Dairy product quality detection method, device, equipment and medium

Abstract

The invention provides a dairy product quality detection method and device, equipment and a medium, and the method comprises the steps: receiving a second radio frequency signal returned by each radio frequency tag when a radio frequency antenna is controlled to emit a first radio frequency signal; and determining a characteristic value corresponding to each second radio frequency signal according to the first signal intensity and the first signal phase, taking the characteristic value smaller than a preset threshold value in the plurality of characteristic values as a first dairy product evaluation parameter, inputting the first dairy product evaluation parameter into a machine learning model, and outputting the quality of the dairy product. According to the scheme, the corresponding dairy product evaluation parameters when the radio frequency signal penetrates through the to-be-tested dairy product can be screened out, and the condition that the radio frequency signal may not penetrate through the to-be-tested dairy product due to the factor that the size of the to-be-tested dairy product is small and the radio frequency signal is refracted after penetrating through the to-be-tested dairy product is considered; according to the scheme, the accurate dairy product evaluation parameters corresponding to the to-be-detected dairy product can be obtained, so that the accurate detection of the quality of the dairy product is improved, and the quality detection can be quickly completed in real time without contacting the to-be-detected dairy product.

Description

Technical Field

[0001] The present application relates to the field of dairy product detection, and particularly relates to a method, device, equipment and medium for detecting the quality of dairy products. Background Art

[0002] Currently, the most commonly used dairy product quality detection technologies include chemical detection technology, electronic nose detection technology, infrared spectroscopy detection technology, etc. However, these detection technologies all have disadvantages such as damaging the detection samples, long detection time, and expensive detection instruments to varying degrees, and it is difficult to promote them to actual application scenarios. Therefore, providing a suitable method for detecting the quality of dairy products has become a technical problem that urgently needs to be solved at present. Summary of the Invention

[0003] In view of this, the purpose of the present application is to provide a method, device, equipment and medium for detecting the quality of dairy products, improve the accurate detection of the quality of dairy products, and be able to complete the quality detection in real time, quickly, and without contacting the dairy product to be detected. The specific solutions are as follows:

[0004] On the one hand, the present application provides a method for detecting the quality of dairy products. The dairy product to be detected is placed between a radio frequency antenna and a radio frequency tag array. The radio frequency tag array includes a plurality of radio frequency tags, and the plurality of radio frequency tags are arranged at intervals on the same straight line. The method includes:

[0005] When controlling the radio frequency antenna to emit a first radio frequency signal, receiving a second radio frequency signal returned by each radio frequency tag; the second radio frequency signal includes a first signal strength and a first signal phase;

[0006] Determining a characteristic value corresponding to each second radio frequency signal according to the first signal strength and the first signal phase;

[0007] Taking the characteristic values less than a preset threshold among the plurality of characteristic values as a first dairy product evaluation parameter; the first dairy product evaluation parameter includes at least one;

[0008] Inputting the first dairy product evaluation parameter into a machine learning model and outputting the quality of the dairy product.

[0009] Specifically, the first radio frequency tag in the radio frequency tag array, the radio frequency antenna and the dairy product to be detected are on the same straight line.

[0010] Specifically, determining a characteristic value corresponding to each second radio frequency signal according to the first signal strength and the first signal phase includes:

[0011] Obtain a third radio frequency signal, where the third radio frequency signal is the one returned by the radio frequency tag when no dairy product to be measured is placed between the radio frequency antenna and the radio frequency tag array, and the third radio frequency signal includes a second signal strength and a second signal phase;

[0012] Determine a signal strength change amount according to the first signal strength and the second signal strength;

[0013] Determine a signal phase change amount according to the first signal phase and the second signal phase;

[0014] Determine an eigenvalue corresponding to each of the second radio frequency signals according to the signal strength change amount and the signal phase change amount.

[0015] Specifically, each radio frequency tag returns multiple second radio frequency signals. Regarding taking the eigenvalues less than a preset threshold among the multiple eigenvalues as the first dairy product evaluation parameter, it includes:

[0016] Perform variance calculation on the eigenvalues corresponding to the multiple second radio frequency signals returned by each radio frequency tag to obtain a variance value corresponding to each radio frequency tag;

[0017] Determine a target radio frequency tag; the variance value corresponding to the target radio frequency tag is less than a preset variance value;

[0018] Take the eigenvalues less than the preset threshold among the eigenvalues corresponding to the target radio frequency tag as the first dairy product evaluation parameter.

[0019] Specifically, the training process of the machine learning model is as follows:

[0020] Obtain a sample feature database, where the sample feature database includes multiple second dairy product evaluation parameters and the quality corresponding to each of the second dairy product evaluation parameters;

[0021] Train the machine learning model according to the sample feature database until the training is completed.

[0022] Specifically, the volume of the dairy product to be measured is less than a preset volume.

[0023] In another aspect, an embodiment of the present application further provides a dairy product quality detection device. The dairy product to be measured is placed between a radio frequency antenna and a radio frequency tag array. The radio frequency tag array includes multiple radio frequency tags, and the multiple radio frequency tags are arranged at intervals on the same straight line. The device includes:

[0024] A receiving unit, configured to receive a second radio frequency signal returned by each radio frequency tag when controlling the radio frequency antenna to emit a first radio frequency signal; the second radio frequency signal includes a first signal strength and a first signal phase;

[0025] A first determination unit, configured to determine an eigenvalue corresponding to each of the second radio frequency signals according to the first signal strength and the first signal phase;

[0026] A second determination unit, configured to use the eigenvalues less than a preset threshold among the multiple eigenvalues as first dairy product evaluation parameters; the first dairy product evaluation parameters include at least one;

[0027] An output unit, configured to input the first dairy product evaluation parameters into a machine learning model and output the quality of the dairy product.

[0028] Specifically, the first radio frequency tag in the radio frequency tag array, the radio frequency antenna, and the dairy product to be measured are located on the same straight line.

[0029] In another aspect, an embodiment of the present application provides a computer device, which includes a processor and a memory:

[0030] The memory is used to store program codes and transmit the program codes to the processor;

[0031] The processor is configured to execute the method described in the above aspect according to the instructions in the program codes.

[0032] In another aspect, an embodiment of the present application provides a computer-readable storage medium, which is used to store a computer program, and the computer program is used to execute the method described in the above aspect.

[0033] An embodiment of the present application provides a method, device, equipment, and medium for detecting the quality of dairy products. The dairy product to be measured is placed between a radio frequency antenna and a radio frequency tag array. The radio frequency tag array includes multiple radio frequency tags, and the multiple radio frequency tags are arranged at intervals on the same straight line. When controlling the radio frequency antenna to emit a first radio frequency signal, the second radio frequency signals returned by each radio frequency tag are received; the second radio frequency signals include a first signal strength and a first signal phase; when the quality of the dairy product to be measured changes, the dielectric characteristics of the dairy product to be measured will change, and the second radio frequency signals will also be different. Determine the eigenvalue corresponding to each second radio frequency signal according to the first signal strength and the first signal phase, and use the eigenvalues less than the preset threshold among the multiple eigenvalues as the first dairy product evaluation parameters. If the eigenvalue is greater than the preset threshold, it indicates that the second radio frequency signal is likely not to penetrate the dairy product to be measured and does not detect the dairy product to be measured. In this way, the determined first dairy product evaluation parameters are obtained when the second radio frequency signal penetrates the dairy product to be measured. Among them, the first dairy product evaluation parameters include at least one, and then input the first dairy product evaluation parameters into a machine learning model to output the quality of the dairy product.

[0034] This solution can screen out the dairy product evaluation parameters corresponding to the radio frequency signal when it penetrates the dairy product to be tested. Considering the factors that the volume of the dairy product to be tested is relatively small and the radio frequency signal refracts when passing through the dairy product to be tested, which may cause the radio frequency signal not to penetrate the dairy product to be tested. By comparing with the preset threshold, this solution can obtain the accurate dairy product evaluation parameters corresponding to penetrating the dairy product to be tested, thereby improving the accurate detection of the quality of dairy products and enabling real-time, fast, and non-contact quality detection of the dairy product to be tested. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0036] Figure 1 The flowchart of a method for detecting the quality of dairy products provided by an embodiment of the present application is shown;

[0037] Figure 2 The propagation diagram of a radio frequency signal provided by an embodiment of the present application is shown;

[0038] Figure 3 The schematic diagram of a dairy product quality detection provided by an embodiment of the present application is shown;

[0039] Figure 4 The structural block diagram of a dairy product quality detection device provided by an embodiment of the present application;

[0040] Figure 5 The schematic diagram of the sample characteristic values returned by the tag array in the software provided by an embodiment of the present application;

[0041] Figure 6 The layout top view of a quality identification of a dairy product to be tested provided by an embodiment of the present application is shown;

[0042] Figure 7 The structural diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] To make the above objects, features, and advantages of the present application more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present application in conjunction with the drawings.

[0044] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application may be implemented in other ways different from those described herein. Persons skilled in the art may make similar extensions without departing from the spirit of the present application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0045] For ease of understanding, a dairy product quality detection method, device, equipment, and medium provided by an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0046] Reference Figure 1 As shown, it is a schematic flowchart of a dairy product quality detection method provided by an embodiment of the present application. The method may include the following steps.

[0047] S101, when controlling the radio frequency antenna to emit a first radio frequency signal, receive the second radio frequency signal returned by each radio frequency tag.

[0048] Specifically, a radio frequency identification (RFID) device may be used to transmit and receive radio frequency signals. The RFID device may include an RFID reader, an RFID antenna (radio frequency antenna), and an RFID tag (radio frequency tag). Reference Figure 2 As shown, it is a schematic diagram of the propagation of a radio frequency signal provided by an embodiment of the present application. The milk sample to be detected is placed in a container and can be placed on the direct path between the RFID antenna and the RFID tag. Part of the radio frequency signal emitted by the RFID antenna is reflected on the surface of the container, part penetrates the milk in the container to reach the tag, and part reaches the tag after being reflected by objects (walls, ceilings, and people) in the environment. The signal returned by the RFID tag to the RFID antenna is the superposition of these signals.

[0049] In an embodiment of the present application, since the interaction between microorganisms in dairy products is one of the reasons for the generation of different flavor substances and will cause changes in the dielectric properties (such as dielectric constant, conductivity) of dairy products, which in turn will affect the radio frequency signal passing through the dairy products, the amplitude and phase of the radio frequency signal will change, resulting in changes in the amplitude and phase of the superimposed signal. Therefore, the quality of dairy products can be detected by analyzing the changes in the radio frequency signal. This detection method does not require contact with the dairy product sample and is a non-contact detection method, which is very suitable for the detection of dairy product quality.

[0050] In an embodiment of the present application, the dairy product to be tested may be placed between the radio frequency antenna and the radio frequency tag array. Reference Figure 3As shown, so that the radio frequency signal emitted by the radio frequency antenna can pass through the dairy product to be measured and be received by the radio frequency tag. The radio frequency tag array may include a plurality of radio frequency tags. The plurality of radio frequency tags may be arranged at intervals on the same straight line, and the arrangement direction of the plurality of radio frequency tags is perpendicular to the signal propagation direction, and the signal propagation direction is the direction where the radio frequency antenna and the dairy product to be measured are located.

[0051] Specifically, assuming that the electromagnetic wave emitted by the RFID antenna propagates in a straight line, the radio frequency tag located directly behind the dairy product to be measured will receive the signal, and the signal value returned by the radio frequency tag can be analyzed. However, the electromagnetic wave will refract in the dairy product to be measured, which will cause the signal received by the radio frequency tag directly behind the dairy product to be measured not necessarily include the part of the signal that passes through the dairy product to be measured. Therefore, a tag array needs to be set behind the dairy product to be measured, and the tag array may include a plurality of radio frequency tags.

[0052] By setting a plurality of radio frequency tags, it can be ensured that at least one radio frequency tag can receive the radio frequency signal emitted by the radio frequency antenna. Compared with setting only one radio frequency tag, the radio frequency signal reception efficiency can be improved, and then the detection speed of the dairy product to be measured can be improved.

[0053] Specifically, the radio frequency antenna can be controlled to emit a first radio frequency signal. After receiving the first radio frequency signal, the radio frequency tag can return a second radio frequency signal to the radio frequency antenna, that is, the second radio frequency signal returned by each radio frequency tag can be received. The second radio frequency signal may include the first signal strength RSSI target and the first signal phase

[0054] In a possible implementation manner, the first radio frequency tag, the radio frequency antenna, and the dairy product to be measured in the radio frequency tag array are located on the same straight line. Refer to Figure 4 As shown, it is a layout top view for quality identification of the dairy product to be measured provided by an embodiment of the present application, aligning the first radio frequency tag, the dairy product to be measured, and the radio frequency antenna.

[0055] In this way, the radio frequency tag can receive the refracted signal passing through the dairy product to be measured to the greatest extent, that is, ensure that more radio frequency tags can receive the refracted signal passing through the dairy product to be measured, and then make more second radio frequency signals also be the refracted signals passing through the dairy product to be measured, improving the accuracy of detecting the dairy product to be measured.

[0056] In the embodiment of the present application, the volume of the dairy product to be measured may be less than a preset volume, that is to say, the volume of the dairy product to be measured may be relatively small. By making the first radio frequency tag, the radio frequency antenna, and the dairy product to be measured in the radio frequency tag array located on the same straight line, it can be ensured that at least one second radio frequency signal passes through the dairy product to be measured, improving the detection success rate and accuracy of the small-volume dairy product to be measured.

[0057] S102. Determine the eigenvalue corresponding to each second radio frequency signal according to the first signal strength and the first signal phase.

[0058] In the embodiments of the present application, calculations can be performed based on the first signal strength and the first signal phase to calculate the eigenvalue corresponding to the second radio frequency signal, so as to measure the quality of the dairy product to be tested according to the eigenvalue.

[0059] In a possible implementation manner, S102 may specifically be to obtain a third radio frequency signal, where the third radio frequency signal is the one returned by the radio frequency tag when no dairy product to be tested is placed between the radio frequency antenna and the radio frequency tag array. That is to say, the radio frequency signal returned by each radio frequency tag in the air environment. It can be understood that each radio frequency tag has a corresponding third radio frequency signal. For each radio frequency tag, the eigenvalue can be determined according to its respective third radio frequency signal and the second radio frequency signal. The signal strength and signal phase in the third radio frequency signal can be respectively denoted as the second signal strength RSSI air and the second signal phase

[0060] Specifically, A is the original amplitude of the signal, and the signal strength can be expressed as:

[0061] RSSI = 20log e A

[0062] Assume that the frequency of the electromagnetic wave signal emitted by the radio frequency antenna is f. After passing through an object with a penetration length of D and reaching the radio frequency tag, after the original amplitude A of the signal decays, the amplitude A of the obtained second radio frequency signal target can be expressed as:

[0063]

[0064] where D is the width of the dairy product to be tested in the signal propagation direction, and L is the lateral distance between the radio frequency antenna and the tag array.

[0065] Assume that the attenuation of the signal amplitude returned by the radio frequency tag without a container between the antenna and the tag array can be expressed as:

[0066]

[0067] Then it can be obtained that:

[0068]

[0069] That is to say, the signal strength change ΔRSSI can be determined according to the first signal strength RSSI target and the second signal strength RSSI air , and ΔRSSI can be expressed by the following formula:

[0070]

[0071] Wherein, A target is the amplitude of the second radio frequency signal when the dairy product to be measured is placed between the radio frequency antenna and the radio frequency tag, and A air is the amplitude of the third radio frequency signal when the dairy product to be measured is not placed between the radio frequency antenna and the radio frequency tag, and α target is a function of the relative dielectric constant of the dairy product to be measured when the dairy product to be measured is placed between the radio frequency antenna and the radio frequency tag, and α air is a function of the relative dielectric constant of air when the dairy product to be measured is not placed between the radio frequency antenna and the radio frequency tag.

[0072] Wherein, the function of the relative dielectric constant represented by α can be:

[0073]

[0074] Wherein, tanδ is the tangent of the dielectric loss angle.

[0075] Specifically, according to the first signal phase and the second signal phase to determine the signal phase change amount can be expressed as:

[0076]

[0077] Wherein, β target is a function of the relative dielectric constant of the dairy product to be measured when the dairy product to be measured is placed between the radio frequency antenna and the radio frequency tag, and β air is a function of the relative dielectric constant of air when the dairy product to be measured is not placed between the radio frequency antenna and the radio frequency tag.

[0078] Wherein, the function of the relative dielectric constant represented by β can be:

[0079]

[0080] Specifically, according to the signal strength change amount ΔRSSI and the signal phase change amount to determine the eigenvalue corresponding to each second radio frequency signal can be expressed as:

[0081]

[0082] S103, using the eigenvalues less than the preset threshold among the multiple eigenvalues as the first dairy product evaluation parameter.

[0083] In the embodiments of the present application, due to the volume limitation of the dairy product to be measured, the second radio frequency signals returned by the radio frequency tags located at different positions may pass through the dairy product to be measured or may not. For example, when the volume of the dairy product to be measured is relatively small, it is very likely that the second radio frequency signal will not pass through the dairy product to be measured. Then, the second radio frequency signal will not carry the relevant characteristics of the dairy product to be measured and will not be affected by the change in the dielectric constant of the dairy product to be measured. Therefore, it is necessary to screen the eigenvalues to determine the first dairy product evaluation parameter that can be used to evaluate the quality of the dairy product.

[0084] Specifically, the RFID signal contains 16 frequency bands. The phase of the signal passing through the liquid will show different offsets in different frequency bands, while the phase offset of the signal not passing through the liquid is small in different frequency bands. That is to say, when the second radio frequency signal passes through the dairy product to be measured, the phase of the first signal will have a large offset compared with the phase of the second signal. If the second radio frequency signal does not pass through the dairy product to be measured, it means that the offset of the phase of the first signal compared with the phase of the second signal is small, and the radio frequency signals obtained by the second radio frequency signal and the third radio frequency signal both passing through the air.

[0085] Therefore, according to the calculation formula of the eigenvalue, if the second radio frequency signal passes through the dairy product to be measured, the signal phase change amount is relatively large, and the eigenvalue will be relatively small. If the second radio frequency signal does not pass through the dairy product to be measured, the signal phase change amount will be relatively small, and the eigenvalue is relatively large.

[0086] A preset threshold can be set in advance. If the eigenvalue is less than the preset threshold, it means that the second radio frequency signal passes through the dairy product to be measured. That is, the eigenvalues less than the preset threshold among the multiple eigenvalues are used as the first dairy product evaluation parameter. The first dairy product evaluation parameter can include at least one, and multiple first dairy product evaluation parameters can be determined.

[0087] In this way, the second radio frequency signals that do not penetrate the dairy product to be measured are screened out, and it is determined that the first dairy product evaluation parameter can reflect the quality change of the dairy product to be measured, that is, it is affected by the dielectric constant of the dairy product to be measured. Subsequently, the quality of the dairy product to be measured is evaluated based on the first dairy product evaluation parameter, which improves the accuracy of the quality evaluation of the dairy product to be measured.

[0088] In the embodiments of the present application, parallel tests can be performed on the dairy product to be measured multiple times to ensure the accuracy of the data. For each radio frequency tag, multiple second radio frequency signals can be obtained through multiple tests, that is, the second radio frequency signals returned by each radio frequency tag include multiple.

[0089] Specifically, S103 may include the following steps. For each radio frequency tag, the variance of the eigenvalues corresponding to the multiple second radio frequency signals returned by it can be calculated to obtain the variance value corresponding to each radio frequency tag, and the variance value is used to evaluate the stability of the eigenvalues of the radio frequency tag. If the variance value is large, it indicates that the fluctuation of the eigenvalues measured by the radio frequency tag is large and the reliability of the data is low, then the data of this radio frequency tag may not be used for the detection of dairy product quality.

[0090] That is to say, the target radio frequency tag can be determined according to the variance size, and the variance value corresponding to the target radio frequency tag is less than the preset variance value. And the eigenvalues less than the preset threshold in the eigenvalues corresponding to the target radio frequency tag are used as the first dairy product evaluation parameter.

[0091] In this way, by considering the stability of the data of a single radio frequency tag, more accurate and stable eigenvalues are determined as the first dairy product evaluation parameter to achieve the quality evaluation of the dairy product to be measured.

[0092] S104, input the first dairy product evaluation parameter into the machine learning model to output the quality of the dairy product.

[0093] In the embodiment of the present application, the machine learning model can be pre-trained. The first dairy product evaluation parameter is input into the machine learning model, and artificial intelligence is used for quality detection, so as to output the quality of the dairy product to be measured, thereby achieving accurate detection of the dairy product to be measured. Among them, the quality of the dairy product can be good, average, poor, etc.

[0094] Specifically, the training process of the machine learning model may include the following steps. First, a sample feature database can be obtained. The sample feature database includes multiple second dairy product evaluation parameters and the quality corresponding to each second dairy product evaluation parameter. That is to say, for various dairy product qualities, a large number of dairy products can be collected, and the dairy product evaluation parameters of these dairy products can be obtained to construct a sample feature database.

[0095] Then, the model is trained according to the sample feature database through machine learning algorithms (extreme learning machine, support vector machine, etc.) until the training is completed to obtain a trained machine learning model for classifying and identifying the quality of dairy products using this model.

[0096] In actual application, the RFID antenna, the tag array (including 7 radio frequency tags), and the milk sample to be measured can be placed well, and the acquisition data software is used to obtain the eigenvalues. Refer to Figure 5 As shown, it is a schematic diagram of the sample eigenvalues returned by the tag array in a software provided by the embodiment of the present application.

[0097] The milk sample to be tested can be not placed on the detection bracket, and data can be collected for 30 seconds as environmental signal data. Then, the milk sample to be tested is placed on the detection bracket, and data of the milk sample is collected for 30 seconds. The software calculates the milk sample characteristic values returned by 7 tags in the tag array by using the above formula. Multiple parallel tests are repeated, and the final data is shown in the following table.

[0098] Label 1 Label 2 Label 3 Label 4 Label 5 Label 6 Label 7 0.881603 1.859545 -26.8449 3.760467 2.689016 3.976714 2.880898 0.866424 1.850861 -45.0469 3.656448 2.269654 3.859663 2.885536 0.897605 1.896344 -31.4918 3.675893 3.043627 4.004502 2.832155 0.889662 1.909272 -51.1097 3.596213 1.895288 3.899561 3.082501 0.903713 1.909797 -56.7106 3.641481 2.543205 3.874038 2.900148

[0099] As can be seen from the above table, each tag is less than its respective set preset threshold, and the collected signals all pass through the milk sample to be tested. The variances of Tag 3, Tag 5, and Tag 6 are relatively large. Therefore, the characteristic values obtained by these three tags are unstable and are excluded. The remaining tag characteristic values are used as the final characteristic values of the sample. The average value of 5 parallel experiments is taken to obtain the final characteristic values shown in the following table.

[0100] Sample Label 1 Label 2 Label 4 Label 7 Milk 1 0.888907 1.706491 2.981194 2.283215

[0101] Then, the final characteristic values of the milk sample are imported into the detection software to identify the quality of the milk sample, so as to determine whether the quality is good or bad.

[0102] The embodiment of the present application provides a method for detecting the quality of dairy products, which can screen out the dairy product evaluation parameters corresponding to when the radio frequency signal penetrates the dairy product to be tested, and considers the factors that when the volume of the dairy product to be tested is relatively small and the radio frequency signal refracts when passing through the dairy product to be tested, resulting in the situation that the radio frequency signal may not penetrate the dairy product to be tested. Through comparison with the preset threshold, this solution can obtain accurate dairy product evaluation parameters corresponding to penetrating the dairy product to be tested, thereby improving the accurate detection of the quality of dairy products and being able to complete the quality detection in real time, quickly, and without contacting the dairy product to be tested.

[0103] Based on the above method for detecting the quality of dairy products, the embodiment of the present application further provides a device for detecting the quality of dairy products. Refer to Figure 6 As shown, it is a structural block diagram of a device for detecting the quality of dairy products provided by the embodiment of the present application. The device may include:

[0104] A receiving unit 201, configured to receive the second radio frequency signals returned by each of the radio frequency tags when controlling the radio frequency antenna to emit a first radio frequency signal; the second radio frequency signals include a first signal strength and a first signal phase;

[0105] A first determination unit 202, configured to determine the characteristic value corresponding to each of the second radio frequency signals according to the first signal strength and the first signal phase;

[0106] A second determination unit 203, configured to use the characteristic values less than the preset threshold among the multiple characteristic values as the first dairy product evaluation parameters; the first dairy product evaluation parameters include at least one;

[0107] An output unit 204 for inputting the first dairy product evaluation parameter into a machine learning model and outputting the quality of the dairy product.

[0108] Specifically, the first radio frequency tag in the radio frequency tag array, the radio frequency antenna, and the dairy product to be measured are located on the same straight line.

[0109] Specifically, a first determination unit for:

[0110] Obtaining a third radio frequency signal, which is returned by the radio frequency tag when no dairy product to be measured is placed between the radio frequency antenna and the radio frequency tag array, and the third radio frequency signal includes a second signal strength and a second signal phase;

[0111] Determining a signal strength change amount according to the first signal strength and the second signal strength;

[0112] Determining a signal phase change amount according to the first signal phase and the second signal phase;

[0113] Determining an eigenvalue corresponding to each of the second radio frequency signals according to the signal strength change amount and the signal phase change amount.

[0114] Specifically, each radio frequency tag returns a plurality of second radio frequency signals, and a second determination unit for:

[0115] Calculating the variance of the eigenvalues corresponding to the plurality of second radio frequency signals returned by each radio frequency tag to obtain a variance value corresponding to each radio frequency tag;

[0116] Determining a target radio frequency tag; the variance value corresponding to the target radio frequency tag is less than a preset variance value;

[0117] Taking the eigenvalues less than a preset threshold among the eigenvalues corresponding to the target radio frequency tag as the first dairy product evaluation parameter.

[0118] Specifically, the training process of the machine learning model is:

[0119] Obtaining a sample feature database, which includes a plurality of second dairy product evaluation parameters and the quality corresponding to each of the second dairy product evaluation parameters;

[0120] Training the machine learning model according to the sample feature database until the training is completed.

[0121] Specifically, the volume of the dairy product to be measured is less than a preset volume.

[0122] The embodiment of the present application provides a dairy product quality detection device, which can screen out the dairy product evaluation parameters corresponding to the radio frequency signal penetrating the dairy product to be measured. Considering the factors that the volume of the dairy product to be measured is relatively small and the radio frequency signal refracts when passing through the dairy product to be measured, resulting in the situation that the radio frequency signal may not penetrate the dairy product to be measured. Through comparison with the preset threshold, this solution can obtain the accurate dairy product evaluation parameters corresponding to penetrating the dairy product to be measured, thereby improving the accurate detection of the quality of dairy products and being able to complete the quality detection in real time, quickly, and without contacting the dairy product to be measured.

[0123] On the other hand, the embodiment of the present application provides a computer device. Refer to Figure 7 As shown, it is a structural diagram of a computer device provided by the embodiment of the present application. The computer device includes a processor 310 and a memory 320:

[0124] The memory 320 is used to store program codes and transmit the program codes to the processor 310;

[0125] The processor 310 is used to execute the method provided by the above embodiment according to the instructions in the program codes.

[0126] This computer device may include a terminal device or a server, and the foregoing device may be configured in this computer device.

[0127] On the other hand, the embodiment of the present application further provides a storage medium, which is used to store a computer program, and the computer program is used to execute the method provided by the above embodiment.

[0128] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by program instructions and hardware. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium can be at least one of the following media: read-only memory (English: Read-only Memory, abbreviation: ROM), RAM, magnetic disk, or optical disk and other media that can store program codes.

[0129] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment.

[0130] The above are only the preferred embodiments of the present application. Although the present application has been disclosed above with preferred embodiments, it is not intended to limit the present application. Any person skilled in the art can make many possible changes and modifications to the technical solution of the present application, or modify it into equivalent embodiments with equivalent changes, without departing from the scope of the technical solution of the present application. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the content of the technical solution of the present application still fall within the scope of protection of the technical solution of the present application.

Claims

1. A method for detecting the quality of dairy products, characterized in that, The dairy product to be measured is placed between a radio frequency antenna and a radio frequency tag array, and the radio frequency tag array includes a plurality of radio frequency tags, and the plurality of radio frequency tags are arranged at intervals on the same straight line; the method includes: When controlling the radio frequency antenna to emit a first radio frequency signal, receiving a second radio frequency signal returned by each of the radio frequency tags; the second radio frequency signal includes a first signal strength and a first signal phase; Determining a characteristic value corresponding to each of the second radio frequency signals according to the first signal strength and the first signal phase; Taking the characteristic values less than a preset threshold among the plurality of characteristic values as a first dairy product evaluation parameter; the first dairy product evaluation parameter includes at least one; Inputting the first dairy product evaluation parameter into a machine learning model to output the quality of the dairy product.

2. The method according to claim 1, wherein The first radio frequency tag in the radio frequency tag array, the radio frequency antenna, and the dairy product to be measured are located on the same straight line.

3. The method according to claim 1, wherein Determining a characteristic value corresponding to each of the second radio frequency signals according to the first signal strength and the first signal phase includes: Obtaining a third radio frequency signal, where the third radio frequency signal is returned by the radio frequency tag when the dairy product to be measured is not placed between the radio frequency antenna and the radio frequency tag array, and the third radio frequency signal includes a second signal strength and a second signal phase; Determining a signal strength change amount according to the first signal strength and the second signal strength; Determining a signal phase change amount according to the first signal phase and the second signal phase; Determining a characteristic value corresponding to each of the second radio frequency signals according to the signal strength change amount and the signal phase change amount.

4. The method according to claim 1, wherein Each of the radio frequency tags returns a plurality of second radio frequency signals, and taking the characteristic values less than a preset threshold among the plurality of characteristic values as a first dairy product evaluation parameter includes: Calculating the variance of the characteristic values corresponding to the plurality of second radio frequency signals returned by each of the radio frequency tags to obtain a variance value corresponding to each of the radio frequency tags; Determining a target radio frequency tag; the variance value corresponding to the target radio frequency tag is less than a preset variance value; Taking the characteristic values less than a preset threshold among the characteristic values corresponding to the target radio frequency tag as the first dairy product evaluation parameter.

5. The method according to any one of claims 1-4, characterized in that, The training process of the machine learning model is: Obtaining a sample characteristic database, where the sample characteristic database includes a plurality of second dairy product evaluation parameters and the quality corresponding to each of the second dairy product evaluation parameters; Training the machine learning model according to the sample characteristic database until the training is completed.

6. The method according to any one of claims 1 to 4, characterized in that The volume of the dairy product to be measured is less than a preset volume.

7. A dairy product quality detection device, characterized in that, The dairy product to be measured is placed between a radio frequency antenna and a radio frequency tag array, and the radio frequency tag array includes a plurality of radio frequency tags, and the plurality of radio frequency tags are arranged at intervals on the same straight line; the device includes: A receiving unit, configured to receive a second radio frequency signal returned by each of the radio frequency tags when controlling the radio frequency antenna to emit a first radio frequency signal; the second radio frequency signal includes a first signal strength and a first signal phase; A first determination unit, configured to determine a characteristic value corresponding to each of the second radio frequency signals according to the first signal strength and the first signal phase; A second determination unit, configured to use the eigenvalue less than a preset threshold among the multiple eigenvalues as a first dairy product evaluation parameter; the first dairy product evaluation parameter includes at least one; An output unit, configured to input the first dairy product evaluation parameter into a machine learning model and output the quality of the dairy product.

8. The device according to claim 7, characterized in that, The first radio frequency tag in the radio frequency tag array, the radio frequency antenna, and the dairy product to be measured are located on the same straight line.

9. A computer device, characterized in that, The computer device includes a processor and a memory: The memory is configured to store program code and transmit the program code to the processor; The processor is configured to execute the method according to any one of claims 1-6 based on the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is configured to store a computer program, and the computer program is configured to execute the method according to any one of claims 1-6.

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