Sample monitoring method, system and equipment based on RFID technology and medium

Abstract

The invention discloses a sample monitoring method, system and equipment based on an RFID technology and a medium, and relates to the technical field of RFID, sample signal data is preprocessed, the preprocessed RFID signal data can provide accurate and reliable original data for sample monitoring, and support is provided for subsequent path loss prediction and positioning prediction; by establishing a path loss prediction model, the loss of a signal in a transmission process can be accurately predicted, and necessary data support is provided for accurate positioning of a sample; the positioning network model established by using the Bi-LSTM technology can be combined with RFID signal data and path loss data to carry out real-time and accurate positioning prediction on the sample, and the efficiency and accuracy of sample monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the field of RFID technology, and in particular to a sample monitoring method, system, equipment and medium based on RFID technology. Background Art

[0002] In the sample management industry, sample management is currently mainly managed using RFID technology. RFID (Radio Frequency Identification) technology is a key technology for building the "Internet of Things". RFID technology is a wireless communication technology that can identify specific targets through radio signals without direct contact with the object and read and write related data. High-frequency HF radio frequency identification equipment operates in the 13.56MHz frequency band. The system transmits energy through inductive coupling of the antenna coil. The magnetic field energy drops rapidly through inductive coupling.

[0003] However, after investigating the relevant industries, it was found that the number of samples was huge, the categories were numerous, and the storage was not standardized; during the process of collecting and returning samples, there was a phenomenon of multiple people taking or storing samples at the same time, which easily caused chaos in sample management; during the sample return process, the samples were not stored in the specified location, which made it difficult to find the samples.

[0004] In summary, the current application of RFID technology in sample monitoring makes it difficult to identify and monitor the sample collection trajectory. At the same time, the current RFID technology is difficult to distinguish similar or close samples, which leads to the samples not being returned or not being returned to the corresponding location.

[0005] Due to the above technical problems, there is an urgent need for a sample monitoring method that uses RFID technology to accurately locate and track samples, thereby solving the problem of chaotic sample management. Summary of the invention

[0006] Based on the problems raised by the above background technology, the purpose of the present invention is to provide a sample monitoring method, system, equipment and medium based on RFID technology, which solves the problem that it is difficult to identify and monitor the sample taking trajectory when applying RFID technology to sample monitoring. At the same time, the current RFID technology is difficult to distinguish similar and similar samples, which leads to the problem that samples are not returned or are not returned to the corresponding location.

[0007] The present invention is achieved through the following technical solutions:

[0008] The first aspect of the present invention provides a sample monitoring method based on RFID technology, comprising the following steps:

[0009] Step S1, obtaining sample signal data, and preprocessing the sample signal data to obtain RFID signal data;

[0010] Step S2: establishing a path loss prediction model, and using the path loss prediction model to process the RFID signal data to obtain path loss data;

[0011] Step S3: Establish a positioning network model based on Bi-LSTM technology, input the RFID signal data and the path loss data into the positioning network model to perform positioning prediction and obtain sample coordinates.

[0012] In the above technical solution, during the sample monitoring process, it is necessary to accurately obtain the location and status information of the sample. In this method, by preprocessing the sample signal data, the preprocessed RFID signal data can provide accurate and reliable original data for sample monitoring, and provide support for subsequent path loss prediction and positioning prediction.

[0013] During the transmission of RFID signals, the signals will be affected by environmental factors, resulting in path loss. Traditional monitoring methods may not be able to accurately predict path loss, thus affecting the accuracy of positioning. In this method, by establishing a path loss prediction model, the loss of signals during transmission can be accurately predicted, providing the necessary data support for the precise positioning of samples.

[0014] Accurate positioning of samples is a key technical issue in sample monitoring. Traditional positioning methods may have problems such as low positioning accuracy and inability to update location information in real time. Since Bi-LSTM (Bidirectional Long Short Term Memory) technology can process time series data and has good prediction capabilities for dynamically changing location information, the positioning network model established by this method using Bi-LSTM technology can combine RFID signal data and path loss data to perform real-time and accurate positioning prediction of samples, thereby improving the efficiency and accuracy of sample monitoring.

[0015] In an optional embodiment, preprocessing the sample signal data comprises the following steps:

[0016] Step S11, constructing an RFID unified data format, extracting data from the sample signal data according to the RFID unified data format, and establishing a signal path tree based on the extracted data;

[0017] Step S12, constructing a multidimensional signal structure database according to the signal path tree, and performing data mining on the data in the multidimensional signal structure database to obtain sample signal features;

[0018] Step S13: using sliding window technology to sequence the sample signal features to generate RFID signal data.

[0019] In an optional embodiment, data mining is performed on the data in the multidimensional signal structure database, comprising the following steps:

[0020] Step S121, acquiring position data from the data in the multidimensional signal structure database, performing position mining on the position data, and obtaining a sample position sequence;

[0021] Step S122, recursively searching the sample position sequence, and performing a closedness test on the recursive search results to obtain a closed path and a non-closed path;

[0022] Step S123: Acquire a sample processing path, compare the closed path with the sample processing path, and divide the closed path into a frequent closed path and an infrequent closed path according to the comparison result.

[0023] In an optional embodiment, the sample signal features are serialized using a sliding window technique, comprising the following steps:

[0024] Step S131, extracting the time data groups corresponding to the frequent closed paths, the infrequent closed paths and the infrequent closed paths from the signal path tree, and forming corresponding spatiotemporal data sets;

[0025] Step S132, serializing the spatiotemporal dataset in the form of a time axis to obtain a spatiotemporal sequence dataset;

[0026] Step S133: Find the minimum frequently closed period in the spatiotemporal sequence data set, and construct a sliding window and a sliding step, wherein the sliding window is larger than the minimum frequently closed period, and the sliding step is smaller than the minimum frequently closed section.

[0027] In an optional embodiment, processing the RFID signal data using the path loss prediction model includes the following steps:

[0028] Step S21, traversing the RFID signal data to obtain an identification data group and a signal data group of the RFID signal data at time t;

[0029] Step S22: performing sample attribute matching on the identification data group. If the sample attributes match, the path loss value of the signal data group is calculated using the path loss prediction model.

[0030] In an optional embodiment, a positioning network model is established based on the Bi-LSTM technology, including:

[0031] Establishing an input layer, wherein the input layer is used to obtain RFID signal data and path loss data;

[0032] Establishing a preprocessing layer, wherein the preprocessing layer is used to preprocess the RFID signal data and the path loss data to generate a feature sequence;

[0033] Establishing a Bi-LSTM layer, wherein the Bi-LSTM layer is used to extract features from the feature sequence;

[0034] The Bi-LSTM layer includes a forward feature extraction layer and a reverse feature extraction layer. The forward feature extraction layer is used to perform forward feature extraction on the feature sequence in combination with the path loss data to obtain the path loss feature; the reverse feature extraction layer is used to perform reverse feature extraction on the feature sequence to obtain the path feature;

[0035] Establishing a pattern recognition layer, wherein the pattern recognition layer is used to perform pattern recognition on the path features to generate a path pattern;

[0036] Establishing a feature splicing layer, wherein the feature splicing layer is used to splice the path loss feature with the path feature based on the path pattern and using an attention mechanism to obtain a splicing feature;

[0037] A DNN layer is established, and the DNN layer is used to perform positioning prediction according to the splicing features to generate sample coordinates.

[0038] In an optional embodiment, the RFID signal data and the path loss data are input into the positioning network model to perform positioning prediction, comprising the following steps:

[0039] Step S31, preprocessing the RFID signal data to generate a path feature sequence, and performing point-to-point splicing of the path feature sequence and the path loss data to generate a path loss feature sequence;

[0040] Step S32: using the forward feature extraction layer to extract loss features from each sequence point in the path loss feature sequence, and sequentially performing feature recognition on the loss features of each sequence point to obtain path loss features; using the reverse feature extraction layer to extract path features from each sequence point in the path feature sequence, and sequentially performing feature recognition on the path features of each sequence point to obtain path features;

[0041] Step S33, performing frequent closure detection on the path feature, and generating a path pattern according to the frequent closure detection result; wherein the path pattern includes a frequent closure pattern, a non-frequent closure pattern, and a non-closure pattern;

[0042] Step S34: if the path mode is a frequent closed mode, the path loss feature is spliced ​​with the path feature to obtain a standard splicing feature, and the standard splicing feature is compared with the sample processing path to generate a standard path feature; if the path mode is a non-frequent closed mode or a non-closed mode, the path loss feature is spliced ​​with the path feature layer by layer using an attention mechanism to obtain a non-standard splicing feature;

[0043] Step S35, using the DNN layer to perform positioning prediction on the standard splicing features to generate standard sample coordinates; extracting several features from the non-standard splicing features as starting features, and based on the starting features, using the DNN layer to perform positioning prediction on the RFID signal data to generate non-standard sample coordinates.

[0044] The second aspect of the present invention provides a sample monitoring system based on RFID technology, comprising:

[0045] A preprocessing module, used to obtain sample signal data, preprocess the sample signal data, and obtain RFID signal data;

[0046] A loss processing module, used to establish a path loss prediction model, and use the path loss prediction model to process the RFID signal data to obtain path loss data;

[0047] The positioning prediction module is used to establish a positioning network model based on the Bi-LSTM technology, input the RFID signal data and the path loss data into the positioning network model for positioning prediction, and obtain the sample coordinates.

[0048] A third aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a sample monitoring method based on RFID technology when executing the computer program.

[0049] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a sample monitoring method based on RFID technology.

[0050] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0051] The present invention constructs a dual neural network model, and predicts the path loss coefficient under the current environment through the path loss prediction model, thereby avoiding positioning errors caused by environmental changes; through the positioning network model, the occurrence of actual conflicts is reduced while accurately positioning, and at the same time, labels of different protocols can be taken into account. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0053] Figure 1 A schematic diagram of a flow chart of a sample monitoring method based on RFID technology provided in Example 1 of the present invention;

[0054] Figure 2 A schematic diagram of the structure of a sample monitoring system based on RFID technology provided in Example 2 of the present invention;

[0055] Figure 3 A schematic diagram of the structure of an electronic device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0056] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0057] Embodiment 1 of the present invention provides a sample monitoring method based on RFID technology, such as Figure 1 As shown, the sample monitoring method based on RFID technology includes the following steps:

[0058] Step S1, obtaining sample signal data, and preprocessing the sample signal data to obtain RFID signal data;

[0059] Step S2: establishing a path loss prediction model, and using the path loss prediction model to process the RFID signal data to obtain path loss data;

[0060] Step S3: Establish a positioning network model based on Bi-LSTM technology, input the RFID signal data and the path loss data into the positioning network model to perform positioning prediction and obtain sample coordinates.

[0061] It should be noted that during the sample monitoring process, it is necessary to accurately obtain the location and status information of the sample. In this method, by preprocessing the sample signal data, the preprocessed RFID signal data can provide accurate and reliable original data for sample monitoring, and provide support for subsequent path loss prediction and positioning prediction.

[0062] During the transmission of RFID signals, the signals will be affected by environmental factors, resulting in path loss. Traditional monitoring methods may not be able to accurately predict path loss, thus affecting the accuracy of positioning. In this method, by establishing a path loss prediction model, the loss of signals during transmission can be accurately predicted, providing the necessary data support for the precise positioning of samples.

[0063] Accurate positioning of samples is a key technical issue in sample monitoring. Traditional positioning methods may have problems such as low positioning accuracy and inability to update location information in real time. Since Bi-LSTM technology can process time series data and has good prediction capabilities for dynamically changing location information, the positioning network model established by this method using Bi-LSTM technology can combine RFID signal data and path loss data to perform real-time and accurate positioning prediction of samples, thereby improving the efficiency and accuracy of sample monitoring.

[0064] In an optional embodiment, preprocessing the sample signal data comprises the following steps:

[0065] Step S11, constructing an RFID unified data format, extracting data from the sample signal data according to the RFID unified data format, and establishing a signal path tree based on the extracted data;

[0066] Step S12, constructing a multidimensional signal structure database according to the signal path tree, and performing data mining on the data in the multidimensional signal structure database to obtain sample signal features;

[0067] Step S13: using sliding window technology to sequence the sample signal features to generate RFID signal data.

[0068] In this embodiment, the RFID unified data format is constructed as: identification data group, location data group, time data group, signal data group; wherein the signal data group includes signal strength, which is processed along with the time data group in the subsequent processing of step S1, and one time data group corresponds to one signal strength. The RFID unified data format is constructed because existing high-frequency card reading devices such as ISO14443 protocol and ISO15693 protocol can only set a single protocol to read the label of the corresponding protocol, and cannot simultaneously read labels of two different protocols. In the embodiment of the present invention, in order to achieve compatibility between different RFID protocols, it is proposed to convert the data from different RFID tags into a unified format at the data level to facilitate subsequent data processing.

[0069] Specifically, extracting data from the sample signal data in accordance with the RFID unified data format includes: when a sample is selected, a unique identifier is assigned to it, that is, the same sample has a unique identifier, which is globally unique and is used to subsequently identify whether it is the same sample to avoid misidentification during the sample signal data processing process. At the same time, the identification data group also includes data such as sample attributes, such as sample shape, sample length, sample name, etc.

[0070] After assigning the sample identifier, the sample signal data with the same identifier are aggregated, and the time-related data are extracted from the sample signal data and placed in the time data group, such as: sample scanning time, sample retention time, sample destruction time, etc.; and the position-related data are extracted from the sample signal data and placed in the position data group, such as: sample coordinates.

[0071] The aggregated sample signal data is sorted according to the time sequence in the time data group, and then a signal path tree is constructed based on the position data in the position data group corresponding to the time data group. A signal path tree represents the same sample, the structure of the tree represents the time information of the sample, and the nodes of the tree store the position data of the sample, thereby completing the construction of the signal path tree. The signal path tree can provide the movement trend information of the sample, provide correct data for the subsequent mining of sample signal features, and improve the efficiency of data traversal.

[0072] Among them, the purpose of constructing a multidimensional signal structure database based on the signal path tree is that since the sample signal data has multidimensional attributes, sample attributes, time attributes and space attributes, in order to correctly mine the characteristic information of the data, it is necessary to construct a correct and reasonable multidimensional signal structure database.

[0073] In this embodiment, a multidimensional signal structure database is constructed using sample attributes, and the multidimensional signal structure database of sample A is constructed as follows {(sample A)(sample parameter a)(storage warehouse a): 4; (sample A)(sample parameter a)(storage warehouse b): 1; ...; (sample A)(sample parameter d)(storage warehouse d): 2}. Among them, the sample parameters include sample size, material, etc. The storage warehouse is used to indicate the storage location of the sample type, and the following number indicates the number of samples of this category in the identification process. Establishing a multidimensional signal structure database based on sample attributes is beneficial to laboratory sample management, etc. For example, in an experiment, there are several groups of control samples, and the composition and content of each group of samples are slightly different. If the data of this type of attribute is not processed in the RFID data, it will cause sample mixing, which will lead to experimental errors.

[0074] In an optional embodiment, data mining is performed on the data in the multidimensional signal structure database, comprising the following steps:

[0075] Step S121, acquiring position data from the data in the multidimensional signal structure database, performing position mining on the position data, and obtaining a sample position sequence;

[0076] Step S122, recursively searching the sample position sequence, and performing a closedness test on the recursive search results to obtain a closed path and a non-closed path;

[0077] Step S123: Acquire a sample processing path, compare the closed path with the sample processing path, and divide the closed path into a frequent closed path and an infrequent closed path according to the comparison result.

[0078] It should be noted that in this method, the sample signal feature includes the path feature of the sample, which specifically includes frequent closed paths, infrequent closed paths, and non-closed paths. The purpose of extracting this feature in this method is to study the movement process of the sample and the position reached after movement, so as to better manage the storage and use of the sample.

[0079] In an optional embodiment, the sample signal features are serialized using a sliding window technique, comprising the following steps:

[0080] Step S131, extracting the time data groups corresponding to the frequent closed paths, the infrequent closed paths and the infrequent closed paths from the signal path tree, and forming corresponding spatiotemporal data sets;

[0081] Step S132, serializing the spatiotemporal dataset in the form of a time axis to obtain a spatiotemporal sequence dataset;

[0082] Step S133: Find the minimum frequently closed period in the spatiotemporal sequence data set, and construct a sliding window and a sliding step, wherein the sliding window is larger than the minimum frequently closed period, and the sliding step is smaller than the minimum frequently closed section.

[0083] In the embodiment of the present invention, the RFID signal data is a time and space sequence multidimensional data including a sliding window. The minimum frequently closed section is used as the division base point, the sliding window is set to be larger than the minimum frequently closed period, and the sliding step is set to be smaller than the minimum frequently closed section in order to detect frequently closed paths and avoid missed detection.

[0084] Among them, the spatiotemporal data set is serialized and displayed in the form of a time axis, which includes not only the time point of sample movement, but also the time period when the sample stays at a certain place during a certain period of time. Because in the storage and use of samples, it not only includes the movement time, but also the sample retention time. Taking this retention time into account will help the positioning network model to perform accurate positioning detection while ensuring that the window can cover the complete sequence.

[0085] In an optional embodiment, processing the RFID signal data using the path loss prediction model includes the following steps:

[0086] Step S21, traversing the RFID signal data to obtain an identification data group and a signal data group of the RFID signal data at time t;

[0087] Step S22: performing sample attribute matching on the identification data group, and if the sample attributes match consistently, using the path loss prediction model to calculate the path loss value of the signal data group.

[0088] It should be noted that, in order to ensure the identity of the samples, it is necessary to match the sample attributes in the identification data set in this step, namely, the sample shape, sample length, sample name, etc., to prevent sample mixing.

[0089] In this embodiment, the path loss model may use a logarithmic path fading model or other models to calculate the signal strength in the signal data group, thereby obtaining a path loss value.

[0090] Furthermore, after calculating the path loss value at time t, if the spatial data of the RFID signal data at time t+1, that is, the sample in the spatiotemporal sequence data set from time t to time t+1 is in a stagnant state, the path loss value at time t is used as the path loss value at time t+1 until the stagnant state disappears. Through this judgment, the consumption of computing resources is reduced and the computing efficiency is improved.

[0091] In an optional embodiment, a positioning network model is established based on the Bi-LSTM technology, including:

[0092] Establishing an input layer, wherein the input layer is used to obtain RFID signal data and path loss data;

[0093] Establishing a preprocessing layer, wherein the preprocessing layer is used to preprocess the RFID signal data and the path loss data to generate a feature sequence;

[0094] Establishing a Bi-LSTM layer, wherein the Bi-LSTM layer is used to extract features from the feature sequence;

[0095] The Bi-LSTM layer includes a forward feature extraction layer and a reverse feature extraction layer. The forward feature extraction layer is used to perform forward feature extraction on the feature sequence in combination with the path loss data to obtain the path loss feature; the reverse feature extraction layer is used to perform reverse feature extraction on the feature sequence to obtain the path feature;

[0096] Establishing a pattern recognition layer, wherein the pattern recognition layer is used to perform pattern recognition on the path features to generate a path pattern;

[0097] Establishing a feature splicing layer, wherein the feature splicing layer is used to splice the path loss feature with the path feature based on the path pattern and using an attention mechanism to obtain a splicing feature;

[0098] A DNN layer is established, and the DNN layer is used to perform positioning prediction according to the splicing features to generate sample coordinates.

[0099] In an optional embodiment, the RFID signal data and the path loss data are input into the positioning network model to perform positioning prediction, comprising the following steps:

[0100] Step S31, preprocessing the RFID signal data to generate a path feature sequence, and performing point-to-point splicing of the path feature sequence and the path loss data to generate a path loss feature sequence;

[0101] Step S32: using the forward feature extraction layer to extract loss features from each sequence point in the path loss feature sequence, and sequentially performing feature recognition on the loss features of each sequence point to obtain path loss features; using the reverse feature extraction layer to extract path features from each sequence point in the path feature sequence, and sequentially performing feature recognition on the path features of each sequence point to obtain path features;

[0102] Step S33, performing frequent closure detection on the path feature, and generating a path pattern according to the frequent closure detection result; wherein the path pattern includes a frequent closure pattern, a non-frequent closure pattern, and a non-closure pattern;

[0103] Step S34: if the path mode is a frequent closed mode, the path loss feature is spliced ​​with the path feature to obtain a standard splicing feature, and the standard splicing feature is compared with the sample processing path to generate a standard path feature; if the path mode is a non-frequent closed mode or a non-closed mode, the path loss feature is spliced ​​with the path feature layer by layer using an attention mechanism to obtain a non-standard splicing feature;

[0104] Step S35, using the DNN layer to perform positioning prediction on the standard splicing features to generate standard sample coordinates; extracting several features from the non-standard splicing features as starting features, and based on the starting features, using the DNN layer to perform positioning prediction on the RFID signal data to generate non-standard sample coordinates.

[0105] It should be noted that the present invention needs to perform forward and reverse feature extraction on the RFID signal data through the Bi-LSTM layer, wherein the forward extraction is used to extract the characteristics of the RFID signal changes during the forward sequence propagation process, and it is necessary to combine the path loss data for feature extraction; and the purpose of the reverse extraction is to extract the relevant features about the path in the RFID sequence data, and it only needs to rely on the time and location related data in the RFID signal data for feature extraction. It should be noted that the preprocessing before forward extraction and reverse extraction also includes the judgment of sample attributes. If the sample identification and sample attributes are inconsistent, it means that they are not the data of the same sample, and the data is deemed to be abnormal data and discarded.

[0106] Among them, point-to-point splicing is to splice the path loss value at a certain time in the spatiotemporal series data set at the sequence point, so that the corresponding path loss value can be extracted simultaneously when performing spatiotemporal feature extraction in the future. The feature node calculation of the spatiotemporal features and the path loss values ​​can obtain the path loss features.

[0107] Specifically, the process of identifying the loss features of each sequence point in turn is as follows:

[0108]

[0109] In the above formula, is the path loss feature at time t in the forward extraction process, a represents forward extraction, f() is the activation function, w1 and w2 are the time series features X at time t respectively. t and path loss value N t The input weight of , b1 represents the forward extraction bias value.

[0110] In this embodiment, the input weights are randomly generated.

[0111] Specifically, the process of identifying the path features of each sequence point in turn is as follows:

[0112]

[0113] In the above formula, is the path loss feature at time t in the reverse extraction process, c represents reverse extraction, f() is the activation function, w3 and w4 are the time features at time t respectively and spatial characteristics The input weight of , b2 represents the reverse extraction bias value.

[0114] In step S1, the path data is divided into non-closed paths, frequently closed paths and non-frequently closed paths by preprocessing the data. The process of pattern matching of path features here is similar to that in step S12. The frequently closed path belongs to the regular path of the sample, which basically follows the predetermined path of the sample for storage and retrieval. In order to save computing resources, a frequent closedness detection is first performed here, and the data corresponding to the frequent closed pattern is standardized and spliced ​​using a template matching the sample processing path to generate a standard splicing feature. In the subsequent DNN layer recognition process, the past sample processing path is used as a reference for coordinate prediction.

[0115] For non-frequent closed modes or non-closed modes, the driving path is not a standard path, and the attention mechanism is used to splice the path loss features and path features layer by layer to obtain non-standard splicing features. In the subsequent DNN layer recognition process, several features of the non-standard splicing features are extracted as starting features. Based on the starting features, the DNN layer is used to locate and predict the path of the RFID signal data, and the coordinate position that may be moved to in the future can be generated.

[0116] Embodiment 2 of the present invention provides a sample monitoring system based on RFID technology, such as Figure 2 As shown, the sample monitoring system based on RFID technology includes:

[0117] A preprocessing module, used to obtain sample signal data, preprocess the sample signal data, and obtain RFID signal data;

[0118] A loss processing module, used to establish a path loss prediction model, and use the path loss prediction model to process the RFID signal data to obtain path loss data;

[0119] The positioning prediction module is used to establish a positioning network model based on the Bi-LSTM technology, input the RFID signal data and the path loss data into the positioning network model for positioning prediction, and obtain the sample coordinates.

[0120] Embodiment 3 of the present invention provides an electronic device, such as Figure 3 As shown, the electronic device includes a processor 21, a memory 22, an input device 23 and an output device 24; the number of processors 21 in the computer device can be one or more. Figure 3 A processor 21 is taken as an example; the processor 21, the memory 22, the input device 23 and the output device 24 in the electronic device can be connected by a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0121] The memory 22 is a computer-readable storage medium that can be used to store software programs, computer executable programs, and modules. The processor 21 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 22, that is, the sample monitoring method based on RFID technology of Example 1 is implemented.

[0122] The memory 22 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 22 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 22 may further include a memory remotely arranged relative to the processor 21, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0123] The input device 23 can be used to receive the ID and password input by the user, etc. The output device 24 is used to output the network configuration page.

[0124] Embodiment 4 of the present invention further provides a computer-readable storage medium, wherein the computer-executable instructions are used to implement the sample monitoring method based on RFID technology as provided in Embodiment 1 when executed by a computer processor.

[0125] An embodiment of the present invention provides a storage medium containing computer executable instructions, and its computer executable instructions are not limited to the method operations provided in Example 1, and can also execute related operations in the sample monitoring method based on RFID technology provided in any embodiment of the present invention.

[0126] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A sample monitoring method based on RFID technology, characterized in that: The steps include: Step S1, obtaining sample signal data, and preprocessing the sample signal data to obtain RFID signal data; Step S2: establishing a path loss prediction model, and using the path loss prediction model to process the RFID signal data to obtain path loss data; Step S3: Establish a positioning network model based on Bi-LSTM technology, input the RFID signal data and the path loss data into the positioning network model to perform positioning prediction and obtain sample coordinates.

2. The sample monitoring method based on RFID technology according to claim 1, characterized in that: Preprocessing the sample signal data includes the following steps: Step S11, constructing an RFID unified data format, extracting data from the sample signal data according to the RFID unified data format, and establishing a signal path tree based on the extracted data; Step S12, constructing a multidimensional signal structure database according to the signal path tree, and performing data mining on the data in the multidimensional signal structure database to obtain sample signal features; Step S13: using sliding window technology to sequence the sample signal features to generate RFID signal data.

3. The sample monitoring method based on RFID technology according to claim 2, characterized in that: Performing data mining on the data in the multidimensional signal structure database comprises the following steps: Step S121, acquiring position data from the data in the multidimensional signal structure database, performing position mining on the position data, and obtaining a sample position sequence; Step S122, recursively searching the sample position sequence, and performing a closedness test on the recursive search results to obtain a closed path and a non-closed path; Step S123: Acquire a sample processing path, compare the closed path with the sample processing path, and divide the closed path into a frequent closed path and an infrequent closed path according to the comparison result.

4. The sample monitoring method based on RFID technology according to claim 3 is characterized in that: The sample signal features are serialized using a sliding window technique, comprising the following steps: Step S131, extracting the time data groups corresponding to the frequent closed paths, the infrequent closed paths and the infrequent closed paths from the signal path tree, and forming corresponding spatiotemporal data sets; Step S132, serializing the spatiotemporal dataset in the form of a time axis to obtain a spatiotemporal sequence dataset; Step S133: Find the minimum frequently closed period in the spatiotemporal sequence data set, and construct a sliding window and a sliding step, wherein the sliding window is larger than the minimum frequently closed period, and the sliding step is smaller than the minimum frequently closed section. In the embodiment of the present invention, the RFID signal data is a time and space sequence multi-dimensional data including a sliding window.

5. The sample monitoring method based on RFID technology according to claim 1, characterized in that: Processing the RFID signal data using the path loss prediction model includes the following steps: Step S21, traversing the RFID signal data to obtain an identification data group and a signal data group of the RFID signal data at time t; Step S22: performing sample attribute matching on the identification data group, and if the sample attributes match consistently, using the path loss prediction model to calculate the path loss value of the signal data group.

6. The sample monitoring method based on RFID technology according to claim 1, characterized in that: The positioning network model is established based on Bi-LSTM technology, including: Establishing an input layer, wherein the input layer is used to obtain RFID signal data and path loss data; Establishing a preprocessing layer, the preprocessing layer is used to preprocess the RFID signal data and generate a feature sequence; Establishing a Bi-LSTM layer, wherein the Bi-LSTM layer is used to extract features from the feature sequence; The Bi-LSTM layer includes a forward feature extraction layer and a reverse feature extraction layer. The forward feature extraction layer is used to perform forward feature extraction on the feature sequence in combination with the path loss data to obtain the path loss feature; the directional feature extraction layer is used to perform reverse feature extraction on the feature sequence to obtain the path feature; Establishing a pattern recognition layer, wherein the pattern recognition layer is used to perform pattern recognition on the path features to generate a path pattern; Establishing a feature splicing layer, wherein the feature splicing layer is used to splice the path loss feature with the path feature based on the path pattern and using an attention mechanism to obtain a splicing feature; A DNN layer is established, and the DNN layer is used to perform positioning prediction according to the splicing features to generate sample coordinates.

7. The sample monitoring method based on RFID technology according to claim 6, characterized in that: Inputting the RFID signal data and the path loss data into the positioning network model for positioning prediction includes the following steps: Step S31, preprocessing the RFID signal data to generate a path feature sequence, and performing point-to-point splicing of the path feature sequence and the path loss data to generate a path loss feature sequence; Step S32: using the forward feature extraction layer to extract loss features from each sequence point in the path loss feature sequence, and sequentially performing feature recognition on the loss features of each sequence point to obtain path loss features; using the reverse feature extraction layer to extract path features from each sequence point in the path feature sequence, and sequentially performing feature recognition on the path features of each sequence point to obtain path features; Step S33, performing frequent closure detection on the path feature, and generating a path pattern according to the frequent closure detection result; wherein the path pattern includes a frequent closure pattern, a non-frequent closure pattern, and a non-closure pattern; Step S34: if the path mode is a frequent closed mode, the path loss feature is spliced ​​with the path feature to obtain a standard splicing feature, and the standard splicing feature is compared with the sample processing path to generate a standard path feature; if the path mode is a non-frequent closed mode or a non-closed mode, the path loss feature is spliced ​​with the path feature layer by layer using an attention mechanism to obtain a non-standard splicing feature; Step S35, using the DNN layer to perform positioning prediction on the standard splicing features to generate standard sample coordinates; extracting several features from the non-standard splicing features as starting features, and based on the starting features, using the DNN layer to perform positioning prediction on the RFID signal data to generate non-standard sample coordinates.

8. The sample monitoring system based on RFID technology is characterized by: include: A preprocessing module, used to obtain sample signal data, preprocess the sample signal data, and obtain RFID signal data; A loss processing module, used to establish a path loss prediction model, and use the path loss prediction model to process the RFID signal data to obtain path loss data; The positioning prediction module is used to establish a positioning network model based on the Bi-LSTM technology, input the RFID signal data and the path loss data into the positioning network model for positioning prediction, and obtain the sample coordinates.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the sample monitoring method based on RFID technology as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the sample monitoring method based on RFID technology as described in any one of claims 1 to 7 is implemented.

Images