Method and system for sensing stacking state of articles in safety box, terminal and storage medium

Abstract

The invention relates to a method and a system for sensing a stacking state of articles in a safety box, a terminal and a storage medium, which are used for accurately judging the stacking state of the articles through a machine learning algorithm in combination with time delay and intensity information of signals and geometrical characteristics of the articles. The stacking state of the articles is judged according to the time delay change of the feedback signal, and the accurate judgment of the stacking condition of the articles is realized by accurately measuring and analyzing the signal time delay; the moving speed of the label is estimated through the Doppler effect, and therefore the dynamic change of the object is judged; the signal phase difference is analyzed to judge the direction and position of the object, and the judgment accuracy is improved; and the stacking state of the articles is identified by detecting the intensity change of the feedback signal of the RFID tag. Judging whether the articles are stacked or not by using the attenuation characteristic of the signal intensity; by combining data of signal time delay, intensity, Doppler effect and phase difference, through algorithm analysis, the stacking state of articles in the cabinet is accurately judged, and the conditions of single-layer placement and multi-layer stacking can be effectively distinguished.

Description

Technical Field

[0001] The present invention relates to, in particular to, a method for sensing the stacking state of items in a safe, a system for sensing the stacking state of items in a safe, an intelligent terminal and a storage medium. Background Art

[0002] RFID systems are mainly used for the identification and positioning of items. In modern office environments, RFID safes are widely used to store and manage important documents and items. However, due to the complex and changeable stacking status of items such as documents, existing RFID technology has certain limitations in identifying and managing these items. When facing stacked items, existing RFID technology is prone to signal interference and misreading, resulting in reduced accuracy in identifying items; at the same time, the existing system can only identify the RFID tag of the item, but cannot obtain the stacking status and specific location of the item, making it difficult to achieve refined management and access operations; difficulties are often encountered when searching and accessing items, affecting work efficiency and user experience, resulting in poor user experience. At the same time, the existing RFID system has deficiencies in judging the stacking status of items. Specifically, the existing technology mainly relies on the location information and signal strength of the RFID tag to judge the stacking status of items, but due to the multipath effect and environmental interference of the signal, this method often cannot accurately judge the actual stacking status of items in practical applications. Summary of the invention

[0003] The technical problem to be solved by the present invention is that the existing RFID technology is prone to signal interference and misreading when facing stacked items, resulting in reduced object recognition accuracy; at the same time, the existing system can only identify the RFID tag of the item, but cannot obtain the stacking status and specific location of the item, making it difficult to achieve refined management and access operations; difficulties are often encountered when searching and accessing items, affecting work efficiency and user experience, resulting in poor user experience. At the same time, the existing RFID system has shortcomings in judging the stacking status of items. Specifically, the existing technology mainly relies on the location information and signal strength of the RFID tag to judge the stacking situation of the items, but due to the multipath effect and environmental interference of the signal, this method is often unable to accurately judge the actual stacking situation of the items in practical applications. In view of the above-mentioned defects of the prior art, a sensing method, a sensing system and a storage medium for the stacking status of items in a safe are provided.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A method for sensing the stacking state of items in a safe is constructed, comprising:

[0006] Obtain feedback signals from labels attached to items in the safe;

[0007] Perform feature extraction on the feedback signal to obtain key features of the feedback signal;

[0008] A preliminary judgment of the stacking of items is obtained based on the key feature analysis of the feedback signal;

[0009] The analysis and judgment results are processed and stored.

[0010] Preferably, the extracting features of the feedback signal to obtain key features of the feedback signal includes:

[0011] At least one of the time delay, intensity, speed and angle of the feedback signal is obtained, and the stacking condition of the objects is determined based on the information.

[0012] Preferably, the stacking state of the objects is determined by measuring the time delay of the feedback signal return signal to analyze the relative distance and position between the objects;

[0013] and / or comparing the intensity of the feedback signal with a preset signal intensity threshold to determine the stacking state of the objects;

[0014] and\or calculating the speed of the feedback signal by the change value of the reflection frequency of the feedback signal to determine the stacking state of the objects;

[0015] And\or by analyzing the phase difference of the feedback signal, calculating the direction and angle of the feedback signal to determine the stacking state of the objects.

[0016] Preferably, after obtaining a preliminary judgment on the stacking of objects based on the key feature analysis of the feedback signal, the method further includes:

[0017] The model is trained using a machine learning algorithm and the key features of the feedback signal and the stacking status of the objects to obtain a trained judgment model, and the trained judgment model is used to analyze new feedback signals to determine the stacking status of the objects.

[0018] Preferably, the step of analyzing the new feedback signal using the trained judgment model to judge the stacking status of the objects further comprises:

[0019] The analysis results and judgment situations are processed and stored, and the new feedback signal analysis and judgment results are used as training data for training the judgment model to train the model to improve the accuracy of the judgment model.

[0020] Preferably, obtaining the feedback signal of the label attached to the item in the safe further includes:

[0021] The attached tag includes an antenna and a chip, and obtains a feedback signal through a reader. After the reader sends a signal, the chip modulates and adjusts it, and then the signal is reflected through the antenna to form a feedback signal.

[0022] Preferably, the reader / writer includes an antenna, a transmitting module, a receiving module and a signal processing module, the transmitting module sends a signal, the receiving module receives a feedback signal, and the signal processing module analyzes the feedback signal to extract key features of the feedback signal.

[0023] Construct a sensing program for the stacking status of items in a safe, including:

[0024] A data acquisition module obtains feedback signals from labels attached to items in the safe;

[0025] The data processing module extracts the features of the feedback signal to obtain key features;

[0026] The data judgment module obtains preliminary item stacking judgment based on the key feature analysis of the feedback signal;

[0027] The data management module processes and stores the feature extraction and judgment results.

[0028] A smart terminal is constructed, which includes at least one memory, at least one processor, and a sensing program for the stacking state of items in a safe stored in the memory and executable on the processor. When the sensing program for the stacking state of items in the safe is executed by the processor, the steps of the sensing method for the stacking state of items in the safe as described above are implemented.

[0029] A computer-readable storage medium is constructed, on which a sensing program for the stacking state of items in a safe is stored. When the sensing program for the stacking state of items in a safe is executed by a processor, the steps of the sensing method for the stacking state of items in a safe as described above are implemented.

[0030] The beneficial effects of the present invention are: by introducing a machine learning algorithm, combining the signal delay and intensity information, and the geometric characteristics of the object, an accurate judgment of the stacking state of the object is achieved, so as to solve the problem that the existing technology cannot accurately judge the actual stacking situation of the objects in the RFID safe. The stacking state of the object is judged by the delay change of the RFID tag feedback signal, and the accurate judgment of the stacking situation of the object is achieved by accurately measuring and analyzing the signal delay; the Doppler effect is used to estimate the movement speed of the tag, so as to judge the dynamic change of the object; the direction and position of the object are judged by analyzing the signal phase difference, so as to improve the accuracy of the judgment; the stacking state of the object is identified by detecting the intensity change of the RFID tag feedback signal. The attenuation characteristics of the signal intensity are used to judge whether the objects are stacked; the data of signal delay, intensity, Doppler effect and phase difference are combined, and the stacking state of the objects in the cabinet is accurately judged through algorithm comprehensive analysis. The algorithm can effectively distinguish between single-layer placement and multi-layer stacking. The detection accuracy of the stacking status of items in RFID safes is improved, the problem that the existing technology cannot accurately judge the actual stacking situation of items is solved, real-time monitoring of the stacking status of items is realized, the efficiency and accuracy of inventory management are improved, and the possibility of misjudgment and missed judgment is reduced through precise signal analysis and algorithm processing, thereby improving the reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. The drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work:

[0032] Figure 1 A schematic diagram of a flow chart of a method for sensing the stacking status of items in a safe according to a preferred embodiment of the present invention;

[0033] Figure 2 A schematic diagram of a sensing system for sensing the stacking status of items in a safe according to a preferred embodiment of the present invention;

[0034] Figure 3 The figure is a schematic diagram of the internal structure of a smart terminal according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the following will be described clearly and completely in combination with the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are partial embodiments 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 creative work belong to the protection scope of the present invention.

[0036] A method for sensing the stacking status of items in a safe according to a preferred embodiment of the present invention; Figure 1 , which is a flow chart of a method for sensing the stacking status of items in a safe provided by an embodiment of the present invention. The method can be executed by a device, and the device can be implemented by software and / or hardware.

[0037] Specifically, in this embodiment, the method for sensing the stacking state of items in the safe includes:

[0038] S100: collecting feedback signals of labels attached to items;

[0039] In a preferred embodiment of the present invention, each item is first affixed with an RFID tag and then placed in a safe, and the feedback signals of all tags are collected by an RFID reader in the safe to realize signal collection of the RFID tag information on the items.

[0040] Furthermore, the hardware equipment installation in the safe is realized by installing an RFID reader inside the safe to ensure that it can scan the RFID tags of all items, and then triggering the RFID reader to collect signals regularly or when the user operates, so as to obtain the RFID tag feedback signals of the items in the cabinet.

[0041] Furthermore, an RFID tag is attached to an object for identifying the object, wherein the RFID tag includes a chip and an antenna. Then, the signal of the RFID tag is transmitted and received through an RFID reader. The RFID reader includes an antenna, a transmitting module, a receiving module and a signal processing module. The antenna transmits electromagnetic waves, and the RFID signal is sent through the transmitting module. When the RFID signal encounters the RFID tag, the RFID tag reflects the signal back, and the chip modulates and adjusts the signal. The reflected feedback signal is received by the receiving module of the reader, and then the received signal is processed by the signal processing module, so that the RFID reader receives and reads and writes the RFID feedback signal. By obtaining the feedback signal of the object, data support is provided for subsequent feature extraction and model training until the feedback signal collection of all tags is completed, thereby ensuring the accuracy of the data in the subsequent steps.

[0042] S101: extracting features of the feedback signal to obtain time delay, strength and fluctuation information of the feedback signal, and analyzing the feedback signal to obtain a preliminary stacking judgment;

[0043] In a preferred embodiment of the present invention, the feedback signal is processed to extract key features such as delay, strength and fluctuation information of the feedback signal. The signal processing module includes a signal amplifier, a filter, an analog-to-digital converter and a processor, wherein the signal amplifier is used to amplify the weak feedback signal and filter out noise through the filter, and then the analog signal is converted into a digital signal through the analog-to-digital converter, and finally the signal delay, strength and fluctuation information are analyzed by the processor.

[0044] Furthermore, by accurately measuring the delay of the RFID tag feedback signal, the relative position and distance between the objects are analyzed to obtain the signal delay information. Since stacked objects will affect the propagation path and delay, it is possible to determine whether objects are stacked by analyzing the delay data. It is also possible to detect the change in the intensity of the RFID tag feedback signal. When stacked objects appear, the signal strength will be weakened. Therefore, the stacking of objects can be identified by comparing the preset signal strength threshold. Among them, the areas where the feedback signal delay is longer and the strength is weaker are judged as areas where objects may be stacked.

[0045] Furthermore, the fluctuation information includes calculating the radial velocity and angle of the tag feedback signal, wherein the radial velocity of the feedback signal uses the Doppler effect to estimate the movement speed of the tag feedback signal. When the feedback signal moves closer to the reader, the frequency of the reflected signal increases; conversely, when the feedback signal moves away from the reader, the frequency of the reflected signal decreases. By monitoring the frequency change, the radial velocity of the feedback signal can be calculated, thereby realizing the relationship between the change in signal frequency and the movement speed of the feedback signal. The angle of the feedback signal uses the phase difference of phase keying modulation to analyze the direction of the feedback signal. By analyzing the phase difference of the feedback signals received on different antennas, the direction of the feedback signal can be inferred. When the reader transmits a phase-coded signal, the feedback signal will also carry fragrance information, thereby calculating the angle information of the feedback signal, thereby obtaining the distance and angle from the tag to the reader.

[0046] Furthermore, when the RFID reader completes data collection, the signal processing module processes the feedback signal, extracts the delay, strength and fluctuation of the feedback signal, and determines whether the items are stacked. By extracting key features from the original signal, effective input data is provided for model training until the feature extraction of all signals is completed to obtain the delay, strength and fluctuation of all feedback signals, thereby improving the efficiency and accuracy of subsequent model training.

[0047] S102: Model training is performed using a machine learning algorithm and the stacking of items;

[0048] In a preferred embodiment of the present invention, after obtaining the key feature extraction and the actual stacking situation, the model is trained using a machine learning algorithm and combined with the actual stacking situation of the items, and the features are analyzed to generate a judgment model. Through continuous model training and achieving the expected accuracy, a model that can judge the stacking state of the items is obtained, and the accurate judgment of the stacking state of the items is achieved. The data of the feedback signal delay, intensity, radial velocity and angle are combined, and the machine learning algorithm is used for comprehensive analysis to accurately judge the stacking situation of the items in the cabinet, and can also effectively distinguish between single-layer and multi-layer placement, providing more accurate inventory management information.

[0049] Furthermore, the acquisition of training data for the machine learning algorithm is the same as the aforementioned steps and will not be repeated here. That is, the acquisition, analysis and extracted parameters of the delay, intensity, radial velocity and angle data of the RFID tag feedback signal can all be used as the original data of the judgment model of the machine learning algorithm, so as to continuously improve the accuracy of the judgment model.

[0050] S103: Analyze the new feedback signal using the trained model to determine the stacking status of the objects;

[0051] In a preferred embodiment of the present invention, the trained model is used to analyze new feedback signals to determine whether items are stacked. The analysis of all new feedback signals is completed to obtain the judgment results of whether all items are stacked, thereby continuously improving the efficiency and accuracy of item management. In addition, new feedback signals are continuously used as data of the trained model to retrain the model to continuously improve the model's judgment on the stacking of items and the accuracy of the judgment.

[0052] S104: Processing and storing the analysis and judgment results;

[0053] In a preferred embodiment of the present invention, the above analysis structure is processed and stored in a data management module to generate detailed information and stacking status of the items, and the generated item information is stored in a database, which is convenient for users to query and manage the analysis results. The data management module includes a memory and management software, the memory is used to store the analysis structure of the signal processing module, the management software provides an interface for users to query and manage items, and the analysis results are stored in the data management module, which is convenient for users to query and manage.

[0054] By introducing the machine learning algorithm through the above method, combined with the signal delay and strength information, as well as the geometric characteristics of the objects, the accurate judgment of the stacking state of the objects can be achieved, which can effectively improve the accuracy of the judgment. And the model can be adjusted according to different environments and the way the objects are placed, and has strong adaptability. The optimized algorithm can complete the calculation in a short time and is suitable for real-time applications. By analyzing the delay and strength of the feedback signal of the RFID tag, the stacking state and specific location of the objects can be accurately perceived, and the recognition accuracy can be improved; not only can the RFID tag of the object be identified, but also the stacking state and specific location of the object can be obtained, and refined management and access operations can be achieved, and the information feedback is more comprehensive; by accurately sensing and managing the stacking state of the objects, users can find and access items more conveniently, improving work efficiency and user experience.

[0055] Corresponding to a method for sensing the stacking state of items in a safe, the present invention also provides a system for sensing the stacking state of items in a safe, specifically, as follows: Figure 2 As shown, the sensing system for the stacking status of items in the safe includes: a data acquisition module 200, a data processing module 300, a data judgment module 400 and a data management module 500.

[0056] The data acquisition module 200 obtains the feedback signal of the label attached to the items in the safe;

[0057] Specifically, each item is first affixed with an RFID tag and then placed in a safe, and the feedback signals of all tags are collected by the RFID reader in the safe to realize signal collection of the RFID tag information on the items.

[0058] Furthermore, the hardware equipment installation in the safe is realized by installing an RFID reader inside the safe to ensure that it can scan the RFID tags of all items, and then triggering the RFID reader to collect signals regularly or when the user operates, so as to obtain the RFID tag feedback signals of the items in the cabinet.

[0059] Furthermore, an RFID tag is attached to an object for identifying the object, wherein the RFID tag includes a chip and an antenna. Then, the signal of the RFID tag is transmitted and received through an RFID reader. The RFID reader includes an antenna, a transmitting module, a receiving module and a signal processing module. The antenna transmits electromagnetic waves, and the RFID signal is sent through the transmitting module. When the RFID signal encounters the RFID tag, the RFID tag reflects the signal back, and the chip modulates and adjusts the signal. The reflected feedback signal is received by the receiving module of the reader, and then the received signal is processed by the signal processing module, so that the RFID reader receives and reads and writes the RFID feedback signal. By obtaining the feedback signal of the object, data support is provided for subsequent feature extraction and model training until the feedback signal collection of all tags is completed, thereby ensuring the accuracy of the data in the subsequent steps.

[0060] The data processing module 300 performs feature extraction on the feedback signal to obtain key features of the feedback signal;

[0061] Specifically, the feedback signal is processed to extract key features such as the time delay, strength and fluctuation information of the feedback signal. The signal processing module includes a signal amplifier, a filter, an analog-to-digital converter and a processor. The signal amplifier is used to amplify the weak feedback signal and filter out the noise through the filter. The analog signal is then converted into a digital signal through the analog-to-digital converter. Finally, the signal delay, strength and fluctuation information are analyzed by the processor.

[0062] Furthermore, by accurately measuring the delay of the RFID tag feedback signal, the relative position and distance between the objects are analyzed to obtain the signal delay information. Since stacked objects will affect the propagation path and delay, it is possible to determine whether objects are stacked by analyzing the delay data. It is also possible to detect the change in the intensity of the RFID tag feedback signal. When stacked objects appear, the signal strength will be weakened. Therefore, the stacking of objects can be identified by comparing the preset signal strength threshold. Among them, the areas where the feedback signal delay is longer and the strength is weaker are judged as areas where objects may be stacked.

[0063] Furthermore, the fluctuation information includes calculating the radial velocity and angle of the tag feedback signal, wherein the radial velocity of the feedback signal uses the Doppler effect to estimate the movement speed of the tag feedback signal. When the feedback signal moves closer to the reader, the frequency of the reflected signal increases; conversely, when the feedback signal moves away from the reader, the frequency of the reflected signal decreases. By monitoring the frequency change, the radial velocity of the feedback signal can be calculated, thereby realizing the relationship between the change in signal frequency and the movement speed of the feedback signal. The angle of the feedback signal uses the phase difference of phase keying modulation to analyze the direction of the feedback signal. By analyzing the phase difference of the feedback signals received on different antennas, the direction of the feedback signal can be inferred. When the reader transmits a phase-coded signal, the feedback signal will also carry fragrance information, thereby calculating the angle information of the feedback signal, thereby obtaining the distance and angle from the tag to the reader.

[0064] Furthermore, when the RFID reader completes data collection, the signal processing module processes the feedback signal, extracts the delay, strength and fluctuation of the feedback signal, and determines whether the items are stacked. By extracting key features from the original signal, effective input data is provided for model training until the feature extraction of all signals is completed to obtain the delay, strength and fluctuation of all feedback signals, thereby improving the efficiency and accuracy of subsequent model training.

[0065] The data judgment module 400 obtains a preliminary judgment on the stacking of objects based on the key feature analysis of the feedback signal;

[0066] Specifically, a preliminary judgment on the stacking of items can be obtained by analyzing the closed-in features of the feedback signal. After obtaining the key feature extraction and stacking judgment, the model is trained and the features are analyzed using a machine learning algorithm in combination with the actual stacking of items to generate a judgment model. Through continuous model training and achieving the expected accuracy, a model that can judge the stacking state of items is obtained, thereby achieving accurate judgment on the stacking state of items. The data on the feedback signal delay, intensity, radial velocity and angle are combined, and a comprehensive analysis is performed through a machine learning algorithm to accurately judge the stacking state of items in the cabinet, and to effectively distinguish between single-layer and multi-layer placement, providing more accurate inventory management information.

[0067] Furthermore, the acquisition of training data for the machine learning algorithm is the same as the aforementioned steps and will not be repeated here. That is, the acquisition, analysis and extracted parameters of the delay, intensity, radial velocity and angle data of the RFID tag feedback signal can all be used as the original data of the judgment model of the machine learning algorithm, so as to continuously improve the accuracy of the judgment model.

[0068] Furthermore, the trained model is used to analyze new feedback signals to determine whether items are stacked. This process is repeated until all new feedback signals are analyzed to determine whether all items are stacked, thereby continuously improving the efficiency and accuracy of item management. New feedback signals are also used as data for the trained model to train the model again, thereby continuously improving the model's ability to determine the stacking of items and the accuracy of the determination.

[0069] The data management module 500 processes and stores the feature extraction and judgment results.

[0070] Specifically, the above analysis structure is processed and stored in the data management module to generate detailed information and stacking status of the items, and the generated item information is stored in the database, which is convenient for users to query and manage the analysis results. The data management module includes a memory and management software, the memory is used to store the analysis structure of the signal processing module, the management software provides an interface for users to query and manage items, and the analysis results are stored in the data management module, which is convenient for users to query and manage.

[0071] Based on the above embodiments, the present invention further provides an intelligent terminal, whose principle block diagram is as follows: Figure 3 As shown, the above-mentioned intelligent terminal includes a processor, a memory, a network interface and a display screen connected through a system bus. The processor of the intelligent terminal is used to provide computing and control capabilities. The memory of the intelligent terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a sensing program for the stacking status of items in the safe. The memory provides an environment for the operation of the operating system and the sensing program for the stacking status of items in the safe in the non-volatile storage medium. The network interface of the intelligent terminal is used to communicate with an external terminal through a network connection. When the sensing program for the stacking status of items in the safe is executed by the processor, the steps of any one of the above-mentioned sensing methods for the stacking status of items in the safe are implemented. The display screen of the intelligent terminal can be a liquid crystal display screen or other display screens.

[0072] Those skilled in the art will understand that Figure 3 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the scheme of the present invention, and does not constitute a limitation on the smart terminal to which the scheme of the present invention is applied. The specific smart terminal may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0073] In an embodiment of the present invention, a smart terminal is provided. The smart terminal includes a memory, a processor, and a sensing program for the stacking state of items in a safe stored in the memory and executable on the processor. When the sensing program for the stacking state of items in the safe is executed by the processor, the following operation instructions are performed:

[0074] Put the items with RFID tags on their surface into the safe;

[0075] The RFID reader in the safe scans the RFID tag of the item to obtain a feedback signal;

[0076] Extract key features of the feedback signal, including the delay, intensity and fluctuation information of the feedback signal;

[0077] Analyze the delay, strength and fluctuation information of the feedback signal;

[0078] Determine the area where stacking may exist based on the key feature analysis results of the feedback signal;

[0079] Model training is performed using machine learning algorithms and key features of feedback signals and item stacking status;

[0080] Use the trained model to analyze new feedback signals and determine the stacking status of items;

[0081] The model is trained again with new feedback signals and item stacking conditions;

[0082] The analysis and judgment results are processed and stored.

[0083] An embodiment of the present invention further provides a computer-readable storage medium, on which a sensing program for the stacking status of items in a safe is stored. When the sensing program for the stacking status of items in a safe is executed by a processor, the steps of any one of the methods for sensing the stacking status of items in a safe provided in the embodiments of the present invention are implemented.

[0084] It should be understood that the present invention is described by some embodiments, and those skilled in the art will appreciate that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A method for sensing the stacking status of items in a safe, characterized in that: include: Obtain feedback signals from labels attached to items in the safe; Perform feature extraction on the feedback signal to obtain key features of the feedback signal; A preliminary judgment of the stacking of items is obtained based on the key feature analysis of the feedback signal; The analysis and judgment results are processed and stored.

2. The sensing method according to claim 1, characterized in that: The feature extraction of the feedback signal to obtain key features of the feedback signal includes: At least one of the time delay, intensity, speed and angle of the feedback signal is obtained, and the stacking condition of the objects is determined based on the information.

3. The sensing method according to claim 2, characterized in that: By measuring the time delay of the feedback signal returning the signal, the relative distance and position between the objects are analyzed to determine the stacking state of the objects; and / or comparing the intensity of the feedback signal with a preset signal intensity threshold to determine the stacking state of the objects; and\or calculating the speed of the feedback signal by the change value of the reflection frequency of the feedback signal to determine the stacking state of the objects; And\or by analyzing the phase difference of the feedback signal, calculating the direction and angle of the feedback signal to determine the stacking state of the objects.

4. The sensing method according to claim 1, characterized in that: After obtaining a preliminary object stacking judgment based on the key feature analysis of the feedback signal, the method further includes: The model is trained using a machine learning algorithm and the key features of the feedback signal and the stacking status of the objects to obtain a trained judgment model, and the trained judgment model is used to analyze new feedback signals to determine the stacking status of the objects.

5. The sensing method according to claim 4, characterized in that: The analyzing the new feedback signal by using the trained judgment model to judge the stacking condition of the objects also includes: The analysis results and judgment situations are processed and stored, and the new feedback signal analysis and judgment results are used as training data for training the judgment model to train the model to improve the accuracy of the judgment model.

6. The sensing method according to claim 1, characterized in that: The step of obtaining the feedback signal of the label attached to the item in the safe further comprises: The attached tag includes an antenna and a chip, and obtains a feedback signal through a reader. After the reader sends a signal, the chip modulates and adjusts it, and then the signal is reflected through the antenna to form a feedback signal.

7. The sensing method according to claim 6, characterized in that: The reader / writer comprises an antenna, a transmitting module, a receiving module and a signal processing module. The transmitting module sends a signal, the receiving module receives a feedback signal, and the signal processing module analyzes the feedback signal to extract key features of the feedback signal.

8. A sensing program for the stacking status of items in a safe, characterized in that: include: A data acquisition module obtains feedback signals from labels attached to items in the safe; The data processing module extracts the features of the feedback signal to obtain key features; The data judgment module obtains preliminary item stacking judgment based on the key feature analysis of the feedback signal; The data management module processes and stores the feature extraction and judgment results.

9. An intelligent terminal, characterized in that: The intelligent terminal includes at least one memory, at least one processor, and a sensing program for the stacking status of items in a safe stored in the memory and executable on the processor. When the sensing program for the stacking status of items in a safe is executed by the processor, the steps of the sensing method for the stacking status of items in a safe as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a sensing program for the stacking status of items in a safe, and when the sensing program for the stacking status of items in a safe is executed by a processor, the steps of the sensing method for the stacking status of items in a safe are implemented as described in any one of claims 1 to 7.

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