Adaptive identification method and device for test tube sorting

Abstract

The present invention discloses an adaptive identification method and device for test tube sorting, which relates to the field of sorting technology. The method comprises: taking the environmental and spatial attributes of test tube sorting based on RTID radio frequency identification as constraints, and guiding the determination of high-frequency interference factors with radio frequency identification sorting. With the expectation of minimizing radio frequency interference, the scene node set is analyzed in combination with the radio frequency identification device and the test tube attributes to obtain the adaptation parameter threshold. Based on the high-frequency interference factors, a monitoring array is configured to obtain real-time interference factors, and the real-time test tube attributes and interference factors are input into the scene analysis network for matching, and real-time adaptation parameters are obtained to adaptively control the radio frequency identification device. The method solves the technical problem of low radio frequency identification accuracy and low sorting efficiency caused by complex interference factors due to the diversity of environment and space in the existing test tube sorting technology based on RTID radio frequency identification, and achieves the technical effect of improving the accuracy of radio frequency identification and sorting efficiency in test tube sorting.

Description

Technical Field

[0001] The present application relates to the field of sorting technology, and in particular to an adaptive recognition method and device for test tube sorting. Background Art

[0002] In test tube sorting scenarios based on RTID (Radio Frequency Identification), the demand for accurate and efficient test tube sorting is becoming increasingly prominent. However, the conflicting resource requirements caused by the impact of interference factors on sorting accuracy is also becoming more prominent. Finding a method that can effectively address interference factors and achieve accurate sorting has become a crucial step in solving the test tube sorting problem. Traditional test tube sorting methods are often relatively simple and localized, relying only on fixed RFID parameters or simple environmental controls. They lack overall control over the complex environment and spatial properties in which test tube sorting occurs, lack analysis and response to high-frequency interference factors, and are difficult to fully and accurately assess interference conditions in different scenarios. Parameter adjustment and resource allocation are irrational, resulting in low sorting accuracy and efficiency. The formulation of sorting plans is relatively simple and fixed, and cannot effectively cope with the ever-changing test tube properties and interference scenarios.

[0003] Among the current related technologies, there are complex interference factors in the test tube sorting technology based on RTID radio frequency identification due to the diversity of the environment and space, and it is unable to adapt to the changing environment and test tube properties in real time, resulting in low radio frequency identification accuracy, low sorting efficiency, and poor system stability and adaptability. Summary of the Invention

[0004] The present application provides an adaptive identification method and device for test tube sorting, which uses the environmental and spatial attributes of test tube sorting based on RTID radio frequency identification as constraints, and uses radio frequency identification sorting to guide the determination of high-frequency interference factors. Based on this, scene enumeration and segmentation are performed, and with the expectation of minimizing radio frequency interference, the scene node set is analyzed in combination with radio frequency identification equipment and test tube attributes to obtain adaptation parameter thresholds. After establishing the mapping, a scene parsing tree is constructed and fused into a network. Based on the high-frequency interference factors, a monitoring array is configured to obtain real-time interference factors, and the real-time test tube attributes and interference factors are input into the scene parsing network for matching. Real-time adaptation parameters are obtained to perform adaptive regulation of the radio frequency identification device. This achieves the technical effect of improving the accuracy of radio frequency identification and sorting efficiency in test tube sorting by establishing a scene parsing network based on environmental and spatial attributes, and combining real-time monitoring and adaptive regulation.

[0005] The present application provides an adaptive recognition method for test tube sorting, comprising:

[0006] The method uses the environmental and spatial attributes of the test tube sorting as associated constraints and radio frequency identification sorting as a guide to perform positive sample retrieval, and determines high-frequency interference factors based on sample data set analysis. Based on the high-frequency interference factors, scenario enumeration is performed to determine multiple interference scenarios, and the multiple interference scenarios are subdivided according to preset distances to generate multiple scenario node sets. With the goal of minimizing radio frequency interference, the method uses radio frequency identification devices and multiple test tube attributes as constraints to perform radio frequency parameter adaptability analysis on the multiple scenario node sets to obtain multiple adaptation parameter thresholds, wherein the adaptation parameter thresholds correspond to the test tube attributes one-to-one, and each adaptation parameter threshold includes multiple adaptation parameter sets. A first mapping between test tube attributes and adaptation parameter thresholds and a second mapping between scene nodes and adaptation parameters are established. Based on the first and second mappings, multiple scenario parsing trees are constructed according to the multiple test tube attributes, the multiple scene node sets, and the multiple adaptation parameter thresholds, and the trees are fused to generate a scenario parsing network. A monitoring array is configured based on the high-frequency interference factors to obtain real-time interference factors at fixed points. The real-time test tube attributes and the real-time interference factors are input into the scenario parsing network for matching, and real-time adaptation parameters are obtained to perform adaptive control of the radio frequency identification device.

[0007] The present application also provides an adaptive recognition device for test tube sorting, comprising:

[0008] A high-frequency interference factor determination module is used to perform positive sample retrieval based on the environmental attributes and spatial attributes of the test tube sorting as associated constraints and radio frequency identification sorting as a guide, and to determine the high-frequency interference factors based on the sample data set analysis; a scene node set generation module is used to perform scene enumeration based on the high-frequency interference factors to determine multiple interference scenes, subdivide the multiple interference scenes according to preset distances, and generate multiple scene node sets; a radio frequency parameter adaptability analysis module is used to perform radio frequency parameter adaptability analysis on the multiple scene node sets respectively with the expectation of minimizing radio frequency interference and with radio frequency identification equipment and multiple test tube attributes as constraints, to obtain multiple adaptation parameter thresholds, Among them, the adaptation parameter threshold corresponds to the test tube attribute one by one, and each adaptation parameter threshold includes multiple adaptation parameter sets; a scene parsing tree building module, the scene parsing tree building module is used to establish a first mapping of test tube attributes and adaptation parameter thresholds, and a second mapping of scene nodes and adaptation parameters. Based on the first mapping and the second mapping, multiple scene parsing trees are built according to the multiple test tube attributes, multiple scene node sets and multiple adaptation parameter thresholds, and a scene parsing network is generated by fusion; an adaptive control module, the adaptive control module is used to configure a monitoring array based on the high-frequency interference factors, obtain real-time interference factors at a fixed point, input the real-time test tube attributes and the real-time interference factors into the scene parsing network for matching, and obtain real-time adaptation parameters to perform adaptive control on the radio frequency identification device.

[0009] The present application proposes an adaptive identification method and device for test tube sorting. First, the environmental and spatial attributes of test tube sorting based on RTID radio frequency identification are constrained, and high-frequency interference factors are determined by radio frequency identification sorting guidance. Based on this, scene enumeration and segmentation are performed, and with the expectation of minimizing radio frequency interference, the scene node set is analyzed in combination with radio frequency identification equipment and test tube attributes to obtain adaptation parameter thresholds. After establishing the mapping, a scene parsing tree is constructed and fused into a network. Based on the high-frequency interference factors, a monitoring array is configured to obtain real-time interference factors. The real-time test tube attributes and interference factors are input into the scene parsing network for matching, and real-time adaptation parameters are obtained to perform adaptive regulation of the radio frequency identification device. By establishing a scene parsing network based on environmental and spatial attributes, and combining real-time monitoring and adaptive regulation, the technical effect of improving the accuracy of radio frequency identification and sorting efficiency in test tube sorting is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact sequence. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0011] Figure 1 A schematic flow chart of an adaptive recognition method for test tube sorting provided in an embodiment of the present application;

[0012] Figure 2 A schematic structural diagram of an adaptive recognition device for test tube sorting provided in an embodiment of the present application.

[0013] Explanation of the accompanying symbols: high-frequency interference factor determination module 10, scene node set generation module 20, radio frequency parameter adaptability analysis module 30, scene parsing tree construction module 40, adaptive control module 50. DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, device, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0017] The present application embodiment provides an adaptive recognition method for test tube sorting, such as Figure 1 As shown, the method includes:

[0018] Step S100 uses the environmental and spatial attributes of test tube sorting as association constraints and RFID sorting as a guide to retrieve positive samples. High-frequency interference factors are identified based on the sample dataset analysis. Specifically, environmental attributes such as temperature, humidity, and light intensity, as well as spatial attributes such as the size, layout, and obstruction of the sorting area, are determined. Using RFID sorting as a guide, successful RFID sorting cases are retrieved from historical data to form a positive sample set. The sample dataset is then deeply analyzed, with various factors categorized. The performance of each category in the positive samples is observed. Statistical analysis identifies factors that occur frequently and have a significant impact on RFID sorting as high-frequency interference factors.

[0019] In one possible implementation, positive sample retrieval is performed using the environmental and spatial attributes of the test tube sorting process as associated constraints and radio frequency identification sorting as a guide. High-frequency interference factors are determined based on sample dataset analysis. Step S100 further includes step S110, where a sample dataset is retrieved, wherein the sample dataset includes multiple interference factor sets and multiple recognition evaluation sets. Specifically, historical data related to the test tube sorting process is collected through various channels and organized into a sample dataset. The interference factor set includes various factors that may affect radio frequency identification sorting, such as electromagnetic interference, temperature changes, humidity changes, and the presence of metal objects. The recognition evaluation set includes evaluation indicators for the radio frequency identification performance during each sorting process, such as recognition accuracy, read time, bit error rate, and signal strength.

[0020] Step S120: Cluster the multiple interference factor sets to determine multiple sample interference factors and multiple interference frequencies. Specifically, a data clustering algorithm is used to classify the multiple interference factors collected. By analyzing the characteristics of the interference factors, similar interference factors are grouped together to determine multiple sample interference factors. For example, electromagnetic interference of different intensities is grouped together, and temperature changes of different degrees are grouped together. The number of occurrences or proportions of each sample interference factor in the sample data set are counted to determine its interference frequency. For example, the number of occurrences of electromagnetic interference is counted, and its proportion in all samples is calculated as the interference frequency of the electromagnetic interference.

[0021] Step S130: Based on the multiple interference factor sets and the multiple recognition evaluation sets, interference intensity calculations are performed on the multiple sample interference factors to obtain multiple intensity coefficients. Specifically, the interference factor set is used as the independent variable and the recognition evaluation set as the dependent variable. Based on a single analysis principle, a relationship model between the interference factor and the recognition evaluation is established using methods such as linear regression fitting. For each sample interference factor, the degree of its influence on the recognition evaluation is determined through regression analysis to obtain the best fit coefficient, such as the influence coefficient of a certain interference factor on the recognition accuracy rate or the influence coefficient on the reading time. The obtained multiple best fit coefficients are normalized to make them comparable within a certain range, and ultimately multiple intensity coefficients are obtained. The intensity coefficient reflects the interference intensity of each sample interference factor on the RFID sorting.

[0022] In step S140, multiple interference frequencies are calculated based on the multiple interference frequencies and multiple intensity coefficients, and sample interference factors with a frequency greater than a predetermined frequency threshold are selected as high-frequency interference factors. Specifically, the interference frequency of each sample interference factor is calculated based on the interference frequency and intensity coefficient. The interference frequency and intensity coefficient are weighted and summed to obtain the interference frequency. The interference frequency comprehensively considers the frequency of occurrence of the interference factor and its interference intensity on RFID. A predetermined frequency threshold is set, and sample interference factors with an interference frequency greater than the threshold are determined as high-frequency interference factors. High-frequency interference factors are factors that require special attention and response during the test tube sorting process.

[0023] In one possible implementation, based on the multiple interference factor sets and the multiple identification and evaluation sets, the interference intensity of the multiple sample interference factors is calculated to obtain multiple intensity coefficients. Step S130 further includes step S131, taking the interference factor set as the independent variable and the identification and evaluation set as the dependent variable, based on a single analysis principle, performing linear regression fitting according to the multiple interference factor sets and the multiple identification and evaluation sets, determining multiple optimal fitting coefficients, and normalizing the multiple optimal fitting coefficients to obtain the multiple intensity coefficients. Specifically, first of all, it is clear that in the analysis process, the set of various interference factors that may affect the radio frequency identification of test tube sorting is taken as the independent variable. The interference factors include electromagnetic interference intensity, temperature change amplitude, humidity level, etc. Each interference factor is an independent variable, and different values ​​are used to represent its different states. The set of evaluation indicators of radio frequency identification effect is taken as the dependent variable. The recognition evaluation set usually includes recognition accuracy, reading time, bit error rate, signal strength, etc. The indicators reflect the performance and effect of radio frequency identification, and their values ​​will change with the change of interference factors. The single analysis principle means that in the analysis process, only one dependent variable and multiple independent variables are considered at a time. For each recognition evaluation indicator (dependent variable), linear regression analysis is performed with multiple interference factors (independent variables). Using the given multiple interference factor sets and multiple recognition evaluation indicators, the relationship between the dependent variable and the independent variable is analyzed. The data of the price set is used to determine the best fitting coefficient by linear regression fitting. The purpose of linear regression is to find a straight line so that the sum of the distances from all sample points to this straight line is minimized. The best coefficient value is determined by minimizing the objective function. For each dependent variable, the value of the coefficient is continuously adjusted to minimize the value of the objective function. After obtaining multiple best fitting coefficients through linear regression fitting, due to the different numerical ranges of the coefficients, in order to facilitate the comparison and analysis of the influence of various interference factors on the dependent variables, the best fitting coefficients need to be normalized. The normalization process can be achieved by mapping the coefficient value to the [0,1] interval or standardizing it to a distribution with a mean of 0 and a variance of 1. The multiple intensity coefficients obtained can more intuitively reflect the influence of each interference factor on the radio frequency identification effect. The larger the intensity coefficient, the greater the influence of the interference factor on radio frequency identification.

[0024] In step S132, the minimization objective function used in linear regression fitting is: ; where m is the number of samples, is the dependent variable of the i-th sample, is the intercept, is the coefficient of the independent variable, n is the number of independent variables, The jth independent variable representing the i-th sample, j is less than or equal to n. Specifically, the objective function represents the dependent variable of the i-th sample, Indicates the value of the dependent variable predicted based on the current coefficient value, by continuously optimizing the coefficient , so that the difference between the predicted value and the actual value is minimized.

[0025] Step S200: Based on the high-frequency interference factors, scene enumeration is performed to determine multiple interference scenes, and the multiple interference scenes are subdivided according to a preset distance to generate multiple scene node sets. Specifically, the high-frequency interference factors determined by analyzing the sample data set are clearly identified, and then based on these high-frequency interference factors, various actual situations are enumerated to determine multiple interference scenes. A preset distance is determined, whether it is a spatial distance or a numerical range, and the multiple enumerated interference scenes are subdivided according to this preset distance. The sorting area is divided according to spatial intervals or the interference factor values ​​are divided according to a certain interval, thereby generating multiple scene node sets, each scene node representing a specific subdivided interference scene, including the interference factor status and related information under the scene.

[0026] Step S300, with the goal of minimizing RF interference and the RFID device and multiple test tube attributes as constraints, performs RF parameter adaptability analysis on each of the multiple scene node sets to obtain multiple adaptation parameter thresholds, wherein the adaptation parameter thresholds correspond one-to-one to the test tube attributes, and each adaptation parameter threshold includes multiple adaptation parameter sets. Specifically, with the goal of minimizing RF interference and the RFID device characteristics and multiple test tube attributes as constraints, RF parameter adaptability analysis is performed on each of the multiple scene node sets. By comprehensively considering the interference factors, device characteristics, and test tube attributes in the scene, different RF parameter combinations are tried to determine the most suitable parameters to reduce RF interference and ensure recognition accuracy and stability. Finally, multiple adaptation parameter thresholds are obtained, which correspond one-to-one to the test tube attributes. Each adaptation parameter threshold includes multiple adaptation parameter sets, so that in actual applications, appropriate parameter sets can be selected according to specific test tube attributes and interference scenarios to adjust the parameters of the RFID device, thereby achieving adaptive RF identification and sorting.

[0027] In one possible implementation, with the expectation of minimizing radio frequency interference and with the radio frequency identification device and multiple test tube attributes as constraints, radio frequency parameter adaptability analysis is performed on the multiple scene node sets respectively to obtain multiple adaptation parameter thresholds, wherein the adaptation parameter thresholds correspond to the test tube attributes one by one, and each adaptation parameter threshold includes multiple adaptation parameter sets. Step S300 further includes step S310, randomly selecting a first test tube attribute from the multiple test tube attributes, and constructing an radio frequency identification simulation space based on the first test tube attribute and radio frequency identification device simulation. Specifically, one test tube attribute is randomly selected from multiple test tube attributes as the first test tube attribute. The test tube attributes include the material, size, label type, etc. of the test tube. Since different test tube attributes will have different effects on the reflection, absorption and scattering of the radio frequency signal, it is necessary to analyze different test tube attributes. Based on the selected first test tube attribute and the radio frequency identification device, simulation is performed to construct an radio frequency identification simulation space. In this simulation space, the propagation, reflection and interference of the radio frequency signal under different test tube attributes and environmental conditions can be simulated. Through simulation, the impact of radio frequency parameters on radio frequency identification can be more accurately analyzed, providing a basis for subsequent parameter adaptability analysis.

[0028] Step S320: Based on the RFID simulation space, with the expectation of minimizing RF interference, perform RF parameter adaptability analysis on the multiple scene node sets respectively, generate a first adaptation parameter threshold, and add it to the multiple adaptation parameter thresholds, wherein the RF parameters include RF frequency, antenna gain, transmission power and transmission rate. Specifically, using the constructed RFID simulation space, with the desired goal of minimizing RF interference, RF parameter adaptability analysis is performed on multiple scene node sets. The scene node sets represent different interference scenarios and subdivided areas. Each scene node set may have different interference factors and environmental conditions. During the analysis process, RF parameters including RF frequency, antenna gain, transmit power, and transmission rate are considered. By adjusting these parameters, changes in RF interference and RFID accuracy are observed. For example, different RF frequencies are tried to see which frequency has the lowest interference and the highest recognition accuracy. Antenna gain is adjusted to observe changes in signal strength and coverage. By analyzing multiple scene node sets, a first adaptation parameter threshold is generated. The adaptation parameter threshold is the optimal RF parameter range for the first test tube attribute in different scenarios. For example, for a specific test tube material and interference scenario, a suitable RF frequency range, antenna gain range, transmit power range, and transmission rate range are determined. The generated first adaptation parameter threshold is added to the multiple adaptation parameter thresholds. As different test tube attributes are analyzed, multiple adaptation parameter thresholds are gradually accumulated, providing more parameter options for subsequent scene analysis and adaptive control.

[0029] In one possible implementation, based on the RFID simulation space, and with the goal of minimizing RF interference, RF parameter adaptability analysis is performed on each of the multiple scene node sets to generate a first adaptation parameter threshold, which is added to the multiple adaptation parameter thresholds. RF parameters include RF frequency, antenna gain, transmit power, and transmission rate. Step S320 further includes step S321, constructing a recognition effect evaluation function based on recognition evaluation indicators, wherein the recognition evaluation indicators include recognition accuracy, read time, bit error rate, and signal strength. Specifically, recognition evaluation indicators are first determined. These indicators are crucial for measuring the effectiveness of RFID. Recognition accuracy reflects the proportion of test tubes correctly identified; read time represents the time required to read the test tube label information; bit error rate reflects the degree of error during data transmission; and signal strength reflects the strength of the RF signal. A recognition effect evaluation function is constructed based on the recognition evaluation indicators. Each indicator is assigned a different weight through a weighted summation method. The importance of each indicator is determined based on actual needs. These indicators are then combined to quantitatively evaluate the effectiveness of RFID.

[0030] Step S322 randomly selects a first scene node set from the multiple scene node sets, and randomly selects a first scene node, and uses the first scene node to perform scene rendering on the RFID simulation space to generate a first simulation space. Specifically, one of the multiple scene node sets is randomly selected as the first scene node set, and the scene node set represents different interference scenarios and subdivided areas. Each scene node set may have different interference factors and environmental conditions. Another first scene node is randomly selected from the first scene node set, and the scene node specifically describes a specific interference situation and environmental characteristics. The selected first scene node is used to perform scene rendering on the RFID simulation space. By adding the interference factors and environmental characteristics in the scene node to the simulation space, the simulation space is made closer to the actual situation. The generated first simulation space can more accurately simulate the RFID situation in a specific scenario.

[0031] Step S323: Read the RF parameter range of the RFID device. Within the first simulation space, use the RF parameter range as the optimization space, minimize RF interference, and optimize the RF parameters based on the identification effect evaluation function to obtain a first adaptation parameter, which is added to the first adaptation parameter threshold. Specifically, the RF parameter range of the RFID device is read. RF parameters include RF frequency, antenna gain, transmit power, and transmission rate. These parameters typically have a certain range of values. Within the first simulation space, use the RF parameter range as the optimization space, search for the optimal RF parameter combination within this parameter range, minimize RF interference, and optimize the RF parameters based on the identification effect evaluation function, using various optimization algorithms, such as a genetic algorithm. In the genetic algorithm, a set of initial RF parameter combinations is first randomly generated as the population. The fitness of each parameter combination, that is, the quality of the RF identification effect, is calculated according to the recognition effect evaluation function. Through operations such as selection, crossover and mutation, the population is continuously evolved, so that the fitness gradually improves. After a certain number of iterations, the optimal RF parameter combination is obtained, and the obtained first adaptation parameter is added to the first adaptation parameter threshold. The adaptation parameter is a parameter combination that can enable the RFID device to achieve better performance under specific test tube properties and scenarios. By continuously rendering the scene and optimizing the parameters, the first adaptation parameter threshold can be gradually enriched, providing more options for subsequent adaptive control.

[0032] Step S400, establish a first mapping of test tube attributes and adaptation parameter thresholds, and a second mapping of scene nodes and adaptation parameters. Based on the first mapping and the second mapping, build multiple scene parsing trees according to the multiple test tube attributes, multiple scene node sets and multiple adaptation parameter thresholds, and fuse to generate a scene parsing network. Specifically, establish a first mapping of test tube attributes and adaptation parameter thresholds and a second mapping of scene nodes and adaptation parameters. Based on the two mappings, build multiple scene parsing trees with test tube attributes as the starting point, use different test tube attributes as branch nodes, expand branches according to the scene node set, and then connect each scene node with the corresponding adaptation parameter to represent the radio frequency parameter adjustment strategy of a specific test tube attribute under different interference scenarios. Finally, fuse multiple scene parsing trees to generate a scene parsing network, so that it can comprehensively consider various test tube attributes and interference scenarios, and provide a comprehensive adaptive control solution for radio frequency identification equipment. When real-time test tube attributes and interference factors are input, the adaptation parameters can be quickly found to achieve adaptive control, thereby improving the accuracy and efficiency of test tube sorting.

[0033] In one possible implementation, a first mapping of test tube attributes and adaptation parameter thresholds, and a second mapping of scene nodes and adaptation parameters are established. Based on the first mapping and the second mapping, multiple scene parsing trees are built according to the multiple test tube attributes, multiple scene node sets and multiple adaptation parameter thresholds, and the scene parsing network is generated by fusion. Step S400 further includes step S410. Based on the first mapping and the second mapping, with the test tube attribute as the first child node, the multiple scene node sets as the first leaf node of the first child node, and the adaptation parameter threshold as the second leaf node of the first leaf node, multiple scene parsing trees are built according to the multiple test tube attributes, multiple scene node sets and multiple adaptation parameter thresholds, and the multiple scene parsing trees are fused to generate the scene parsing network. Specifically, based on the first mapping and the second mapping, with the test tube attribute as the first child node, multiple scene node sets as the first leaf node, and the adaptation parameter threshold as the second leaf node of the first leaf node, multiple scenario parsing trees are constructed according to the multiple test tube attributes, scene node sets and adaptation parameter thresholds. Each scenario parsing tree represents a radio frequency parameter adjustment strategy for a test tube attribute under different interference scenarios. Multiple scenario parsing trees are integrated to generate a scenario parsing network. The network can comprehensively consider various test tube attributes and interference scenarios, and provide a comprehensive adaptive control solution for radio frequency identification equipment. When real-time test tube attributes and interference factors are input, the adaptation parameters can be quickly found to achieve adaptive control, thereby improving the accuracy and efficiency of test tube sorting.

[0034] Step S500 configures a monitoring array based on the high-frequency interference factors, acquires real-time interference factors at fixed points, inputs real-time test tube attributes and the real-time interference factors into the scene analysis network for matching, and obtains real-time adaptation parameters to adaptively control the radio frequency identification device. Specifically, a monitoring array is configured based on the determined high-frequency interference factors, the array including various sensors to cover the test tube sorting area, acquires real-time interference factors at fixed points, and a data processing system analyzes and processes the data collected by the sensors to determine the current real-time interference factors. During the test tube sorting process, the real-time test tube attributes are determined, and the real-time test tube attributes and the real-time interference factors are input into the constructed scene analysis network. The scene analysis network searches and matches the inputs in the network to determine real-time adaptation parameters suitable for the current scenario. The real-time adaptation parameters are obtained to adaptively control the radio frequency identification device. Dynamic adaptive control is achieved by adjusting device parameters such as radio frequency frequency and antenna gain, thereby improving the efficiency and accuracy of test tube sorting.

[0035] In one possible implementation, a monitoring array is configured based on the high-frequency interference factors, real-time interference factors are acquired at a fixed point, the real-time test tube attributes and the real-time interference factors are input into the scene analysis network for matching, and real-time adaptation parameters are acquired to adaptively control the radio frequency identification device. Step S500 further includes step S510, which acquires a historical interference factor monitoring sequence within a preset window. Specifically, a preset time window is first determined. The window can be a period of time in the past, such as the past few minutes, hours, or days. The monitoring system collects historical interference factor data within the preset window to form a historical interference factor monitoring sequence. Interference factors include electromagnetic interference, temperature changes, humidity changes, and other factors that may affect test tube sorting radio frequency identification. The historical interference factor monitoring sequence records the changes in interference factors over the past period of time, providing a data basis for subsequent analysis and prediction.

[0036] Step S520, a comprehensive fluctuation analysis is performed based on the historical interference factor monitoring sequence. If the fluctuation trend does not meet the expected index, the interference factor of the next node is predicted according to the fluctuation trend to obtain the predicted interference factor. Specifically, a comprehensive fluctuation analysis is performed on the acquired historical interference factor monitoring sequence, which is achieved through statistical analysis, time series analysis and other methods. For example, the mean, variance, trend and other indicators of the interference factor are calculated to understand the change pattern and fluctuation of the interference factor, and the expected index is set. These indicators can be determined according to actual needs and experience. For example, the fluctuation range, change speed and other indicators of the interference factor are set as expected indicators. If the analysis finds that the fluctuation trend of the interference factor does not meet the expected index, it means that the change of the interference factor may exceed the normal range and further prediction is required. According to the fluctuation trend of the interference factor, an appropriate prediction method is used to predict the interference factor of the next node. For example, a time series prediction model, such as an ARIMA model, a neural network model, etc., can be used to predict future interference factors based on historical data. The predicted interference factors are obtained through prediction, which provides a basis for subsequent predictive regulation.

[0037] In step S530, the system matches the predicted interference factors to obtain predicted adaptation parameters, and then predictively adjusts the RFID device at the next node. Specifically, once the predicted interference factors are obtained, the system matches the adaptation parameters based on these prediction results. This is achieved by querying an established adaptation parameter database or using a specific algorithm. The adaptation parameter database stores the adaptation parameters corresponding to different interference factors. The adaptation parameters are determined through the previous solution process and can enable the RFID device to achieve better performance in specific interference scenarios. After finding the corresponding predicted adaptation parameters based on the predicted interference factors, the RFID device at the next node is predictively adjusted. This means that before the interference factors actually occur, the system can adjust the operating parameters of the RFID device in advance to adapt to the interference situation. Predictive adjustment can improve the adaptability of the RFID system in complex and dynamic environments, ensuring the efficiency and accuracy of the test tube sorting process. By adjusting the device parameters in advance, the system can better respond to changes in interference factors, reduce the impact on test tube sorting, and improve the speed and accuracy of sorting.

[0038] The embodiment of the present application adopts the environmental and spatial attributes of the test tube sorting based on RTID radio frequency identification as constraints, and uses radio frequency identification sorting to guide the determination of high-frequency interference factors. Based on this, scene enumeration and segmentation are performed, and with the expectation of minimizing radio frequency interference, the scene node set is analyzed in combination with the radio frequency identification device and the test tube attributes to obtain the adaptation parameter threshold. After the mapping is established, a scene parsing tree is built and fused into a network. Based on the high-frequency interference factors, a monitoring array is configured to obtain real-time interference factors, and the real-time test tube attributes and interference factors are input into the scene parsing network for matching. Real-time adaptation parameters are obtained to perform adaptive regulation on the radio frequency identification device. By establishing a scene parsing network based on environmental and spatial attributes, and combining real-time monitoring and adaptive regulation, the technical effect of improving the accuracy of radio frequency identification and sorting efficiency in test tube sorting is achieved.

[0039] In the above, refer to Figure 1 A method for adaptive recognition of test tube sorting according to an embodiment of the present invention is described in detail. Figure 2 An adaptive recognition device for test tube sorting according to an embodiment of the present invention is described.

[0040] According to an embodiment of the present invention, an adaptive recognition device for test tube sorting is designed to address the technical issues of low RFID accuracy, low sorting efficiency, and poor system stability adaptability in existing RTID-based test tube sorting technology, which suffer from complex interference factors caused by environmental and spatial diversity, and cannot adapt to changing environmental and test tube properties in real time. By establishing a scene parsing network based on environmental and spatial properties and combining real-time monitoring and adaptive control, the device achieves the technical effect of improving RFID accuracy and sorting efficiency in test tube sorting. The device includes: a high-frequency interference factor determination module 10, a scene node set generation module 20, a RF parameter adaptability analysis module 30, a scene parsing tree construction module 40, and an adaptive control module 50.

[0041] The high-frequency interference factor determination module 10 is used to perform positive sample retrieval based on the environmental and spatial attributes of the test tube sorting as associated constraints and radio frequency identification sorting as a guide, and to determine the high-frequency interference factors based on the analysis of the sample data set;

[0042] The scene node set generation module 20 is used to perform scene enumeration based on the high-frequency interference factors to determine multiple interference scenes, subdivide the multiple interference scenes according to preset distances, and generate multiple scene node sets;

[0043] The radio frequency parameter adaptability analysis module 30 is configured to perform radio frequency parameter adaptability analysis on the multiple scene node sets, with the goal of minimizing radio frequency interference and with the radio frequency identification device and multiple test tube attributes as constraints, to obtain multiple adaptation parameter thresholds, wherein the adaptation parameter thresholds correspond to the test tube attributes one by one, and each adaptation parameter threshold includes multiple adaptation parameter sets;

[0044] The scenario parsing tree building module 40 is used to establish a first mapping between test tube attributes and adaptation parameter thresholds, and a second mapping between scene nodes and adaptation parameters. Based on the first and second mappings, multiple scenario parsing trees are built according to the multiple test tube attributes, multiple scene node sets, and multiple adaptation parameter thresholds, and the resulting scene parsing network is fused together.

[0045] The adaptive control module 50 is used to configure the monitoring array based on the high-frequency interference factors, obtain real-time interference factors at a fixed point, input the real-time test tube properties and the real-time interference factors into the scene analysis network for matching, and obtain real-time adaptation parameters to adaptively control the radio frequency identification device.

[0046] The specific configuration of the high-frequency interference factor determination module 10 will be described in detail below. As described above, the environmental attributes and spatial attributes of the test tube sorting are used as association constraints, and the radio frequency identification sorting is used as a guide for positive sample retrieval. The high-frequency interference factors are determined based on the sample data set analysis. The high-frequency interference factor determination module 10 further includes: a sample data set retrieval unit, which is used to retrieve and obtain a sample data set, wherein the sample data set includes multiple interference factor sets and multiple identification evaluation sets; a factor clustering unit, which is used to perform factor clustering on the multiple interference factor sets to determine multiple sample interference factors and multiple interference frequencies; an interference intensity calculation unit, which is used to perform interference intensity calculation on the multiple sample interference factors based on the multiple interference factor sets and the multiple identification evaluation sets to obtain multiple intensity coefficients; a high-frequency interference factor setting unit, which is used to calculate multiple interference frequencies based on the multiple interference frequencies and the multiple intensity coefficients, and select the sample interference factor greater than the predetermined frequency threshold as the high-frequency interference factor.

[0047] Wherein, according to the multiple interference factor sets and the multiple identification evaluation sets, the interference intensity calculation is performed on the multiple sample interference factors to obtain multiple intensity coefficients. The interference intensity calculation unit further includes: a linear regression fitting subunit, the linear regression fitting subunit is used to use the interference factor set as an independent variable and the identification evaluation set as a dependent variable, based on a single analysis principle, to perform linear regression fitting according to the multiple interference factor sets and the multiple identification evaluation sets, determine multiple best fitting coefficients, and normalize the multiple best fitting coefficients to obtain the multiple intensity coefficients; an objective function subunit, the objective function subunit is used in which the minimization objective function used for linear regression fitting is: wherein the minimization objective function used for linear regression fitting is: ; where m is the number of samples, is the dependent variable of the i-th sample, is the intercept, is the coefficient of the independent variable, n is the number of independent variables, The j-th independent variable representing the i-th sample, where j is less than or equal to n.

[0048] The specific configuration of the RF parameter adaptability analysis module 30 will be described in detail below. As described above, with the expectation of minimizing RF interference, with the RFID device and multiple test tube attributes as constraints, RF parameter adaptability analysis is performed on the multiple scene node sets respectively to obtain multiple adaptation parameter thresholds, wherein the adaptation parameter thresholds correspond to the test tube attributes one by one, and each adaptation parameter threshold includes multiple adaptation parameter sets. The RF parameter adaptability analysis module 30 further includes: a simulation space identification unit, the simulation space identification unit is used to randomly select a first test tube attribute from the multiple test tube attributes, and construct an RFID simulation space based on the first test tube attribute and the RFID device simulation; a RF parameter adaptability analysis unit, the RF parameter adaptability analysis unit is used to perform RF parameter adaptability analysis on the multiple scene node sets respectively based on the RFID simulation space and with the expectation of minimizing RF interference, generate a first adaptation parameter threshold, and add it to the multiple adaptation parameter thresholds, wherein the RF parameters include RF frequency, antenna gain, transmit power, and transmission rate.

[0049] Among them, based on the radio frequency identification simulation space, with the expectation of minimizing radio frequency interference, the radio frequency parameter adaptability analysis is performed on the multiple scene node sets respectively, and a first adaptation parameter threshold is generated and added to the multiple adaptation parameter thresholds, wherein the radio frequency parameters include radio frequency frequency, antenna gain, transmission power and transmission rate, and the radio frequency parameter adaptability analysis unit further includes: a recognition effect evaluation function construction subunit, the recognition effect evaluation function construction subunit is used to construct a recognition effect evaluation function based on recognition evaluation indicators, wherein the recognition evaluation indicators include recognition accuracy, reading time, bit error rate and signal strength; a scene rendering subunit, the scene rendering subunit is used to randomly select a first scene node set from the multiple scene node sets, and randomly select a first scene node, use the first scene node to perform scene rendering on the radio frequency identification simulation space to generate a first simulation space; a parameter optimization subunit, the parameter optimization subunit is used to read the radio frequency parameter range of the radio frequency identification device, and in the first simulation space, use the radio frequency parameter range as the optimization space, with the expectation of minimizing radio frequency interference, perform radio frequency parameter optimization based on the recognition effect evaluation function to obtain a first adaptation parameter, and add it to the first adaptation parameter threshold.

[0050] The specific configuration of the scene parsing tree building module 40 will be described in detail below. As described above, a first mapping of the test tube attribute and the adaptation parameter threshold, and a second mapping of the scene node and the adaptation parameter are established. Based on the first mapping and the second mapping, multiple scene parsing trees are built according to the multiple test tube attributes, multiple scene node sets and multiple adaptation parameter thresholds, and the scene parsing network is generated by fusion. The scene parsing tree building module 40 further includes: a scene parsing network fusion unit, which is used to build multiple scene parsing trees based on the first mapping and the second mapping, with the test tube attribute as the first child node, the multiple scene node sets as the first leaf node of the first child node, and the adaptation parameter threshold as the second leaf node of the first leaf node, according to the multiple test tube attributes, multiple scene node sets and multiple adaptation parameter thresholds, and fuse the multiple scene parsing trees to generate the scene parsing network.

[0051] The specific configuration of the adaptive control module 50 will be described in detail below. As described above, a monitoring array is configured based on the high-frequency interference factors, real-time interference factors are acquired at a fixed point, and the real-time test tube attributes and the real-time interference factors are input into the scene analysis network for matching. Real-time adaptation parameters are obtained to adaptively control the RFID device. The adaptive control module 50 further includes: a historical interference factor monitoring sequence acquisition unit, which is used to acquire a historical interference factor monitoring sequence within a preset window; a comprehensive fluctuation analysis unit, which is used to perform comprehensive fluctuation analysis based on the historical interference factor monitoring sequence. If the fluctuation trend does not meet the expected indicators, the interference factor of the next node is predicted based on the fluctuation trend to obtain a predicted interference factor; and a predicted adaptation parameter unit, which is used to obtain a predicted adaptation parameter based on the predicted interference factor matching, and predictively control the RFID device of the next node.

[0052] An adaptive recognition device for test tube sorting provided by an embodiment of the present invention can execute an adaptive recognition method for test tube sorting provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0053] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0054] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An adaptive recognition method for test tube sorting, characterized in that: include: Using the environmental and spatial attributes of the test tube sorting as correlation constraints and radio frequency identification sorting as a guide for positive sample retrieval, high-frequency interference factors are determined based on sample data set analysis; Perform scene enumeration based on the high-frequency interference factors to determine multiple interference scenes, subdivide the multiple interference scenes according to preset distances, and generate multiple scene node sets; With the expectation of minimizing radio frequency interference and with radio frequency identification devices and multiple test tube attributes as constraints, radio frequency parameter adaptability analysis is performed on the multiple scene node sets to obtain multiple adaptation parameter thresholds, wherein the adaptation parameter thresholds correspond to the test tube attributes one by one, and each adaptation parameter threshold includes multiple adaptation parameter sets; Establishing a first mapping between test tube attributes and adaptation parameter thresholds, and a second mapping between scene nodes and adaptation parameters; based on the first and second mappings, constructing multiple scene parsing trees according to the multiple test tube attributes, multiple scene node sets, and multiple adaptation parameter thresholds, and fusing them to generate a scene parsing network; A monitoring array is configured based on the high-frequency interference factors, real-time interference factors are obtained at fixed points, real-time test tube attributes and the real-time interference factors are input into the scene analysis network for matching, and real-time adaptation parameters are obtained to perform adaptive control on the radio frequency identification device.

2. The adaptive recognition method for test tube sorting according to claim 1, characterized in that: Identify high-frequency interference factors, including: Retrieving and obtaining a sample data set, wherein the sample data set includes a plurality of interference factor sets and a plurality of recognition evaluation sets; performing factor clustering on the plurality of interference factor sets to determine a plurality of sample interference factors and a plurality of interference frequencies; Calculating interference intensity of the plurality of sample interference factors according to the plurality of interference factor sets and the plurality of identification evaluation sets to obtain a plurality of intensity coefficients; A plurality of interference frequencies are calculated based on the plurality of interference frequencies and the plurality of intensity coefficients, and sample interference factors greater than a predetermined frequency threshold are selected as the high-frequency interference factors.

3. The adaptive recognition method for test tube sorting according to claim 2, characterized in that: Get multiple intensity coefficients, including: Taking the interference factor set as an independent variable and the identification evaluation set as a dependent variable, based on a single analysis principle, performing linear regression fitting on the multiple interference factor sets and the multiple identification evaluation sets, determining multiple best fitting coefficients, and normalizing the multiple best fitting coefficients to obtain the multiple intensity coefficients; Among them, the minimization objective function used for linear regression fitting is: ； Where m is the number of samples, is the dependent variable of the i-th sample, is the intercept, is the coefficient of the independent variable, n is the number of independent variables, The j-th independent variable representing the i-th sample, where j is less than or equal to n.

4. The adaptive recognition method for test tube sorting according to claim 1, characterized in that: Get multiple adaptation parameter thresholds, including: randomly selecting a first test tube attribute from the plurality of test tube attributes, and constructing an RFID simulation space based on the first test tube attribute and RFID device simulation; Based on the radio frequency identification simulation space, with the expectation of minimizing radio frequency interference, radio frequency parameter adaptability analysis is performed on the multiple scene node sets respectively, and a first adaptation parameter threshold is generated and added to the multiple adaptation parameter thresholds, wherein the radio frequency parameters include radio frequency frequency, antenna gain, transmission power and transmission rate.

5. The adaptive recognition method for test tube sorting according to claim 4, characterized in that: Generating a first adaptation parameter threshold includes: Constructing a recognition effect evaluation function based on recognition evaluation indicators, where the recognition evaluation indicators include recognition accuracy, reading time, bit error rate and signal strength; Randomly selecting a first scene node set from the multiple scene node sets, and randomly selecting a first scene node, and performing scene rendering on the RFID simulation space using the first scene node to generate a first simulation space; Read the radio frequency parameter range of the radio frequency identification device, and in the first simulation space, use the radio frequency parameter range as the optimization space, minimize radio frequency interference, perform radio frequency parameter optimization based on the recognition effect evaluation function, obtain a first adaptation parameter, and add it to the first adaptation parameter threshold.

6. The adaptive recognition method for test tube sorting according to claim 1, characterized in that: Generate a scene parsing network, including: Based on the first mapping and the second mapping, with the test tube attribute as the first child node, multiple scene node sets as the first leaf node of the first child node, and the adaptation parameter threshold as the second leaf node of the first leaf node, multiple scene parsing trees are built according to the multiple test tube attributes, multiple scene node sets and multiple adaptation parameter thresholds, and the multiple scene parsing trees are integrated to generate the scene parsing network.

7. The adaptive recognition method for test tube sorting according to claim 1, characterized in that: Obtain real-time adaptation parameters to adaptively control the RFID device, and then also include: Obtaining historical interference factor monitoring sequences within a preset window; Performing a comprehensive fluctuation analysis based on the historical interference factor monitoring sequence, and if the fluctuation trend does not meet the expected indicators, predicting the interference factor of the next node based on the fluctuation trend to obtain a predicted interference factor; According to the predicted interference factor matching, the predicted adaptation parameter is obtained, and the radio frequency identification device of the next node is predictively regulated.

8. An adaptive recognition device for test tube sorting, characterized in that: The device is used to implement the adaptive recognition method for test tube sorting according to any one of claims 1 to 7, and the device comprises: A high-frequency interference factor determination module is used to determine high-frequency interference factors based on the analysis of the sample data set, using the environmental and spatial attributes of the test tube sorting as association constraints and radio frequency identification sorting as a guide for positive sample retrieval; A scene node set generation module, the scene node set generation module is used to perform scene enumeration based on the high-frequency interference factor to determine multiple interference scenes, subdivide the multiple interference scenes according to preset distances, and generate multiple scene node sets; A radio frequency parameter adaptability analysis module is configured to perform radio frequency parameter adaptability analysis on the multiple scene node sets with the expectation of minimizing radio frequency interference and with radio frequency identification devices and multiple test tube attributes as constraints, thereby obtaining multiple adaptation parameter thresholds, wherein the adaptation parameter thresholds correspond to the test tube attributes one-to-one, and each adaptation parameter threshold includes multiple adaptation parameter sets; A scenario parsing tree building module, the scenario parsing tree building module is used to establish a first mapping between test tube attributes and adaptation parameter thresholds, and a second mapping between scene nodes and adaptation parameters. Based on the first and second mappings, multiple scenario parsing trees are built according to the multiple test tube attributes, multiple scene node sets, and multiple adaptation parameter thresholds, and the resulting scene parsing network is fused together. An adaptive control module is used to configure a monitoring array based on the high-frequency interference factors, obtain real-time interference factors at a fixed point, input real-time test tube attributes and the real-time interference factors into the scene analysis network for matching, and obtain real-time adaptation parameters to adaptively control the radio frequency identification device.

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