Gardenia stir-frying degree detection method, device and equipment based on intelligent sensor

Abstract

The embodiment of the invention discloses a cape jasmine stir-frying degree detection method, device and equipment based on an intelligent sensor. The specific implementation mode of the method comprises the steps that image and smell information collection is conducted on cape jasmine, and an image frame sequence reflecting cape jasmine color changes and a sensor response value set reflecting cape jasmine smell changes are obtained; determining the change trend of the color characteristic value in the image frame sequence to obtain the color characteristic of the gardenia; determining the change trend of the odor characteristic value in the sensor response value set to obtain the odor characteristic of the gardenia; performing principal component analysis on the color features and the smell features to obtain main features; determining main components of the main features to obtain new feature vectors; for the feature vectors, the color feature vectors and the smell feature vectors are combined through a fusion strategy, and new comprehensive feature vectors are obtained; and generating a detection result based on the comprehensive feature vector. According to the embodiment, detection of the stir-frying degree of the gardenia is accelerated, and the requirement of detection of the stir-frying degree of the gardenia on real-time performance can be better met.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, device, equipment and computer-readable medium for detecting the degree of frying of gardenia based on an intelligent sensor. Background Art

[0002] The main methods for monitoring the degree of gardenia frying include computer vision systems and electronic nose technology. Computer vision systems analyze the color changes of gardenia through image processing technology, while electronic nose technology detects the odor changes of gardenia through sensor arrays. Although new technologies and algorithms have emerged in recent years, these methods still belong to the improvement and innovation of these two main directions. While these methods improve detection accuracy, they may face challenges in real-time and complexity. Summary of the invention

[0003] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0004] Some embodiments of the present disclosure propose a method, device, equipment and computer-readable medium for detecting the degree of frying of gardenia based on an intelligent sensor to solve the technical problems mentioned in the above background technology section.

[0005] In a first aspect, some embodiments of the present disclosure provide a method for detecting the degree of frying of gardenia based on an intelligent sensor, the method comprising: collecting image and odor information of gardenia to obtain an image frame sequence reflecting the color change of gardenia and a sensor response value set reflecting the odor change of gardenia; determining the change trend of the color feature value in the image frame sequence to obtain the color feature of gardenia; determining the change trend of the odor feature value in the sensor response value set to obtain the odor feature of gardenia; performing principal component analysis on the color feature and the odor feature to obtain the main feature; determining the principal component of the main feature to obtain a new feature vector; for the feature vector, combining the color feature vector and the odor feature vector through a fusion strategy to obtain a new comprehensive feature vector; and generating a detection result based on the comprehensive feature vector.

[0006] In a second aspect, some embodiments of the present disclosure provide a device for detecting the degree of frying of gardenia based on an intelligent sensor, the device comprising: a detection unit, configured to collect image and odor information of the gardenia, and obtain an image frame sequence reflecting the color change of the gardenia and a sensor response value set reflecting the odor change of the gardenia; a first extraction unit, configured to determine the changing trend of the color feature value in the image frame sequence, and obtain the color feature of the gardenia; a second extraction unit, configured to determine the changing trend of the odor feature value in the sensor response value set, and obtain the odor feature of the gardenia; an analysis unit, configured to perform principal component analysis on the color feature and the odor feature to obtain the main feature; a fusion unit, configured to combine the color feature vector and the odor feature vector through a fusion strategy to obtain a new comprehensive feature vector; and a generation unit, configured to generate a detection result based on the comprehensive feature vector.

[0007] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0008] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner in the first aspect is implemented.

[0009] One of the above-mentioned embodiments of the present disclosure has the following beneficial effects: the method for monitoring the degree of frying of gardenia according to some embodiments of the present disclosure can more accurately monitor the degree of frying of gardenia, and can better meet the requirements of real-time performance and precision in practical applications. Specifically, the inventors found that the reason why the traditional method has a slow detection speed is that the amount of data is large and the processing is complex. Based on this, the method disclosed in the present disclosure adopts multi-sensor fusion technology, combined with computer vision system and electronic nose technology, to simultaneously collect the color and odor information of gardenia. By performing principal component analysis on image and odor features, the main features are extracted, the data dimension is reduced, and the data processing process is simplified. Further, by using data-level and feature-level fusion strategies, the image features and odor features are effectively combined to form a comprehensive feature vector, thereby improving the performance of the classification model. These improvement measures significantly speed up the detection speed and enhance the real-time performance while ensuring the detection effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0011] Figure 1 is a schematic diagram of an application scenario of a method for detecting the degree of frying of gardenia based on an intelligent sensor in some embodiments of the present disclosure;

[0012] Figure 2 is a flow chart of some embodiments of a method for detecting the degree of frying of gardenia based on an intelligent sensor according to the present disclosure;

[0013] Figure 3 is a flow chart of other embodiments of a method for detecting the degree of frying of gardenia based on an intelligent sensor according to the present disclosure;

[0014] Figure 4 is a schematic structural diagram of some embodiments of the roasting degree detection device according to the present disclosure;

[0015] Figure 5 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0016] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0017] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0018] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0019] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0020] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0021] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0022] Figure 1 A schematic diagram showing an application scenario of a method for detecting the degree of frying of gardenia based on an intelligent sensor to which some embodiments of the present disclosure can be applied.

[0023] exist Figure 1 In the application scenario shown, first, the computing device 101 can collect image and odor information of gardenia 102, and obtain an image frame sequence reflecting the color change of gardenia and a sensor response value set reflecting the odor change of gardenia 103. Then, the computing device 101 can determine the change trend of the color feature value in the image frame sequence to obtain the color feature of gardenia; determine the change trend of the odor feature value in the sensor response value set to obtain the odor feature of gardenia 104. After that, the color feature and the odor feature are subjected to principal component analysis to obtain the main feature. In this embodiment, a total of 10 principal components are analyzed. Next, the cumulative variance is calculated for the selected 10 principal components and superimposed to determine the principal component of the main feature to obtain a new feature vector 105. For the feature vector, the color feature vector and the odor feature vector are combined by a fusion strategy to obtain a new comprehensive feature vector 106. In this embodiment, the color vector and the odor vector are pieced together using a data-level fusion strategy. Finally, based on the comprehensive feature vector 106, a detection result 107 is generated. In this embodiment, the detection result of gardenia is that it is in the early stage of frying.

[0024] It should be noted that the computing device 101 can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or electronic devices, or as a single server or a single electronic device. When the computing device is embodied as software, it can be implemented as multiple software or software modules for providing distributed services, or as a single software or software module. No specific limitation is made here.

[0025] It should be understood that Figure 1 The number of computing devices 101 in FIG. 1 is only illustrative. Any number of computing devices 101 may be provided according to implementation requirements.

[0026] Continue to refer Figure 2 , shows a process 200 of some embodiments of the method for detecting the degree of frying of gardenia based on smart sensors according to the present disclosure. The method for detecting the degree of frying of gardenia based on smart sensors comprises the following steps:

[0027] Step 201, collect image and odor information of gardenia to obtain an image frame sequence reflecting the color change of gardenia and a sensor response value set reflecting the odor change of gardenia

[0028] In some embodiments, the above-mentioned execution entity can set a camera to be fixed at a specific position, and regularly take pictures of the gardenia to form an image frame sequence reflecting the color changes of the gardenia.

[0029] In some optional implementations of some embodiments, the above-mentioned execution subject can also use multi-angle or multi-camera settings to obtain comprehensive gardenia image information.

[0030] In some embodiments, the above-mentioned execution entity may pre-process each image frame, including operations such as cropping and scaling, to ensure that the image size is consistent and focused on the gardenia itself, so that subsequent color feature extraction is more accurate.

[0031] In some embodiments, the above-mentioned execution entity can install a gas sensor array nearby to regularly collect volatile organic compound data in the surrounding air to form a set of sensor response values ​​reflecting changes in the gardenia odor.

[0032] In some optional implementations of some embodiments, the above-mentioned execution body can also adjust the sampling frequency of the sensor according to the type and growth cycle of the gardenia to adapt to the differences between different varieties of gardenia.

[0033] Step 202, determining the change trend of the color feature value in the image frame sequence to obtain the color feature of the gardenia; determining the change trend of the odor feature value in the sensor response value set to obtain the odor feature of the gardenia.

[0034] In some embodiments, the execution subject may determine the change trend of the color feature value by calculating the change rate of the average hue, saturation and brightness of each image frame in the image frame sequence.

[0035] In some optional implementations of some embodiments, the above-mentioned execution body may also apply a moving average method or an exponential smoothing method to smooth the color feature values ​​to reduce the impact of noise.

[0036] In some embodiments, the above-mentioned execution entity can determine the change trend of the odor characteristic value by analyzing the odor characteristic value at each time point in the sensor response value set.

[0037] Step 203, perform principal component analysis on the color features and the odor features to obtain the main features; determine the principal components of the main features to obtain a new feature vector.

[0038] In some optional implementations of some embodiments, the above-mentioned execution entity may use a standardization or normalization method to pre-process the original feature values ​​to eliminate the influence of dimensions and scales between different features.

[0039] In some embodiments, the execution entity may determine the principal component of the main feature by selecting the principal component whose cumulative variance contribution rate reaches a certain threshold.

[0040] Step 204, combining the color feature vector and the odor feature vector through a fusion strategy.

[0041] In some embodiments, the execution entity may use a simple weighted average method to combine the color feature vector and the odor feature vector.

[0042] In some optional implementations of some embodiments, the execution subject may use a more complex fusion strategy. For example, multiple base models may be trained to process color feature vectors and odor feature vectors respectively, and then a meta-model may be used to integrate the outputs of the base models to generate a final fused feature vector.

[0043] Step 205: Generate a detection result based on the comprehensive feature vector.

[0044] In some embodiments, the above-mentioned execution entity can use a machine learning model, such as a support vector machine, a random forest, a gradient boosting tree, etc., to generate a stir-fry degree detection result based on a comprehensive feature vector training model.

[0045] In some optional implementations of some embodiments, the above-mentioned execution entity may use a deep learning model, such as a multi-layer perceptron, a convolutional neural network, or a long short-term memory network, to further improve the accuracy of the detection results.

[0046] The methods provided in some embodiments of the present disclosure accelerate the detection of the degree of frying of gardenia, and can better meet the real-time requirements of the detection of the degree of frying of gardenia.

[0047] Further references Figure 3 , which shows the process 300 of other embodiments of the method for detecting the degree of frying of gardenia based on the intelligent sensor. The process 300 of the method for detecting the degree of frying of gardenia based on the intelligent sensor includes the following steps:

[0048] Step 301, using a camera to shoot the fried sample to obtain an image frame sequence; using an electronic nose to collect the smell information of the gardenia during the frying process to obtain a sensor response value set.

[0049] In some embodiments, the execution entity may synchronously record the image frame sequence and the sensor response value set to ensure that the timestamps of the two are consistent, which facilitates subsequent comprehensive analysis.

[0050] In some optional implementations of some embodiments, the above-mentioned execution entity may also use data synchronization technology, such as time stamp correction or data interpolation, to ensure the precise temporal correspondence between image frames and sensor response values.

[0051] Step 302: extract feature values ​​through RGB, HSV and Lab color space models and average them to obtain color features.

[0052] In some embodiments, the execution subject may perform statistical calculations on the feature values ​​in each color space to extract the color feature values ​​of each frame of the image. For example, statistics such as the mean, standard deviation, maximum value, and minimum value of each color channel may be calculated.

[0053] In some embodiments, the execution subject may average the feature values ​​extracted from the three color spaces of RGB, HSV and Lab to obtain the final color feature. Specifically, the feature values ​​in each color space may be weighted averaged with the same weight, or the weights of the feature values ​​in different color spaces may be adjusted according to experimental results to optimize the final color feature.

[0054] Step 303, obtaining the odor characteristics by recording the stable response value of the sensor.

[0055] Step 304, by selecting all features with cumulative variance exceeding 99%, the main features are obtained.

[0056] Step 305 , combining the color feature vector and the odor feature vector through a feature-level fusion strategy to obtain a comprehensive feature vector.

[0057] In some embodiments, the specific implementation of steps 303-305 and the technical effects thereof can be referred to in Figure 2 The steps 202-204 in the corresponding embodiment are not described in detail here.

[0058] Step 306, determining the performance of the comprehensive feature vector acting on different classification models.

[0059] In some optional implementations of some embodiments, the above-mentioned execution entity can divide the data set into a training set and a test set, use the training set to train the model, and then evaluate the performance of the model on the test set. Common performance evaluation indicators include accuracy, precision, recall, F1 score and area under the AUC-ROC curve.

[0060] In some embodiments, the execution subject may use a cross-validation method to further evaluate the stability and generalization ability of the model. Specifically, the data set may be divided into K subsets, and K-1 subsets are used as training sets each time, and the remaining 1 subset is used as a validation set, and this is repeated K times, and finally the average of the K validation results is taken as the performance evaluation result of the model.

[0061] Step 307: Generate a detection result based on the performance of the classification model.

[0062] In some optional implementations of some embodiments, the above-mentioned execution entity may use an integrated learning method, such as a voting method, a stacking method or a boosting method, to combine the prediction results of multiple classification models to generate a final detection result, thereby further improving the accuracy and robustness of the detection result.

[0063] In some optional implementations of some embodiments, the above-mentioned execution entity can visualize the detection results so that the user can intuitively understand the degree of frying of gardenia.

[0064] from Figure 3 It can be seen that Figure 2 Compared with the description of some corresponding embodiments, Figure 3 The process 300 of the method for detecting the degree of frying of gardenia based on intelligent sensors in some corresponding embodiments embodies the steps of generating an image frame sequence and a set of sensor response values, as well as the steps of generating a detection result. Thus, the scheme described in these embodiments can more accurately capture the changes in the frying process of gardenia by synchronously recording the color and smell information of gardenia during the frying process to ensure the temporal consistency of the data. In addition, by evaluating the performance of different classification models based on the effect of comprehensive feature vectors, the optimal model can be selected according to the actual detection requirements, thereby improving the accuracy and robustness of the detection results. For example, in an application scenario, there are high requirements for the real-time and accuracy of the detection results, so a deep learning model with the best performance can be selected, while in another scenario, a lightweight machine learning model can be selected to balance the detection performance and resource consumption when computing resources are limited.

[0065] Further references Figure 4 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a device for detecting the degree of frying of gardenia based on an intelligent sensor. Figure 2 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0066] like Figure 4As shown, the apparatus 400 for detecting the degree of frying of gardenia based on intelligent sensor in some embodiments comprises: a detection unit 401, a first extraction unit 402, a second extraction unit 403, an analysis unit 404, a fusion unit 405 and a generation unit 406. The detection unit 401 is configured to collect image and odor information of gardenia to obtain an image frame sequence reflecting the color change of gardenia and a sensor response value set reflecting the odor change of gardenia; the first extraction unit 402 is configured to determine the change trend of the color feature value in the image frame sequence to obtain the color feature of gardenia; the second extraction unit 403 is configured to determine the change trend of the odor feature value in the sensor response value set to obtain the odor feature of gardenia; the analysis unit 404 is configured to perform principal component analysis on the color feature and the odor feature to obtain the main feature; the fusion unit 405 is configured to combine the color feature vector and the odor feature vector through a fusion strategy for the feature vector to obtain a new comprehensive feature vector; the generation unit 406 is configured to generate a detection result based on the comprehensive feature vector.

[0067] In an optional implementation of some embodiments, the detection unit 401 is further configured to: extract feature values ​​through RGB, HSV and Lab color space models and average them to obtain color features.

[0068] In an optional implementation of some embodiments, the generating unit 406 is further configured to: determine the performance of the comprehensive feature vector acting on different classification models, and generate a detection result based on the performance of the classification model.

[0069] It is understood that the units described in the device 400 are similar to those described in the reference Figure 2 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 400 and the units included therein, and will not be described in detail here.

[0070] Reference below Figure 5 , which shows an electronic device (eg, Figure 1 The electronic devices in some embodiments of the present disclosure may include but are not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0071] like Figure 5As shown, the electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0072] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 5 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 5 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0073] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0074] It should be noted that the computer-readable medium described in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0075] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0076] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: collects image and odor information of gardenia to obtain an image frame sequence reflecting the color change of gardenia and a sensor response value set reflecting the odor change of gardenia; determines the change trend of the color feature value in the image frame sequence to obtain the color feature of gardenia; determines the change trend of the odor feature value in the sensor response value set to obtain the odor feature of gardenia; performs principal component analysis on the color feature and the odor feature to obtain the main feature; determines the principal component of the main feature to obtain a new feature vector; for the feature vector, combines the color feature vector and the odor feature vector through a fusion strategy to obtain a new comprehensive feature vector; generates a detection result based on the comprehensive feature vector.

[0077] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as a separate software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0078] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0079] The units described in some embodiments of the present disclosure may be implemented by software or hardware. The units described may also be provided in a processor, for example, may be described as: a processor includes a detection unit, a first extraction unit, a second extraction unit, an analysis unit, a fusion unit, and a generation unit. The names of these units do not, in some cases, constitute limitations on the units themselves, for example, the detection unit may also be described as a "unit for detecting multi-source information".

[0080] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0081] According to one or more embodiments of the present disclosure, a method for detecting the degree of frying of gardenia based on an intelligent sensor is provided, comprising: collecting image and odor information of gardenia to obtain an image frame sequence reflecting the color change of gardenia and a sensor response value set reflecting the odor change of gardenia; determining the change trend of the color feature value in the image frame sequence to obtain the color feature of gardenia; determining the change trend of the odor feature value in the sensor response value set to obtain the odor feature of gardenia; performing principal component analysis on the color feature and the odor feature to obtain the main feature; determining the principal component of the main feature to obtain a new feature vector; for the feature vector, combining the color feature vector and the odor feature vector through a fusion strategy to obtain a new comprehensive feature vector; and generating a detection result based on the comprehensive feature vector.

[0082] According to one or more embodiments of the present disclosure, when collecting image and odor information of gardenia, an image frame sequence reflecting the color change of gardenia and a sensor response value set reflecting the odor change of gardenia are obtained, including: using a camera to shoot a fried sample to obtain an image frame sequence.

[0083] According to one or more embodiments of the present disclosure, image and odor information of gardenia is collected to obtain an image frame sequence reflecting the color change of gardenia and a sensor response value set reflecting the odor change of gardenia, including: using an electronic nose to collect odor information of gardenia during the frying process to obtain a sensor response value set.

[0084] According to one or more embodiments of the present disclosure, image and odor information of gardenia are collected to obtain an image frame sequence reflecting the color change of gardenia and a sensor response value set reflecting the odor change of gardenia, including: obtaining an image frame sequence by photographing a roasted sample with a camera; and obtaining a sensor response value set by collecting odor information of gardenia during the roasting process with an electronic nose.

[0085] According to one or more embodiments of the present disclosure, generating a detection result based on a comprehensive feature vector includes: determining the performance of the comprehensive feature vector acting on different classification models; and generating a detection result based on the performance of the classification model.

[0086] According to one or more embodiments of the present disclosure, a device for detecting the degree of frying of gardenia based on an intelligent sensor is provided, comprising: a detection unit, configured to collect image and odor information of gardenia, and obtain an image frame sequence reflecting the color change of gardenia and a sensor response value set reflecting the odor change of gardenia; a first extraction unit, configured to determine the change trend of the color feature value in the image frame sequence, and obtain the color feature of gardenia; a second extraction unit, configured to determine the change trend of the odor feature value in the sensor response value set, and obtain the odor feature of gardenia; an analysis unit, configured to perform principal component analysis on the color feature and the odor feature, and obtain the main feature; a fusion unit, configured to combine the color feature vector and the odor feature vector through a fusion strategy for the feature vector, and obtain a new comprehensive feature vector; a generation unit, configured to generate a detection result based on the comprehensive feature vector.

[0087] According to one or more embodiments of the present disclosure, the detection unit is further configured to: extract feature values ​​through RGB, HSV and Lab color space models and average them to obtain color features.

[0088] According to one or more embodiments of the present disclosure, the generating unit is further configured to: determine the performance of the comprehensive feature vector acting on different classification models, and generate a detection result based on the performance of the classification model.

[0089] According to one or more embodiments of the present disclosure, there is provided an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any of the above methods.

[0090] According to one or more embodiments of the present disclosure, a computer-readable medium is provided, on which a computer program is stored, wherein when the program is executed by a processor, any of the above methods is implemented.

[0091] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for detecting the degree of frying of gardenia based on an intelligent sensor, comprising: The image and odor information of the gardenia are collected to obtain an image frame sequence reflecting the color change of the gardenia and a sensor response value set reflecting the odor change of the gardenia; Determine the change trend of the color feature value in the image frame sequence to obtain the color feature of the gardenia; Determine the change trend of the odor feature value in the sensor response value set to obtain the odor feature of gardenia; perform principal component analysis on the color feature and the odor feature to obtain the main feature; determine the principal component of the main feature to obtain a new feature vector; for the feature vector, combine the color feature vector and the odor feature vector through a fusion strategy to obtain a new comprehensive feature vector; generate a detection result based on the comprehensive feature vector.

2. The method according to claim 1, wherein: The method of collecting image and odor information of gardenia to obtain an image frame sequence reflecting the color change of gardenia and a sensor response value set reflecting the odor change of gardenia includes: obtaining the image frame sequence by shooting a fried sample with a camera.

3. The method according to claim 1, wherein: The method of collecting image and odor information of gardenia to obtain an image frame sequence reflecting the color change of gardenia and a sensor response value set reflecting the odor change of gardenia includes: using an electronic nose to collect the odor information of gardenia during the frying process to obtain the sensor response value set.

4. The method according to claim 1, wherein: The determining of the change trend of the color feature values ​​in the image frame sequence and extracting the color features of the gardenia includes: extracting feature values ​​through RGB, HSV and Lab color space models and averaging them to obtain the color features.

5. The method according to claim 1, wherein: The determining of the change trend of the odor characteristic value in the sensor response value set includes: obtaining the odor characteristic by recording the stable response value of the sensor.

6. The method according to claim 1, wherein: The principal component analysis of the collected color features and odor features includes: selecting all features with cumulative variance exceeding 99% to obtain the main features.

7. The method according to claim 1, wherein: Combining the color feature vector and the odor feature vector through a fusion strategy includes: combining the color feature vector and the odor feature vector through a data level fusion or a feature level fusion strategy to obtain the comprehensive feature vector.

8. The method according to claim 1, wherein: The generating of the detection result includes: determining the performance of the comprehensive feature vector acting on different classification models; and generating the detection result based on the performance of the classification model.

9. A device for detecting the degree of frying of gardenia based on an intelligent sensor, comprising: A detection unit is configured to collect image and odor information of the gardenia, and obtain an image frame sequence reflecting the color change of the gardenia and a sensor response value set reflecting the odor change of the gardenia; A first extraction unit is configured to determine a change trend of color feature values ​​in the image frame sequence to obtain a color feature of the gardenia; A second extraction unit is configured to determine a change trend of the odor characteristic value in the sensor response value set to obtain the odor characteristic of gardenia; an analysis unit, configured to perform principal component analysis on the color feature and the odor feature to obtain a main feature; The fusion unit is configured to combine the color feature vector and the odor feature vector through a fusion strategy to obtain a new comprehensive feature vector; the generation unit is configured to generate a detection result based on the comprehensive feature vector.

10. An electronic device comprising: one or more processors; A storage device having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-8.

11. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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