickly and consume less hardware resources. It is used to filter out obvious abnormal situations and help to find problems in time. At the same time, in this embodiment, the edge computing node performs more complex calculations, specifically involving more advanced algorithms such as Kalman filtering and particle filtering. The edge computing node has a more powerful processing capability, can perform more complex data analysis, perform more in-depth data smoothing and trajectory optimization, and improve the accuracy of the judgment of the location of the item. In the positioning of the item, the Kalman filter can effectively remove the noise caused by factors such as signal fluctuations and equipment errors, making the movement trajectory of the item smoother and more accurate. In the recalculation of the edge computing node, the Kalman filter algorithm helps to improve the accuracy of the item location data, especially in the case of poor continuity or unstable signal. Particle filtering can effectively estimate the location of the item, and using particle filtering to optimize the trajectory can provide a more accurate estimation of the item location by comprehensively analyzing historical data and current observation data. In this embodiment, after obtaining the optimized position trajectory of the anti-lost item after the particle filter algorithm is optimized, the optimized position trajectory is compared with the preset normal trajectory to further confirm whether the location of the item is abnormal. If the optimized position trajectory is significantly different from the normal trajectory, it means that there is an abnormality in the position of the anti-lost item, otherwise there is no abnormality.

[0022] Embodiment 2 See also Figure 1-Figure 2The intelligent processing method of the anti-loss device data in the above embodiment can be used in the intelligent management system of the anti-loss device, which includes the anti-loss device, edge computing nodes, data centers and cloud; wherein the anti-loss device can be connected and communicated with the edge computing nodes and the data centers, the edge computing nodes can be connected and communicated with the cloud, and the cloud can be connected and communicated with the data centers. When the intelligent processing method of the anti-lost device data is used in the intelligent management system of the anti-lost device, the signal strength of the GPS signal and the signal strength of the WiFi signal of the logistics environment of the anti-lost item are detected. The anti-lost item is loaded on a carrier provided with the anti-lost device. The anti-lost device has a built-in GPS positioning unit and a WiFi positioning unit for locating and tracking the anti-lost item carried by the carrier each time. According to the strong and weak state of the signal strength of the GPS signal and the strong and weak state of the signal strength of the WiFi signal, the positioning unit with a strong signal strength among the GPS positioning unit and the WiFi positioning unit is automatically selected to track and locate the position of the anti-lost item, so as to obtain the original item location data of the anti-lost item. After obtaining the original item location data, the original item location data is subjected to noise filtering to obtain the real-time item location data after denoising, and according to the real-time item location data after denoising, the current position state of the anti-lost item is preliminarily calculated to confirm whether there is any abnormality in the position of the anti-lost item, thereby improving the positioning accuracy of the anti-lost device.

Claims

1. An intelligent processing method for anti-lost device data, characterized in that: include: Detecting the signal strength of the GPS signal and the signal strength of the WiFi signal in the logistics environment of the anti-lost item; the anti-lost item is loaded on a carrier equipped with an anti-lost device, and the anti-lost device has a built-in GPS positioning unit and a WiFi positioning unit for locating and tracking the anti-lost item carried by the carrier each time; According to the signal strength of the GPS signal and the signal strength of the WiFi signal, the positioning unit with the stronger signal strength among the GPS positioning unit and the WiFi positioning unit is automatically selected to track and locate the position of the anti-lost item, so as to obtain the original item position data of the anti-lost item; After obtaining the original item location data, the original item location data is subjected to noise filtering to obtain denoised real-time item location data, and the current location status of the anti-lost item is preliminarily calculated based on the denoised real-time item location data to confirm whether there is any abnormality in the location of the anti-lost item.

2. The intelligent processing method for anti-lost device data according to claim 1, characterized in that: When preliminary calculation finds that there is an abnormality in the position of the anti-lost item, the denoised real-time item position data is transmitted to the edge computing node, and the current position status of the anti-lost item is calculated again by the edge computing node to confirm again whether there is an abnormality in the position of the anti-lost item.

3. The intelligent processing method for anti-lost device data according to claim 2, characterized in that: When the edge computing node confirms again that there is an abnormality in the location of the anti-lost item, the edge computing node sends an alarm of the abnormal location of the anti-lost item to the smart terminal responsible for monitoring the anti-lost item.

4. The intelligent processing method for anti-lost device data according to claim 3, characterized in that: After the edge computing node issues an alarm that the location of the anti-loss item is abnormal, the edge computing node directly issues a data transfer instruction and a data clearing instruction to the anti-loss device; the anti-loss device transmits the denoised real-time item location data to the data center for storage according to the data transfer instruction, and clears the denoised real-time item location data stored locally according to the data clearing instruction.

5. The intelligent processing method for anti-lost device data according to claim 2, characterized in that: When the edge computing node confirms again that there is no abnormality in the location of the anti-lost item, the edge computing node requests the cloud to clear the denoised real-time item location data, and the cloud sends a data transfer instruction and a data clearing instruction to the anti-lost device according to the clearing request; the anti-lost device transmits the denoised real-time item location data to the data center for storage according to the data transfer instruction, and clears the denoised real-time item location data stored locally according to the data clearing instruction.

6. The intelligent processing method for anti-lost device data according to claim 1, characterized in that: When preliminary calculations show that there is no abnormality in the location of the anti-lost item, the locally stored real-time item location data after denoising is directly cleared, and the real-time item location data after denoising is no longer transmitted to the data center for storage.

7. The intelligent processing method for anti-lost device data according to any one of claims 1 to 6, characterized in that: When detecting the signal strength of the GPS signal and the signal strength of the WiFi signal in the anti-lost item logistics environment, it includes: collecting the strength data of the GPS signal and the strength data of the WiFi signal in the anti-lost item logistics environment; calculating the strength value of the GPS signal and the strength value of the WiFi signal in the anti-lost item logistics environment based on the strength data of the GPS signal and the strength data of the WiFi signal.

8. The intelligent processing method for anti-lost device data according to claim 7, characterized in that: When automatically selecting a positioning unit with strong signal strength to track and locate the position of the anti-lost item, it includes: comparing the strength value of the GPS signal with a preset GPS signal threshold, and comparing the strength value of the WiFi signal with a preset WiFi signal threshold; if the strength value of the GPS signal is lower than the GPS signal threshold, and the strength value of the WiFi signal is higher than the WiFi signal threshold, the WiFi positioning unit is automatically selected to track and locate the position of the anti-lost item; if the strength value of the GPS signal is higher than the GPS signal threshold, and the strength value of the WiFi signal is lower than the WiFi signal threshold, the GPS positioning unit is automatically selected to track and locate the position of the anti-lost item.

9. The intelligent processing method for anti-lost device data according to claim 1, characterized in that: When the noise filtering is performed on the original object position data, it includes: smoothing the original object position data by a low-pass filtering algorithm to obtain the real-time object position data after noise removal.

10. An anti-lost device, characterized in that: The data of the anti-loss device is processed using the intelligent processing method for anti-loss device data as described in any one of claims 1-9.

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