RFID tag feature extraction and identification method and system based on deep learning

Abstract

The invention relates to the technical field of RFID tags, and particularly discloses an RFID tag feature extraction and identification method and system based on deep learning, and the method mainly comprises the steps: obtaining an RFID reader-writer, converting a radio frequency signal into electric signal data, selecting a specific feature to construct first input data, and carrying out the recognition of the first input data; processing by using a first deep learning model to obtain a first recognition result and a feature contribution degree, determining important features according to the contribution degree, obtaining electric signal data, extracting the important features to form second input data, and processing by using a second model to obtain a second recognition result. According to the method, the RFID electrical signal data is acquired, the specific features are selected to construct the input, the deep learning model is used for identifying and optimizing the features, and the final identification result is obtained through processing, so that the identification accuracy of the RFID tag can be remarkably improved, the tracking efficiency and reliability of the logistics information are greatly improved, inventory management is optimized, the risk of goods loss is effectively reduced, and the economic benefit is improved. And the safety and efficiency of the whole logistics operation are improved.

Description

Technical Field

[0001] The present application relates to the field of RFID tag technology, and in particular to a method and system for extracting and identifying RFID tags based on deep learning. Background Art

[0002] RFID tags are widely used in logistics. The principle is to use radio waves for communication to realize information transmission between the reader and the electronic tag. When the goods with RFID tags enter the working area of ​​the reader, the radio frequency signal emitted by the reader will activate the tag, and the tag will then transmit its own encoded information back to the reader through the built-in antenna to complete the information reading.

[0003] However, in the real environment, there may be various interference sources such as wireless devices and metal structures. The electromagnetic waves they generate may interfere with the transmission of RFID signals, resulting in a decrease in the quality of the signal received by the reader. The quality of the RFID tag will also affect its ability to be recognized by the reader. For example, chip damage, poor antenna design or improper attachment method may cause the reader to be unable to accurately read or decode the signal returned by the tag, resulting in inaccurate logistics information tracking, chaotic inventory management, increased risk of cargo loss and other shortcomings, seriously affecting logistics efficiency and accuracy, and causing unnecessary losses to companies and customers. Summary of the invention

[0004] The present application provides a method and system for RFID tag feature extraction and recognition based on deep learning, which effectively solves the problem in the prior art that environmental interference and tag quality issues affect the transmission of RFID signals, causing the reader to encounter difficulties in reading or decoding the signal returned by the tag, thereby affecting the tracking efficiency and accuracy of logistics information. The application can significantly improve the recognition accuracy of RFID tags, thereby greatly improving the tracking efficiency and reliability of logistics information, helping to optimize inventory management, effectively reduce the risk of cargo loss, and improve the safety and efficiency of overall logistics operations.

[0005] In a first aspect, the present application provides an RFID tag feature extraction and recognition method based on deep learning, comprising: obtaining electrical signal data; wherein the electrical signal data is converted from a radio frequency signal returned by an RFID reader to an RFID tag, and the electrical signal data contains features of multiple categories; determining specific features from features of all categories, and constructing first input data containing the specific features; using a first deep learning model to process the first input data to obtain a first recognition result, as well as the degree of contribution of each category of features to the first recognition result; determining important features from all specific features according to the degree of contribution of each category of features to the first recognition result; obtaining a recognition result of the radio frequency signal returned by the RFID reader to the RFID tag, and obtaining electrical signal data converted from the radio frequency signal according to the recognition result; extracting important features from the electrical signal data, and constructing second input data containing the important features; using a second deep learning model to process the second input data to obtain a second recognition result.

[0006] By acquiring RFID electrical signal data, selecting specific features to construct inputs, using deep learning models to identify and optimize features, and then processing the final recognition results, the recognition accuracy of RFID tags can be significantly improved, thereby greatly improving the tracking efficiency and reliability of logistics information, helping to optimize inventory management, effectively reducing the risk of cargo loss, and improving the safety and efficiency of overall logistics operations.

[0007] Furthermore, the specific feature includes at least one of the following: environmental parameters, specific attributes of the tag, and timestamp and location information.

[0008] Furthermore, determining important features among all specific features according to the degree of contribution of features of each category to the first recognition result includes: determining relevant features among all specific features according to the degree of contribution of features of each category to the first recognition result; constructing all possible subsets of relevant features of all categories; wherein each subset includes at least one relevant feature; constructing data samples; wherein each data sample contains a subset of one category, and all data samples contain subsets of all categories; training a second deep learning model one by one using each data sample; applying the trained second deep learning model to a validation set or a test set, and calculating performance indicators; and determining important features based on the performance indicators.

[0009] Further, determining relevant features among all specific features according to the contribution degree of each category of features to the first recognition result includes: taking specific features whose contribution degree is greater than a preset first threshold as relevant features.

[0010] Furthermore, performance indicators are calculated using evaluation functions in the machine learning framework or custom scripts.

[0011] Furthermore, determining the important features according to the performance indicators includes: screening out a subset whose performance indicators are greater than a preset second threshold and whose feature types are the least; and taking the relevant features included in the screened subset as the important features.

[0012] Furthermore, the recognition result includes success or failure; obtaining the recognition result of the radio frequency signal returned by the RFID reader / writer to the RFID tag includes: obtaining the first recognition result of the radio frequency signal returned by the RFID reader / writer to the RFID tag; determining whether the first recognition result is a failure; for the RFID tag whose first recognition result is a failure, continuously obtaining the recognition result of the RFID reader / writer for the RFID tag according to a preset time interval or condition until a preset first time period is reached or the recognition result is successful.

[0013] Further, obtaining the electrical signal data converted from the radio frequency signal according to the recognition result includes: determining an RFID tag whose recognition result is still failed after reaching a preset first time period; and obtaining the electrical signal data converted from the radio frequency signal returned by the RFID tag.

[0014] Furthermore, the first deep learning model and the second deep learning model are both long short-term memory networks.

[0015] In the second aspect, the present application provides an RFID tag feature extraction and recognition system based on deep learning, which adopts the RFID tag feature extraction and recognition method based on deep learning as described in the first aspect, and includes: a data acquisition module, a specific feature screening and input data construction module, a feature contribution identification module, a feature contribution evaluation and signal recognition module, an input data construction module and a tag recognition module.

[0016] The data acquisition model is used to acquire electrical signal data; wherein the electrical signal data is converted from the radio frequency signal returned by the RFID reader to the RFID tag, and the electrical signal data contains multiple categories of features; the specific feature screening and input data construction module is used to determine specific features from all categories of features and construct first input data containing the specific features; the feature contribution identification module is used to process the first input data using a first deep learning model to obtain a first recognition result, and the contribution degree of each category of features to the first recognition result; the feature contribution evaluation and signal recognition module is used to determine important features among all specific features according to the contribution degree of each category of features to the first recognition result; obtain the recognition result of the radio frequency signal returned by the RFID reader to the RFID tag, and obtain the electrical signal data converted from the radio frequency signal according to the recognition result; the input data construction module is used to extract important features in the electrical signal data and construct second input data containing the important features; the tag recognition module is used to process the second input data using a second deep learning model to obtain a second recognition result.

[0017] In a third aspect, the present application provides an RFID tag feature extraction and identification device based on deep learning, which includes: a memory and a processor.

[0018] The memory is used to store computer programs; the processor is used to implement the steps of the RFID tag feature extraction and recognition method based on deep learning as described in the first aspect when executing the computer program.

[0019] In a fourth aspect, the present application provides a readable storage medium, which stores computer program instructions. When the computer program instructions are read and executed by a processor, the steps of the RFID tag feature extraction and recognition method based on deep learning as described in the first aspect are executed.

[0020] The technical solution provided by this application has at least the following technical effects or advantages: By acquiring RFID electrical signal data, selecting specific features to construct inputs, using deep learning models to identify and optimize features, and then processing the final recognition results, it effectively solves the problem in the existing technology that environmental interference and tag quality issues affect the transmission of RFID signals, causing the reader to encounter difficulties in reading or decoding the signal returned by the tag, thereby affecting the tracking efficiency and accuracy of logistics information. It can significantly improve the recognition accuracy of RFID tags, thereby greatly improving the tracking efficiency and reliability of logistics information, helping to optimize inventory management, effectively reduce the risk of goods loss, and improve the safety and efficiency of overall logistics operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1This is a flow chart of the RFID tag feature extraction and recognition method based on deep learning in Example 1 of the present application; Figure 2 For this application Figure 1 A schematic diagram of a process for determining important features among all specific features according to the contribution of each category of features to the first recognition result; Figure 3 For this application Figure 1 A schematic diagram of a process of obtaining electrical signal data converted from the radio frequency signal according to the recognition result; Figure 4 This is a module diagram of the RFID tag feature extraction and recognition system based on deep learning in Example 2 of the present application. DETAILED DESCRIPTION

[0022] In order to solve the problems raised by the background technology, this application obtains RFID electrical signal data, selects specific features to construct input, uses a deep learning model to identify and optimize the features, and then processes the final recognition results to solve the problem that environmental interference and tag quality affect RFID signal transmission, causing difficulties in reading or decoding by the reader / writer. It can significantly improve the recognition accuracy of RFID tags, thereby greatly improving the tracking efficiency and reliability of logistics information, helping to optimize inventory management, effectively reduce the risk of goods loss, and improve the safety and efficiency of overall logistics operations.

[0023] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0024] Embodiment 1: like Figure 1-Figure 3 As shown, this embodiment provides an RFID tag feature extraction and recognition method based on deep learning, including: S100. Acquire electrical signal data; wherein the electrical signal data is converted from the radio frequency signal returned by the RFID reader to the RFID tag, and the electrical signal data contains multiple types of features.

[0025] The RFID reader is responsible for sending radio frequency signals to activate the RFID tag and receiving the radio frequency signals returned by the tag. The radio frequency circuit is usually composed of components such as the antenna system, RF transceiver, amplifier, tuner, oscillator, etc. When the RFID reader sends radio frequency signals, these signals are radiated through the antenna system to activate the RFID tag. After receiving the radio frequency signal, the RFID tag will return the stored information in the form of radio frequency signals. After receiving these returned radio frequency signals, the antenna system of the RFID reader passes them to the RF transceiver. The RF transceiver is responsible for converting the received radio frequency signals into electrical signals, which are then further processed and decoded to extract the information in the tag.

[0026] The electrical signal data not only contains the original information of the RFID tag, but also contains various types of features. These features may involve physical properties such as signal strength, frequency, phase, etc., and may also contain complex information related to tag location, environmental interference, etc.

[0027] S200. Determine specific features from features of all categories, and construct first input data containing the specific features.

[0028] In certain cases, some specific features may contain additional information about the RFID tag or the environment, such as environmental parameters, specific attributes of the tag, timestamps, and location information. Environmental parameters such as temperature and humidity, although they may not be necessary for basic RFID tag recognition, in environments such as warehouse management and cold chain logistics, changes in these parameters may affect the transmission of RF signals and the performance of the tag, thereby affecting the accuracy of RFID tag recognition. RFID tags may have specific physical properties, such as size, material, shape, etc., or electronic properties, such as capacitance, inductance, etc., which may affect the reflection and transmission of RF signals.

[0029] The specific feature includes at least one of the following: environmental parameters, specific attributes of the tag, and timestamp and location information.

[0030] In addition, the time point and location information of the tag being read may also affect the recognition results. For example, in real-time tracking and monitoring systems, the addition of timestamp and location information can help the model better understand the movement trajectory and state changes of the tag, thereby improving the accuracy of recognition. Although these features may not be necessary for recognition in most cases, in some specific application scenarios, the addition of these features may help improve the accuracy of recognition.

[0031] Specific features are selected from features of all categories, and the first input data containing the specific features is constructed for subsequent training of the deep learning model.

[0032] S300. Use the first deep learning model to process the first input data to obtain a first recognition result and the degree of contribution of each category of features to the first recognition result.

[0033] A trained first deep learning model is used to process first input data containing specific features. The model uses its internal complex neural network structure to extract, combine and classify features of the input data to obtain recognition results. The model evaluates the contribution of each category of features to the recognition results through the feature weights and importance learned during the training process. By understanding which features have a significant impact on the recognition results, feature selection can be further optimized to improve recognition accuracy and efficiency.

[0034] S400. Determine important features among all specific features according to the contribution of each category of features to the first recognition result; obtain the recognition result of the RFID reader / writer on the radio frequency signal returned by the RFID tag, and obtain electrical signal data converted from the radio frequency signal according to the recognition result.

[0035] According to the contribution of each category feature output by the first deep learning model to the first recognition result, important features that have a significant impact on the recognition result can be screened out from all specific features. These important features usually contain the key information required for the recognition task. Using these important features to train the deep model can not only reduce the difficulty of training, because the model no longer needs to process a large number of redundant and noisy features, but also significantly reduce the amount of calculation, improve the training speed and recognition efficiency, and the model can learn the essential laws and patterns of the data by training on fewer but more critical features.

[0036] Environmental interference and tag quality issues often affect the transmission of RFID signals, causing the reader to have difficulty reading or decoding the signal returned by the tag. In this case, the RFID reader can obtain the recognition result of the radio frequency signal returned by the RFID tag, and obtain the electrical signal data converted from the radio frequency signal based on the recognition result, so as to subsequently identify and extract important features in specific features.

[0037] S500. Extract important features from the electrical signal data, and construct second input data containing the important features.

[0038] These important features are accurately extracted from the electrical signal data converted from the radio frequency signal returned by the RFID reader, and these important features are input into the deep learning model as part of the input data, thereby streamlining the data set, which not only reduces the computational burden of the model but also improves recognition efficiency.

[0039] S600. Use a second deep learning model to process the second input data to obtain a second recognition result.

[0040] The second deep learning model is used to process the second input data in S500 to obtain the second recognition result. The recognition result can be accompanied by an output of an accuracy index to measure the reliability of the current recognition result. The accuracy can be a probability value calculated by the model based on internal calculations, indicating the degree of confidence in the recognition result. In addition, an accuracy threshold can be set according to the actual application scenario and needs. When the accuracy of the recognition result of the model is higher than the threshold, the system can automatically accept the result. When the accuracy is lower than the threshold, manual review or operation process is triggered. Outputting the recognition result with accuracy, and combining threshold judgment with manual review, can significantly improve the automation and accuracy of RFID tag recognition, reduce misjudgments and missed judgments, and at the same time reduce the frequency and cost of manual operations, thereby improving overall work efficiency and user experience.

[0041] Therefore, tags that might not have been recognized originally due to various reasons now have a higher probability of being accurately recognized by the model. This improvement not only enhances the recognition capability of the RFID system, but also broadens its scope of application in practical applications.

[0042] In S400, determining important features among all specific features according to the contribution of each category of features to the first recognition result includes: S410. Determine relevant features among all specific features according to the contribution degree of each category of features to the first recognition result.

[0043] Based on the contribution of each category feature output by the first deep learning model to the first recognition result, features with a greater correlation with RFID tag recognition are screened out. These relevant features have a significant impact on the accuracy of the recognition result.

[0044] S420. Construct all possible subsets of all types of relevant features; wherein each subset includes at least one relevant feature.

[0045] After determining the relevant features, all possible subsets of these features are constructed. Each subset contains at least one relevant feature, and these subsets cover all possible combinations of relevant features. In subsequent steps, by comparing the performance of different subsets in model training, we can determine which feature combinations are most critical to the recognition results.

[0046] S430. Construct data samples; wherein each data sample contains a subset of a category, and all data samples contain subsets of all categories.

[0047] After determining the relevant features, we construct all possible subsets of these features. Each subset contains at least one relevant feature, and these subsets cover all possible combinations of relevant features. By comparing the performance of different subsets in model training, we can determine which feature combinations are most critical to the recognition results.

[0048] S440. Use each data sample one by one to train the second deep learning model.

[0049] Each data sample is used one by one to train the second deep learning model. Since each model is trained based on a specific subset of features, their performance will directly reflect the effectiveness of that subset in identifying RFID tags.

[0050] S450. Apply the trained second deep learning model to the validation set or the test set, and calculate the performance index.

[0051] After training the models, we apply them to the validation set or test set to evaluate their performance. By calculating performance metrics, we can quantify how well the models perform in identifying RFID tags.

[0052] S460. Determine important characteristics based on performance indicators.

[0053] By comparing the performance of different subsets in model training, we can determine which feature combinations are most critical to the recognition results and thus mark these features as important features.

[0054] In S410, determining relevant features among all specific features according to the contribution degree of each category of features to the first recognition result includes: taking specific features whose contribution degree is greater than a preset first threshold as relevant features.

[0055] The first threshold represents the minimum standard for a feature to have a significant impact on the recognition result. The contribution of each specific feature to the first recognition result is evaluated one by one and compared with the preset first threshold. If the contribution of a feature is greater than this threshold, it is considered that this feature has a greater relevance to the recognition of the RFID tag, and is therefore marked as a relevant feature.

[0056] In S450, performance indicators are calculated using evaluation functions or custom scripts in the machine learning framework. These evaluation tools can calculate key performance indicators such as accuracy and recall, thereby accurately evaluating the recognition ability of the model.

[0057] In S460, important features are determined based on performance indicators including: S461. Filter out a subset whose performance index is greater than a preset second threshold and has the least number of feature types.

[0058] S462. Take the relevant features contained in the selected subset as important features.

[0059] In S461-S462, the preset second threshold represents an acceptable level of model performance, and then the subsets with performance indicators greater than this threshold and the least number of features are screened out, which not only achieve high recognition performance but also use the least number of features. Therefore, while maintaining a high recognition accuracy, the model calculation amount can be reduced and the model operation efficiency can be improved, thereby achieving dual optimization of model performance and calculation efficiency.

[0060] In S400, the recognition result includes success or failure; obtaining the recognition result of the RFID reader / writer on the radio frequency signal returned by the RFID tag includes: S470. Obtain the first recognition result of the RFID reader / writer on the radio frequency signal returned by the RFID tag.

[0061] S480. Determine whether the first recognition result is a failure.

[0062] S490. For an RFID tag whose first recognition result is a failure, the recognition result of the RFID tag by the RFID reader is continuously obtained according to a preset time interval or condition until the preset first time period is reached or the recognition result is successful.

[0063] The recognition process of RFID tags presents two clear result states: success and failure. In actual application scenarios, due to various temporary interference factors that may exist in the environment, such as the tag being temporarily blocked by other objects, the RFID reader is sometimes unable to immediately and accurately recognize the tag information. In order to cope with this temporary recognition obstacle, when the RFID reader fails to successfully recognize the tag for the first time, it will not immediately start a complex model for recognition and decoding, but will continuously obtain the recognition results of the tag from the RFID reader according to the preset time interval or conditions.

[0064] Acquiring electrical signal data converted from radio frequency signals according to identification results includes: determining an RFID tag whose identification result is still a failure after reaching a preset first time period; and acquiring electrical signal data converted from radio frequency signals returned by the RFID tag.

[0065] If the interference is temporary, then after a period of time, the RFID reader may successfully identify the tag information, and the impact of environmental interference on the RFID recognition process can be effectively dealt with without adding additional computing costs. Only when continuous attempts to identify fail, a more complex model is used for further identification and decoding to ensure the accuracy and reliability of identification.

[0066] The first deep learning model and the second deep learning model are both long short-term memory networks. As a special recurrent neural network, LSTM is good at capturing long-term dependencies in data. LSTM can also effectively process long sequence data, which enables it to accurately identify the radio frequency signal returned by the RFID reader without adding too much computational burden, which can significantly improve the accuracy and reliability of RFID tag recognition.

[0067] Embodiment 2: like Figure 4 As shown, this embodiment provides an RFID tag feature extraction and recognition system based on deep learning, which adopts the RFID tag feature extraction and recognition method based on deep learning as in Example 1, and includes: a data acquisition module, a specific feature screening and input data construction module, a feature contribution identification module, a feature contribution evaluation and signal recognition module, an input data construction module and a tag recognition module.

[0068] The data acquisition model is used to acquire electrical signal data; wherein the electrical signal data is converted from the radio frequency signal returned by the RFID reader to the RFID tag, and the electrical signal data contains multiple categories of features; the specific feature screening and input data construction module is used to determine specific features from all categories of features and construct first input data containing specific features; the feature contribution identification module is used to process the first input data using the first deep learning model to obtain a first recognition result, as well as the contribution of each category of features to the first recognition result; the feature contribution evaluation and signal recognition module is used to determine important features among all specific features based on the contribution of each category of features to the first recognition result; the recognition result of the radio frequency signal returned by the RFID reader to the RFID tag is obtained, and the electrical signal data converted from the radio frequency signal is obtained based on the recognition result; the input data construction module is used to extract important features from the electrical signal data and construct second input data containing important features; the tag recognition module is used to process the second input data using the second deep learning model to obtain a second recognition result.

[0069] This embodiment has the advantages of the RFID tag feature extraction and recognition method based on deep learning in Embodiment 1, and can automatically implement the steps of the RFID tag feature extraction and recognition method based on deep learning.

[0070] Embodiment three: This embodiment provides an RFID tag feature extraction and recognition device based on deep learning, which includes: a memory and a processor.

[0071] The memory is used to store computer programs; the processor is used to implement the steps of the RFID tag feature extraction and recognition method based on deep learning in Example 1 when executing the computer program.

[0072] Embodiment 4: This embodiment provides a readable storage medium, in which computer program instructions are stored. When the computer program instructions are read and executed by a processor, the steps of the RFID tag feature extraction and recognition method based on deep learning in Embodiment 1 are executed.

[0073] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

Claims

1. RFID tag feature extraction and recognition method based on deep learning, characterized in that: include: Acquire electrical signal data; wherein the electrical signal data is converted from the radio frequency signal returned by the RFID reader to the RFID tag, and the electrical signal data contains multiple types of features; Determine a specific feature from features of all categories, and construct first input data containing the specific feature; Processing the first input data using a first deep learning model to obtain a first recognition result and a contribution degree of each category of features to the first recognition result; Determine the important features among all the specific features according to the contribution of each category of features to the first recognition result; obtain the recognition result of the RFID reader / writer on the radio frequency signal returned by the RFID tag, and obtain the electrical signal data converted from the radio frequency signal according to the recognition result; Extracting important features from the electrical signal data, and constructing second input data containing the important features; The second input data is processed using a second deep learning model to obtain a second recognition result.

2. The RFID tag feature extraction and recognition method based on deep learning according to claim 1, characterized in that: The specific feature includes at least one of the following: environmental parameters, specific attributes of the tag, and timestamp and location information.

3. The RFID tag feature extraction and recognition method based on deep learning as claimed in claim 1, characterized in that: The important features are determined among all specific features according to the contribution of each category of features to the first recognition result, including: Determining relevant features among all specific features according to the contribution of each category of features to the first recognition result; Constructing all possible subsets of all types of relevant features; wherein each subset includes at least one relevant feature; Construct data samples; each data sample contains a subset of a category, and all data samples contain subsets of all categories; Using each data sample one by one to train a second deep learning model; Apply the trained second deep learning model to the validation set or test set and calculate the performance metrics; Determine important features based on performance indicators.

4. The RFID tag feature extraction and recognition method based on deep learning as claimed in claim 3, characterized in that: The relevant features are determined among all specific features according to the contribution of each category of features to the first recognition result, including: The specific features whose contribution degree is greater than a preset first threshold are taken as relevant features.

5. The RFID tag feature extraction and recognition method based on deep learning as claimed in claim 3, characterized in that: Calculate performance metrics using evaluation functions in machine learning frameworks or custom scripts.

6. The RFID tag feature extraction and recognition method based on deep learning as claimed in claim 3, characterized in that: Important characteristics determined based on performance indicators include: Filter out a subset whose performance index is greater than a preset second threshold and whose feature types are the least; The relevant features contained in the filtered subset are taken as important features.

7. The RFID tag feature extraction and recognition method based on deep learning according to claim 1, characterized in that: The recognition result includes success or failure; the recognition result of the RFID reader / writer on the radio frequency signal returned by the RFID tag includes: Obtain the first recognition result of the RFID reader / writer on the radio frequency signal returned by the RFID tag; Determine whether the first recognition result is a failure; For an RFID tag whose first recognition result is a failure, the recognition result of the RFID tag by the RFID reader is continuously obtained according to a preset time interval or condition until the preset first time period is reached or the recognition result is successful.

8. The RFID tag feature extraction and recognition method based on deep learning as claimed in claim 7, characterized in that: Acquiring electrical signal data converted from the radio frequency signal according to the recognition result includes: Determine the RFID tags for which the identification result is still a failure after reaching the preset first time period; The electrical signal data converted from the radio frequency signal returned by the RFID tag is obtained.

9. The RFID tag feature extraction and recognition method based on deep learning according to claim 1, characterized in that: The first deep learning model and the second deep learning model are both long short-term memory networks.

10. A RFID tag feature extraction and recognition system based on deep learning, characterized in that: include: Data acquisition module: The data acquisition model is used to acquire electrical signal data; wherein the electrical signal data is converted from the radio frequency signal returned by the RFID reader to the RFID tag, and the electrical signal data contains multiple types of features; Specific feature screening and input data construction module: The specific feature screening and input data construction module is used to determine specific features from all categories of features and construct first input data containing the specific features; Feature contribution identification module: The feature contribution identification module is used to process the first input data using a first deep learning model to obtain a first recognition result and a contribution degree of each category of features to the first recognition result; Feature contribution evaluation and signal identification module: The feature contribution evaluation and signal identification module is used to determine the important features among all specific features according to the contribution of each category of features to the first identification result; obtain the identification result of the RFID reader / writer on the radio frequency signal returned by the RFID tag, and obtain the electrical signal data converted from the radio frequency signal according to the identification result; Input data construction module: the input data construction module is used to extract important features from the electrical signal data and construct second input data containing the important features; Label recognition module: The label recognition module is used to process the second input data using a second deep learning model to obtain a second recognition result.

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