Filter determination method, color sensing device and detection system

Abstract

This disclosure relates to a filter determination method, a color sensing device, and a detection system. The method includes: selecting the minimum number of filters from the detection results corresponding to filter assemblies with different numbers of filters, and using the minimum number as the number of filters in the filter assembly, wherein the filter assembly is used to encode incident light as imaging information; selecting the combination of filter combinations corresponding to different combinations of filters with the same number of filters, and using the combination of filter combinations that reaches a second preset detection result, as the combination method of the filters in the filter assembly; wherein each filter combination includes the minimum number of filters, and the types and / or arrangements of the filters in each filter combination are different. The embodiments of this disclosure can optimize the filter array according to specific applications, reduce costs and integration complexity, and improve measurement accuracy.

Description

Technical Field

[0001] This disclosure relates to the field of detection technology, and in particular to a method for determining a filter, a color sensing device, and a detection system. Background Technology

[0002] Color sensing is a method for determining the composition and content of a analyte based on color changes caused by physical and chemical reactions. It has wide applications in medicine, environmental monitoring, and other fields. However, current color sensing solutions suffer from problems such as large size, low accuracy, complex structure, and high cost. Summary of the Invention

[0003] According to one aspect of this disclosure, a method for determining the filter of a color sensing device is provided, the method comprising:

[0004] Among the detection results corresponding to filter assemblies with different numbers of filters, the minimum number of filters is selected from the detection results that reach the first preset detection result, and the minimum number is used as the number of filters in the filter assembly. The filter assembly is used to encode incident light into imaging information, and the imaging information includes the light intensity value of the incident light. The filter assembly includes multiple different types of filters, and different filters can encode incident light to obtain imaging information.

[0005] Among the detection results corresponding to different combinations of filter elements with the same number of filter elements, the combination of filter elements that achieves the second preset detection result is selected as the combination method of filter elements in the filter element assembly; wherein, each filter element combination includes the minimum number of filter elements, and the types and / or arrangements of filter elements in each filter element combination are different.

[0006] In one possible implementation, the method further includes:

[0007] The imaging information is input into the detection model, and the detection result is obtained by using the output of the detection model, wherein the detection model has a mapping relationship between the imaging information and the detection result.

[0008] In one possible implementation, the method further includes:

[0009] The imaging information is detected to obtain a rectangular grayscale image array.

[0010] In one possible implementation, the detection result includes the components of the analyte or the components of the analyte and the content of each component, and the detection model is based on at least one of least squares method, neural network, support vector machine, Naive Bayes classification, decision tree, k-nearest neighbor algorithm, linear discriminant analysis, linear regression, logistic regression, classification and regression tree, learning vector quantization, bagging method and random forest.

[0011] In one possible implementation, the rectangular grayscale image array includes multiple rectangular regions, each type of filter corresponds to one rectangular region, the multiple rectangular regions correspond to multiple grayscale values, and each rectangular region includes multiple pixels;

[0012] The types of filters include at least one type of metasurface filter, photonic crystal filter, perovskite quantum dot filter, and colloidal quantum dot filter, with each type of filter encompassing multiple different varieties.

[0013] In one possible implementation, the filter combination includes colloidal quantum dot filters, each with a different spectral transmission relationship. The filters encode the incident light based on the spectral transmission relationship and the spectral sensitivity relationship of the corresponding detection component of each filter, thereby obtaining the imaging information of the incident light. The spectral sensitivity relationship represents the relationship between photoresponsivity and light wavelength.

[0014] In one possible implementation, the method includes:

[0015] Provides multiple filter assemblies with different numbers of filters, including: selecting multiple different numbers of filters from N types of filters to form multiple filter assemblies, wherein each of the N types of filters has a different spectral transmission relationship, and the N types of filters can encode colors within a target wavelength range, where N is a positive integer;

[0016] Provide multiple filter assemblies with different filter combination methods, including: determining the minimum number of consecutive or skip distribution combinations from N types of filters multiple times.

[0017] According to one aspect of this disclosure, a color sensing device is provided, the device comprising:

[0018] The filter assembly obtained by the filter determination method of the color sensing device is used to encode incident light into imaging information, the imaging information including the light intensity value of the incident light, and the filter assembly includes multiple different types of filters, each of which can encode incident light to obtain imaging information.

[0019] In one possible implementation, the device further includes:

[0020] A detection component is used to detect the imaging information, which is input into a detection model to obtain a detection result using the output of the detection model, wherein the detection model has a mapping relationship between the imaging information and the detection result.

[0021] In one possible implementation, the detection component includes at least one of a complementary metal-oxide-semiconductor element, a charge-coupled device, an ultraviolet detection element, and an indium gallium arsenide near-infrared detection element.

[0022] According to one aspect of this disclosure, a detection system is provided, the detection system comprising:

[0023] The color sensing device;

[0024] A light source used to emit detection light;

[0025] A color reaction component is configured to obtain one or more of the following after illumination by the probe light: transmitted light, reflected light, or fluorescence, and to incident the light obtained after illumination onto the filter assembly.

[0026] A data processing component is used to obtain detection results based on the detection image or imaging information generated by the color sensing device.

[0027] In one possible implementation, the data processing component is further configured to:

[0028] Acquire a first detection image and a second detection image output by the color sensing device. The first detection image is the detection image output by the color sensing device when no test substance is added to the color reaction component. The second detection image is the detection image output by the color sensing device when the test substance is added to the color reaction component.

[0029] The intensity of the corresponding pixel in the second detection image is subtracted from that in the first detection image to obtain the third detection image;

[0030] The third detection image is input into the detection model, and the detection results of the components of the analyte or the components of the analyte and the content of each component are obtained by using the output results of the detection model. The detection model has a mapping relationship between the detection image or the imaging information and the detection results.

[0031] In one possible implementation, the color reaction component includes a reflective component, a transmissive component, and a fluorescent component, wherein the incident light is any one of the reflected light generated by the reflective component based on the probe light, the transmissive light generated by the probe light penetrating the transmissive component, or the fluorescence generated by the incident light irradiating the fluorescent component.

[0032] In one possible implementation, the filter assembly in the color sensing device is capable of encoding incident light in the 380nm-750nm range.

[0033] According to one aspect of this disclosure, a urine detection system is provided, the urine detection system comprising the color sensor or the detection system described above.

[0034] This embodiment of the disclosure selects the minimum number of filters from the detection results corresponding to filter assemblies with different numbers of filters, and uses the minimum number as the number of filters in the filter assembly. From the detection results corresponding to different combinations of filters with the same number of filters, the combination of filters that reaches the second preset detection result is selected as the combination method of filters in the filter assembly. This can optimize the filter array according to specific applications, reduce cost, size and integration complexity while ensuring the accuracy of the color sensing device, and improve measurement accuracy.

[0035] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0036] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0037] Figure 1 A flowchart is shown of a method for determining the filter of a color sensing device according to an embodiment of the present disclosure.

[0038] Figure 2 A flowchart is shown of a method for determining the filter of a color sensing device according to an embodiment of the present disclosure.

[0039] Figure 3 A schematic diagram of the transmission spectrum of various colloidal quantum dot filters according to embodiments of the present disclosure is shown.

[0040] Figure 4 A schematic diagram is shown illustrating the determination of a detection model and the detection of concentration according to an embodiment of the present disclosure.

[0041] Figure 5 A schematic diagram of a filter determination method for a color sensing device according to an embodiment of the present disclosure is shown.

[0042] Figure 6 A schematic diagram of a color sensing device according to an embodiment of the present disclosure is shown.

[0043] Figure 7 A schematic diagram of a color sensing device according to an embodiment of the present disclosure is shown.

[0044] Figure 8 A schematic diagram is shown of a detection image formed by a detection component according to an embodiment of the present disclosure based on imaging information from a filter component.

[0045] Figure 9 A block diagram of a detection system according to an embodiment of the present disclosure is shown.

[0046] Figure 10 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0047] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0048] In the description of this disclosure, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this disclosure and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure.

[0049] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this disclosure, "a plurality of" means two or more, unless otherwise expressly specified.

[0050] In this disclosure, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this disclosure according to the specific circumstances.

[0051] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0052] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0053] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0054] Color sensing technologies primarily include visual inspection, RGB image processing, and spectral analysis. Spectral analysis relies on expensive and bulky spectrometers, making it unsuitable for portable, low-cost applications such as clinical testing. Visual inspection and RGB image processing have limited color perception capabilities, affecting identification results. Visual inspection relies on subjective human judgment, especially in clinical medicine, where it depends mainly on the doctor's experience and cannot provide quantitative detection.

[0055] It is evident that the color sensing solutions of related technologies cannot simultaneously achieve aspects such as reducing size, improving accuracy, simplifying structure, and reducing cost.

[0056] This disclosure proposes a method for determining the filter element in a color sensing device. The method involves selecting the minimum number of filters from the detection results corresponding to filter assemblies with different numbers of filters, and using this minimum number as the number of filters in the filter assembly. Then, from the detection results corresponding to different combinations of filters with the same number of filters, the method selects the combination of filters that achieves a second preset detection result. This combination is the filter element combination in the filter assembly. This allows for optimization of the filter array based on specific applications, reducing cost, size, and integration complexity while ensuring the accuracy of the color sensing device, and improving measurement accuracy.

[0057] In one possible implementation, the filter determination method for the color sensing device proposed in this disclosure can be executed by a processing component, which includes, but is not limited to, a separate processor, discrete components, or a combination of a processor and discrete components. The processor may include a controller in an electronic device with instruction execution capabilities. The processor can be implemented in any suitable manner, for example, by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components. Within the processor, the executable instructions can be executed by hardware circuits such as logic gates, switches, ASICs, programmable logic controllers, and embedded microcontrollers.

[0058] In one possible implementation, the filter determination method for the color sensing device proposed in this disclosure can be executed by an electronic device, which may include a terminal device, a server, or other processing devices. The terminal device may be a user equipment (UE), mobile device, user terminal, terminal, handheld device, computing device, or in-vehicle device, etc. Examples of terminals include: mobile phones, tablets, laptops, PDAs, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, and wireless terminals in vehicle-to-everything (V2X) networks. For example, the server may be a local server or a cloud server.

[0059] Please see Figure 1 , Figure 1 A flowchart is shown of a method for determining the filter of a color sensing device according to an embodiment of the present disclosure.

[0060] like Figure 1 As shown, the method includes:

[0061] Step S11: Among the detection results corresponding to filter assemblies with different numbers of filters, select the minimum number of filters from the detection results that reach the first preset detection result, and use the minimum number as the number of filters in the filter assembly. The filter assembly is used to encode incident light into imaging information. The imaging information includes the light intensity value of the incident light. The filter assembly includes multiple different types of filters. Different filters can encode incident light to obtain imaging information.

[0062] Step S12: Among the detection results corresponding to different combinations of filter elements with the same number of filter elements, select the combination of filter elements that achieves the second preset detection result as the combination method of the filter elements in the filter element assembly; wherein, each filter element combination includes the minimum number of filter elements, and the types and / or arrangements of the filter elements in each filter element combination are different.

[0063] The embodiments disclosed herein do not limit the specific number, type, and arrangement of the filters in the filter assembly. Those skilled in the art can adapt the above methods to determine the number, type, and arrangement of the filters according to the actual application scenario and needs, thereby reducing the size, cost, and integration complexity of the filter assembly and improving the detection accuracy.

[0064] Generally speaking, the more types of filters in a filter assembly, the better, as a larger number of filters results in more thorough color sampling. However, in practical applications, two points need to be considered: first, the more types of filters there are, the higher the cost of the color sensor and the more complex and difficult the manufacturing process becomes; second, there may be redundancy in the filter sensing channels, which can introduce random errors and affect subsequent qualitative classification and quantitative fitting results. Therefore, by adaptively determining the number and types of filters, the embodiments of this disclosure can reduce the size, cost, and integration complexity of the filter assembly, while improving detection accuracy, thus achieving a balance between the size, cost, integration complexity, and detection accuracy of the filter assembly.

[0065] In one possible implementation, the filter type may include at least one type of filter type made of various materials, such as metasurface filter type, photonic crystal filter type, perovskite quantum dot filter type, and colloidal quantum dot filter type. Each filter type includes multiple different types. For example, the filter assembly of this disclosure embodiment may integrate different materials in the same substrate material, that is, a filter assembly may be made of multiple materials, such as integrating perovskite quantum dots, colloidal quantum dots, etc. on the same substrate; of course, it may also be made of one material (such as the filter assembly being made entirely of colloidal quantum dot filters).

[0066] For example, a filter assembly can be composed of multiple (e.g., several, dozens, or hundreds) types of filters. In this embodiment, colloidal quantum dot filters are preferred because the preparation method of colloidal quantum dots is mature, the preparation process is simple, the cost is low, and it is easy to control. Furthermore, multiple colloidal quantum dots can be integrated on the same substrate using ink printing technology to form a filter array (filter assembly). For example, one type of colloidal quantum dot can produce one type of filter. After selecting the type of colloidal quantum dot, multiple colloidal quantum dots can be printed on the same substrate to form a filter assembly (e.g., a filter array including multiple filters). In addition, colloidal quantum dot filters are not affected by the incident light angle, making them particularly suitable for reflective measurement detection systems, and they have significant advantages compared to other filter arrays.

[0067] For example, embodiments of this disclosure prepare colloidal quantum dot filters with different particle sizes by changing the reaction conditions and component ratios during the synthesis of colloidal quantum dots. Different colloidal quantum dot filters have different spectral transmission functions. Colloidal quantum dot filters are simple to prepare, low in cost, easily integrated via liquid-phase printing (such as ink printing), and easily customized for specific applications. Therefore, the detection system of this disclosure can flexibly customize color sensing schemes for different applications, and the device is small in size and low in cost.

[0068] For example, since each filter on the filter assembly has a different spectral transmission function, it can fully sample and encode the incident light, so that the information of each band of the incident light is converted into light intensity information through the filter assembly. The embodiments of this disclosure can sample the light intensity information (i.e., image information) encoded by the filter assembly to form a detection image (such as a rectangular grayscale image array or other forms of image), and each type of incident light forms a different detection image. The detection image contains the spectral information of the incident light. Compared with traditional large spectrometers, the filter assembly design determined by the filter determination method of the embodiments of this disclosure avoids the complex structure and large volume of the spectral dispersive system, and greatly reduces the volume of the color sensor.

[0069] In one possible implementation, by adaptively determining the number and combination of filters, the present disclosure embodiment can ensure that the number of filters in the filter assembly is the minimum number of detection results that reach a first preset detection result among the detection results corresponding to filter assemblies with different numbers of filters; and that the combination of filters in the filter assembly is a combination that reaches a second preset detection result among the detection results corresponding to different combinations of the same number of filters, wherein the combination includes the type and arrangement of filters, each filter combination includes the minimum number of filters, and the type and / or arrangement of filters in each filter combination are different.

[0070] This disclosure does not limit the specific form or size of the first preset detection result and the second preset detection result. Those skilled in the art can set them according to actual conditions and needs. For example, the first preset detection result and the second preset detection result can be related to the accuracy of color sensing. In this way, this disclosure can take into account the accuracy, size, cost, etc. of color sensing. For example, the first preset detection result and the second preset detection result can be quantities corresponding to the detection results of the filter assembly. For example, if the detection result is the content of a component, the first preset detection result can be a preset content, or a preset mean square error, root mean square error, etc. If the detection result is a classification result, the first preset detection result can be a preset classification accuracy, etc. For example, the second preset detection result can be a preset boundary line for the better detection result. When the detection result reaches the second preset detection result, it can be determined that the corresponding filter component has a better detection result. For example, there may be multiple values ​​that reach the second preset detection result. In this case, in order to reduce the detection threshold, the embodiments of this disclosure can select the filter component corresponding to the smaller value among the multiple detection results that reach the second preset detection result. Of course, the second preset detection result can also be reasonably set so that the selected filter component has the best detection result.

[0071] This disclosure does not limit the specific method for preparing the filter assembly, nor the specific way of using the filter assembly to build a color sensing device and detection system. Those skilled in the art can set it according to the actual situation and needs. The following is an exemplary description.

[0072] Please see Figure 2 , Figure 2 A flowchart is shown of a method for determining the filter of a color sensing device according to an embodiment of the present disclosure.

[0073] In one possible implementation, such as Figure 2 As shown, the method may include:

[0074] Step S31 provides multiple filter assemblies with different numbers of filters, including: selecting multiple filters with different numbers from N types of filters to form multiple filter assemblies, wherein each of the N types of filters has a different spectral transmission relationship, and the N types of filters can encode colors within the target wavelength range, where N is a positive integer;

[0075] Step S32 provides multiple filter assemblies with different filter combination methods, including: determining the minimum number of continuous or skip distribution combinations from N types of filters multiple times.

[0076] The embodiments disclosed herein do not limit the specific size of the target wavelength range, which can be determined by those skilled in the art based on actual circumstances and needs.

[0077] For example, the spectral transmittance relationship can represent the correspondence between the spectral transmittance of the filter and the wavelength of light.

[0078] For example, after preparing multiple filter assemblies with different numbers of filter elements, embodiments of this disclosure can use each filter assembly to detect the test object and obtain multiple detection results. Among the detection results corresponding to filter assemblies with different numbers of filter elements, the minimum number of filter elements is selected from the detection results that achieve the first preset detection result, and the minimum number is used as the number of filter elements in the filter assembly.

[0079] For example, after determining the number of filters in the filter assembly, this embodiment of the disclosure can determine the minimum number of consecutive or skip distribution combinations from N types of filters multiple times to provide multiple filter assemblies with different filter combination methods. In the detection results corresponding to different filter combinations with the same number of filters, the combination method of the filter combination that reaches the second preset detection result is selected as the combination method of the filters in the filter assembly.

[0080] This disclosure does not limit the specific implementation methods for obtaining various detection results. Those skilled in the art can obtain detection results according to actual conditions and needs. The following is an exemplary description.

[0081] In one possible implementation, such as Figure 2 As shown, the method may further include:

[0082] Step S21: Input the imaging information into the detection model, and obtain the detection result using the output of the detection model, wherein the detection model has a mapping relationship between the imaging information and the detection result.

[0083] For example, in this embodiment of the disclosure, after preparing multiple filter assemblies with different numbers of filters, the incident light is encoded using the filter assemblies. Each filter assembly obtains corresponding imaging information based on the incident light, and the imaging information is input into a detection model. The detection result is obtained using the output of the detection model. This embodiment of the disclosure does not limit the source of the incident light. The incident light can be one or more of the following: transmitted light, reflected light, or fluorescence, obtained after the probe light emitted by the light source illuminates the color reaction component.

[0084] For example, in this embodiment of the disclosure, the incident light can be encoded using a filter assembly, and the corresponding imaging information can be obtained by each filter assembly based on the incident light. The imaging information is then input into a detection model, and the detection result is obtained by using the output of the detection model.

[0085] In one possible implementation, such as Figure 2 As shown, the method may further include:

[0086] Step S22: Detect the imaging information to obtain a rectangular grayscale image array.

[0087] For example, the detection model of this disclosure is not limited to obtaining the detection result based on imaging information. In some possible implementations, this disclosure can obtain a detection image (such as a rectangular grayscale image array) based on imaging information through appropriate technical means, input the rectangular grayscale image array into the detection model, and obtain the detection result using the output result of the detection model. The detection model has a mapping relationship between the detection image (such as a rectangular grayscale image array) and the detection result.

[0088] This disclosure does not limit the specific type of the test object, the type of detection result, or the specific implementation method of the detection model. Those skilled in the art can determine the test object and select appropriate detection parameters and models based on actual conditions and needs. For example, in one possible implementation, the test object can be a liquid, and the detection result includes components and the content of each component. For instance, the test object can be pesticides, blood, urine, or other liquid test objects, and the detection result can be the composition of the test object and the content of each component, or it can be other classification results. In one possible implementation, the detection result can include the components of the test object or the components of the test object and the content of each component. The detection model is based on at least one of least squares, neural networks, support vector machines, Naive Bayes classification, decision trees, k-nearest neighbors, linear discriminant analysis, linear regression, logistic regression, classification and regression trees, learning vector quantization, bagging, and random forests. This disclosure does not limit the specific methods for establishing and training the detection model; those skilled in the art can use appropriate means to implement it based on actual conditions and needs.

[0089] In one possible implementation, the rectangular grayscale image array may include multiple rectangular regions, each corresponding to a different filter. Each rectangular region corresponds to multiple grayscale values ​​and includes multiple pixels. For example, the grayscale values ​​of the various rectangular regions in the rectangular grayscale image array are different. Of course, the specific shape of the rectangular grayscale image array is not limited in this embodiment. The rectangular grayscale image array can be any shape composed of multiple regions with different grayscale values, each corresponding to a different filter. For example, the rectangular grayscale image array may include multiple rectangular regions arranged in an array of T rows and P columns, where T and P can both be integers greater than 0. The size of each rectangular region can be the same or different. This embodiment does not limit the arrangement of the rectangular grayscale image array, the number of rows, or the number of columns. For example, T can be 8, and P can be 15, meaning a rectangular grayscale image array may include 120 rectangular regions, forming an 8-row, 15-column rectangular grayscale image array.

[0090] In one possible implementation, the filter type includes at least one type such as metasurface filter, photonic crystal filter, perovskite quantum dot filter, colloidal quantum dot filter, etc., and each filter type includes multiple different types.

[0091] In one possible implementation, the filter combination may include colloidal quantum dot filters, each with a different spectral transmission relationship. The filters encode the incident light based on the spectral transmission relationship and the spectral sensitivity relationship of the corresponding detection component of each filter, thereby obtaining the imaging information of the incident light. The spectral sensitivity relationship represents the relationship between photoresponsivity and light wavelength.

[0092] The following examples illustrate possible implementations of determining the number and combination of filters. It should be understood that the following examples should not be considered as limitations on the embodiments of this disclosure.

[0093] For example, let N be the total number of filters. The filters can be of the colloidal quantum dot type, metasurface structure type, photonic crystal structure type, perovskite quantum dot type, etc. For example, the total number of filters N can range from tens to thousands. Taking colloidal quantum dot filters as an example, this embodiment of the disclosure can synthesize hundreds or thousands of colloidal quantum dots through a chemical synthesis process. The type and number N of colloidal quantum dots in the target wavelength band are determined according to the target wavelength band of the sensing application. For example, if the spectral range of color change is 380nm-750nm, this embodiment of the disclosure can synthesize 240 types of colloidal quantum dots with a transmission rise peak in the 380nm-750nm range.

[0094] For example, in the target wavelength band of the sensing application (e.g., 380nm-750nm), the filters can be arranged from shortest wavelength to longest wavelength according to the transmission rise peak position, with a total of N filters. For instance, according to the arrangement order, 10, 15, 20, 25, 30, ..., N-1 (or other numbers) filters can be selected to form multiple filter assemblies, and imaging information or detection images (e.g., rectangular grayscale image arrays) corresponding to the color changes of the test object under different numbers of filter assemblies can be obtained. A detection model (e.g., a neural network model) is used to classify or fit the imaging information or rectangular grayscale image arrays corresponding to different numbers of filter assemblies to obtain the detection results, and relevant evaluation indicators are used to quantify the classification or fitting effect of the neural network model. For example, the evaluation indicators can be mean square error, root mean square error, classification accuracy, etc.

[0095] For example, assuming the wavelength range of color change of the analyte is 380nm-750nm, and there are 240 types of quantum dot filters within this range, the quantum dot filters can be sorted from smallest to largest according to the wavelength of the rising transmission curve. Following this order, 10, 20, 30, 40, 60, 80, and 120 quantum dot filters are uniformly selected to form various filter assemblies. Different numbers of filter assemblies are used for color measurement of the analyte, acquiring imaging information or rectangular grayscale image arrays of different components and their contents of the analyte (assuming it is liquid). A detection model (such as a neural network model) is used for quantitative fitting, and evaluation indicators such as mean square error are used for assessment. The detection model (such as a neural network model) can map imaging information or rectangular grayscale image arrays to material components and their contents or types. The specific parameters and training process of the detection model (such as a neural network model) can be determined by those skilled in the art according to actual conditions and needs, and will not be elaborated upon in this embodiment.

[0096] For example, embodiments of this disclosure can select a corresponding first preset detection result to determine the minimum number of filters in the filter assembly, such as determining the minimum number M of filters required based on application accuracy requirements. For instance, after obtaining the fitting / classification results of the neural network model under different uniformly distributed filter assemblies, a threshold (first preset detection result) can be set according to the accuracy requirements of the specific color sensing application. The first preset detection result may include classification accuracy, detection limit, etc., and the minimum number of filters required that is greater than the set threshold is determined to be M. For example, the fitting results of each neural network under uniformly distributed filter assemblies of 10, 20, 30, 40, 60, 80, and 120 can be obtained, and the mean square error can be used as the evaluation standard. A mean square error threshold e (first preset detection result) can be set according to the specific application, and the minimum number of filters with a mean square error greater than the threshold e can be selected. Assuming that the minimum number of filters in the filter assembly is M = 30.

[0097] For example, after determining the minimum number of filters in the filter assembly, a number of M filters can be selected from the total number of filters (N types) to determine the combination method of the filters in the filter assembly. For example, it can be a combination method that achieves a second preset detection result among the detection results corresponding to different combinations of filters with the same number of filters.

[0098] In one example, M filters can be selected from a total of N filters, thus determining multiple combinations and forming corresponding filter assemblies. Multiple experiments are then conducted to obtain multiple evaluation metrics for each combination. Since a uniformly distributed set of M filters is not necessarily the optimal result, other combinations such as concentrated continuous distributions and skip distributions are also possible. The step of uniformly selecting M filters is the first step of screening, while the traversal screening is the second step of precise screening. Traversal screening involves significant computational cost. To reduce this, concentrated continuous distributions and uniform distributions can be compared and screened, reducing workload. Alternatively, weights can be assigned based on the main wavelength band of the color change of the analyte, and the filter type can be selected accordingly. For example, assuming there are 240 quantum dot filters (N=240) in the 380nm-750nm wavelength band, the minimum number of filters M=30 requires traversal screening. Due to the large amount of data involved in the computation, this embodiment of the present disclosure can select 30 quantum dot filters in a centralized distribution for screening and comparison. This reduces the number of computations to 210, obtaining the neural network fitting results (i.e., the detection results of the detection model) under 210 consecutive centralized distribution conditions (e.g., 1-30, 2-31, 3-32, ..., 210-239). The neural network fitting results of the 210 cases are compared with the second preset detection results, and the combination of the best evaluation index is selected as the final optimization scheme.

[0099] For example, after selecting M filters and their arrangement, the embodiments of this disclosure can use this combination to guide the fabrication of filter assemblies and color sensors. For instance, after selecting M filters, subsequent mass production of color sensors used for this color sensing application only requires integrating the optimized M filters, instead of integrating N filters, which greatly reduces the number of filters, saves costs, and does not reduce the detection accuracy.

[0100] Of course, for a detailed introduction on the adaptive determination of the number and combination of filters, please refer to the previous description of the detection system, which will not be repeated here.

[0101] Please see Figure 3 , Figure 3 A schematic diagram of the transmission spectrum of various colloidal quantum dot filters according to embodiments of the present disclosure is shown.

[0102] For example, Figure 3 The 120 colloidal quantum dot filters shown cover a wavelength range of 380nm to 750nm. Each of the 120 colloidal quantum dot filters has a different spectral transmittance function, ensuring that it can encode colors within the 380nm to 750nm range. Each colloidal quantum dot filter encodes the incident light as an intensity value I. i .

[0103] In one possible implementation, the filter array preferably comprises colloidal quantum dot filters, each type of colloidal quantum dot filter having a different spectral transmittance relationship, which represents the correspondence between the spectral transmittance of the filter and the wavelength of light.

[0104] The filter encodes the incident light based on the spectral transmission relationship and the spectral sensitivity relationship of the detection component corresponding to each filter, thereby obtaining the imaging information of the incident light. The spectral sensitivity relationship represents the relationship between spectral responsivity and light wavelength.

[0105] In one possible implementation, the filters encode the incident light based on the spectral transmission relationship and the spectral sensitivity relationship with the detection component corresponding to each filter, to obtain imaging information of the incident light, including:

[0106] The filter encodes the incident light based on the following formula 1 to obtain the imaging information of the incident light:

[0107]

[0108] in, θ represents the spectral transmission relationship of the i-th filter. i (λ) represents the spectral sensitivity relationship between the i-th detector component and each filter, where x(λ) represents the spectrum of the incident light, and I i Let λ represent the light intensity value corresponding to the i-th filter, λ represent the light wavelength, λ1 represent the minimum wavelength of the light band, and λ2 represent the maximum wavelength of the light band.

[0109] For example, regarding micro-spectrometer technology, It requires a complex, tedious, and costly calibration process to determine, because the spectral reconstruction process requires... The information. However, regarding the detection scheme proposed in the embodiments of this disclosure, This embodiment does not require calibration to determine its value or spectral reconstruction. It only requires knowing that different colloidal quantum dot filters achieve different encoding results. Then, the detection result can be obtained based on the probe image or the imaging information. For example, a mapping relationship between a rectangular grayscale image array or the imaging information and the material composition and content can be established. Using this mapping relationship, the detection result can be obtained based on the probe image or the imaging information.

[0110] Please see Figure 4 , Figure 4 A schematic diagram is shown illustrating the determination of a detection model and the detection of concentration according to an embodiment of the present disclosure.

[0111] For example, such as Figure 4 As shown, in this embodiment, the incident light is encoded into imaging information by a filter assembly to obtain the light intensity value of each sensing channel. A rectangular grayscale image array is obtained based on the imaging information, and the mapping of the rectangular grayscale image array or imaging information to the material composition and its content is directly realized, thereby achieving qualitative and quantitative detection.

[0112] For example, when detecting substances of different concentrations, the different concentrations of substances cause paper bases and films to exhibit different color changes. The detection system using the embodiments of this disclosure can acquire rectangular grayscale image arrays corresponding to substances of different concentrations, and then use image processing technology and other algorithms for detection.

[0113] For example, such as Figure 4 As shown, to establish a detection model, this embodiment of the disclosure can obtain the average intensity value of each filter region of the rectangular grayscale image array as the light intensity value under the sensing channel of that filter, and then merge them into a high-dimensional vector of the rectangular grayscale image array. Different concentrations of substances correspond to different high-dimensional vectors, and the substance concentration can be fitted using algorithms such as least squares method, neural networks, and various machine learning algorithms to draw a fitting curve, calculate its detection limit (LOD), and obtain the detection model. After the detection model is established, the detection image obtained by the detection component can be used to determine the unknown concentration.

[0114] Experimental comparisons with RGB image-based methods show that the detection method using the filter assembly obtained in this embodiment significantly improves the fitting effect and reduces the detection limit compared to the RGB image method. This embodiment demonstrates strong color sensing capability. For example,... Figure 4 As shown, the present invention provides a highly accurate method for detecting the composition and content of a solution.

[0115] The following example illustrates the method for determining the filter in a color sensor device, using the example of determining the filter assembly in a color sensor device for detecting urine.

[0116] Please see Figure 5 , Figure 5 A schematic diagram of a filter determination method for a color sensing device according to an embodiment of the present disclosure is shown.

[0117] For example, the glucose content in urine is an important marker for measuring whether or not someone has diabetes and the degree of diabetes, so it is meaningful to develop a low-cost, portable urine glucose detection sensor.

[0118] Figure 5 Figure (1) shows a flowchart of the optimization process for urine glucose detection filters. Exemplarily, this embodiment of the present disclosure uses an array of colloidal quantum dot filters for a urine glucose detection color sensor, for example, using 120 types of colloidal quantum dots (N=120) with transmission rise peaks located in the 380nm-750nm spectral range. However, when fabricating a urine glucose color sensor, simply using 120 types of colloidal quantum dot filters is not feasible because the filter sensing channels are redundant. This redundancy introduces random errors, affecting the detection results of glucose by the detection model formed by pattern recognition methods such as neural networks. Therefore, in this embodiment, multiple filter arrays can be formed by selecting multiple filter arrays of different numbers from N types of filter arrays to provide multiple filter arrays with different numbers of filter arrays. Among the detection results corresponding to filter arrays with different numbers of filter arrays, the minimum number of filter arrays is selected from the detection results that achieve a first preset detection result. This minimum number is used as the number of filter arrays in the filter array. For example, 120 filter arrays can be sorted according to the rising peak from smallest to largest. 20, 30, 40, 60, 90, and 120 uniformly distributed quantum dot filter arrays can be selected to form 7 filter arrays. Further, 7 corresponding color sensors are prepared. The image sensors used are all of the same complementary metal-oxide-semiconductor (CMOS) type, and the same neural network architecture or other pattern recognition method is used to form a detection model for quantitative fitting. In this example, a three-layer fully connected neural network is used, and the mean square error is used as the evaluation index (normalized). The evaluation indices for the 7 color sensors are as follows: Figure 5As shown in (2). The color sensor composed of 120 quantum dot filters is not the optimal result. This is because for glucose sensing, 120 quantum dot filters are redundant. Redundant filters introduce random errors, such as detector noise, which affect the fitting effect. Compared with the sensor composed of 120 filters, the sensor composed of 20 uniformly distributed filters (quantum dot filter numbers are 1, 7, 13, 19, 25, 31, 37, 43, 49, 55, 61, 67, 73, 79, 85, 91, 97, 103, 109, 115; arranged from short wavelength to long wavelength according to the absorption peak position of the filter transmission spectrum) has the best evaluation index. Its evaluation index is nearly an order of magnitude lower than that of the sensor composed of 120 filters. The first step of the filter optimization method proposed in the embodiments of this disclosure reduces the number of quantum dot filters from 120 to 20, improving detection performance (the evaluation index is reduced by nearly an order of magnitude) while reducing costs (cost is reduced to one-sixth). Therefore, for urine glucose sensing, a color sensor composed of 20 different filters is used.

[0119] For example, further, since 20 uniformly distributed filter types are not necessarily the optimal result among all distributions of the 20 filter types, embodiments of this disclosure can determine the optimal distribution of the 20 filter types. For example, the minimum number of consecutive or skip distribution combinations can be determined multiple times from N filter types to provide multiple filter assemblies with different filter combination methods. Among the detection results corresponding to different filter combinations with the same number of filter types, the combination method of the filter combination that reaches the second preset detection result is selected as the filter combination method in the filter assembly. For example, 20 common types are selected from 120 filter types. One selection method involves choosing 20 consecutively distributed filters (1-20, 11-30, ..., 101-120, a total of 11 arrangements) to form a corresponding filter array, which is then used to construct the corresponding color sensor, resulting in 11 color sensors. For example, a detection model constructed using neural network algorithms can be fitted to the detection results of each of the 11 color sensors, using mean squared error (MSE) as the evaluation metric (normalized). Figure 5As shown in (3). The evaluation index of the color sensor with 11 continuously distributed filters is greater than that of the color sensor with uniform distribution. Therefore, in the second step of determining the 20 types of filters, this embodiment of the present disclosure selects 20 types of uniformly distributed filters to reduce the detection limit (LOD). Of course, different optimization results will appear in the second step for different applications. For example, for urine nitrite sensor, in one example, the color sensor composed of continuously distributed 71-90 quantum dot filters has the best performance and the smallest evaluation index. Finally, the color sensor is further prepared by forming an array of the selected 20 uniformly distributed filters (quantum dot filter numbers are 1, 7, 13, 19, 25, 31, 37, 43, 49, 55, 61, 67, 73, 79, 85, 91, 97, 103, 109, 115).

[0120] Please see Figure 6 , Figure 6 A schematic diagram of a color sensing device according to an embodiment of the present disclosure is shown.

[0121] like Figure 6 As shown, the device includes:

[0122] The filter assembly 30 obtained using the filter determination method of the color sensing device is used to encode incident light into imaging information. The imaging information includes the light intensity value of the incident light. The filter assembly 30 includes multiple different types of filters, each capable of encoding incident light to obtain imaging information. For example, the imaging information can be light intensity information. A detection component (such as a CCD) can be used to detect the imaging information (light intensity information) to obtain a rectangular grayscale image array. The imaging process is essentially the process of using a detection component (such as a CCD) to detect the imaging information (light intensity information) to obtain a rectangular grayscale image array.

[0123] The filter determination method for the color sensing device described in this disclosure involves selecting the minimum number of filters from the detection results corresponding to filter assemblies with different numbers of filters, and using this minimum number as the number of filters in the filter assembly. Then, from the detection results corresponding to different combinations of filters with the same number of filters, the method selects the combination of filters that achieves the second preset detection result. This combination is the filter combination method in the filter assembly. The filter array can be optimized according to specific applications, reducing cost, size, and integration complexity while ensuring the accuracy of the color sensing device, and improving measurement accuracy. This allows the color sensing device implemented using the filter assembly to simultaneously achieve reduced size, improved accuracy, simplified structure, and reduced cost.

[0124] It should be noted that the detailed description of the filter determination method for the color sensing device is provided in the previous description and will not be repeated here.

[0125] Please see Figure 7 , Figure 7 A schematic diagram of a color sensing device according to an embodiment of the present disclosure is shown.

[0126] In one possible implementation, such as Figure 7 As shown, the device may further include:

[0127] The detection component 40 is used to detect the imaging information, which is input into the detection model to obtain the detection result using the output of the detection model. The detection model has a mapping relationship between the imaging information and the detection result.

[0128] In one possible implementation, the detection component may include at least one of a complementary metal-oxide-semiconductor (CMOS) element, a charge-coupled device (CCD) element, an ultraviolet (UV) detection element, and an indium gallium arsenide (IGaAs) near-infrared (NIIR) detection element. For example, for visible light color sensing applications, the detection component may be a CMOS or a CCD element; for ultraviolet (UV) color sensing applications, the detection component is an UV detection element; and for near-infrared (NIIR) color sensing applications, the detection component is an IGaAs NIIR detection element. Thus, the detection system of this embodiment eliminates the need for an imaging optical lens, further reducing instrument size and cost.

[0129] For example, the detection component acquires image information of the filter component under different colors. The image information can be hyperspectral image information or grayscale image. The grayscale image is similar to a barcode distribution. In this embodiment of the disclosure, a rectangular grayscale image array and a pattern recognition algorithm are directly used for qualitative classification and quantitative detection of substances.

[0130] Please see Figure 8 , Figure 8 A schematic diagram is shown of a detection image formed by a detection component according to an embodiment of the present disclosure based on imaging information from a filter component.

[0131] For example, each colloidal quantum dot filter can cover a rectangular area on the detector assembly, with each rectangular area consisting of nearly a hundred pixels. The advantage of using colloidal quantum dots is that their projection function is unaffected by the incident light angle, making them suitable for a wide range of applications, including reflective oblique incidence scenarios. Rectangular grayscale image array technology eliminates the need for imaging lenses, further reducing cost and size compared to other imaging techniques.

[0132] The embodiments disclosed herein acquire detection images, such as rectangular grayscale image array data, rather than spectral image data through a filter assembly and a detection assembly. This eliminates the need for calibration and spectral reconstruction processes, reduces processing complexity and cost, simplifies the detection process, and improves measurement accuracy.

[0133] This disclosure does not limit the combination of the filter assembly and the detector assembly. Each filter in the filter assembly has a different spectral transmission function, which can fully sample and encode the incident light. This allows the information of each band of the incident light to be converted into light intensity information by the filter assembly, and then sampled by the detector assembly to form a detection image (such as a rectangular grayscale image array). For example, each type of incident light forms different imaging information, which includes the spectral information of the incident light and plays a spectral role in representing color changes. Compared with traditional large spectrometers, the design of the filter assembly avoids complex and bulky spectroscopic systems, greatly reducing the size of the color sensor.

[0134] Please see Figure 9 , Figure 9 A block diagram of a detection system according to an embodiment of the present disclosure is shown.

[0135] like Figure 9 As shown, the detection system may include:

[0136] The color sensing device includes a filter assembly 30 and a detector assembly 40;

[0137] Light source 10 is used to emit detection light;

[0138] Color reaction component 20, the color reaction component 20 is used to obtain one or more of the following after the detection light is irradiated: transmitted light, reflected light or fluorescence, and to incident the light obtained after irradiation onto the filter assembly;

[0139] The data processing component 50 is used to obtain the detection result based on the detection image or imaging information generated by the color sensing device.

[0140] Compared with visual inspection and RGB methods in related technologies, the detection system of this disclosure can achieve more accurate color sensing. In the specific color information acquisition process, by acquiring a detection image (such as a rectangular grayscale image array) instead of spectral image data, the effect of eliminating the need for calibration and spectral reconstruction can be achieved, simplifying the testing process and requirements.

[0141] In this embodiment, a light source 10 emits probe light to illuminate a color reaction component. The light emitted by the light source 10 passes through the color reaction component 20 to obtain one or more of the following: transmitted light, reflected light, or fluorescence, and is then incident on the filter assembly 30. The filter assembly encodes the incident light into imaging information, which includes the light intensity value of the incident light. The filter assembly 30 includes multiple filters of different types, and different filters can encode the incident light to obtain different imaging information. The detection component 40 detects the imaging information and generates a detection image (such as a rectangular grayscale image array). The data processing component 50 obtains the detection result based on the detection image or the imaging information, which can achieve accurate color sensing. Furthermore, this system has the advantages of low cost, small size, and the ability to optimize the filter array according to specific applications, reducing costs and integration complexity, and improving measurement accuracy.

[0142] The embodiments disclosed herein do not limit the specific implementation of the light source 10, color reaction component 20, filter component 30, detection component 40, and data processing component 50. Those skilled in the art can adopt appropriate technical means to implement them according to actual conditions and needs. The following is an exemplary description.

[0143] For example, the light source 10 can be customized according to the specific application. For example, for visible light applications, LED light source 10, halogen tungsten lamp light source 10, etc. can be selected, or natural light and other forms of light sources can be used.

[0144] For example, the color reaction component 20 can interact with the analyte to produce a detectable color change. When the probe light illuminates the color reaction component 20, color-related information of the color reaction component 20 can be obtained. For example, the color reaction component 20 can be filled or loaded with certain reagents that can interact with the analyte to produce a color change. The materials of the reagents include one or more of quantum dot materials, chemical dyes, fluorescent luminescent materials, etc. In one embodiment, the color reaction component 20 can be obtained by loading or filling the above-mentioned reagents onto a carrier. The carrier can be set according to actual needs. For example, optional carrier materials include one or more of polytetrafluoroethylene (PTFE), polyvinylidene fluoride (PVDF), polyethylene terephthalate (PET), nylon, non-woven fabric, MCE, PP, etc.

[0145] For example, the color reaction component 20 can be a customized placement slot in which the reagent can be placed. Alternatively, in one embodiment, a carrier such as a paper base or film can be placed in the placement slot, and the reagent can be loaded on the carrier. Those skilled in the art can achieve maximum optical efficiency or obtain target optical information by reasonably adjusting the positional relationship between the color reaction component 20 and the light source 10 and the detection component 40. Of course, the specific positional relationship between the color reaction component 20 and the light source 10 and the detection component 40 is not limited in the embodiments of this disclosure.

[0146] In one possible implementation, the filter type includes at least one type of metasurface filter, photonic crystal filter, perovskite quantum dot filter, and colloidal quantum dot filter, with each filter type including multiple different models.

[0147] In one possible implementation, the detection component includes at least one of a complementary metal-oxide-semiconductor element, a charge-coupled device, an ultraviolet detection element, and an indium gallium arsenide near-infrared detection element.

[0148] In one possible implementation, the filter assembly includes colloidal quantum dot filters, each type of colloidal quantum dot filter having a different spectral transmittance relationship, which represents the correspondence between the spectral transmittance of the filter and the wavelength of light.

[0149] The filter encodes the incident light based on the spectral transmission relationship, the spectral sensitivity relationship of the detection component corresponding to each filter, and the spectrum of the incident light to obtain the imaging information of the incident light. The spectral sensitivity relationship represents the relationship between spectral responsivity and light wavelength.

[0150] In one possible implementation, the filters encode the incident light based on the spectral transmission relationship and the spectral sensitivity relationship with the detection component corresponding to each filter, to obtain imaging information of the incident light, including:

[0151] In one embodiment, the filter encodes the incident light based on the following formula to obtain the imaging information of the incident light:

[0152] in, θ represents the spectral transmission relationship of the i-th filter. i (λ) represents the spectral sensitivity relationship between the i-th detector component and each filter, where x(λ) represents the spectrum of the incident light, and I i Let λ represent the light intensity value corresponding to the i-th filter, λ represent the light wavelength, λ1 represent the minimum wavelength of the light band, and λ2 represent the maximum wavelength of the light band.

[0153] In one possible implementation, the probe image is a rectangular grayscale image array, comprising multiple rectangular regions, with each filter corresponding to one rectangular region. Each rectangular region corresponds to multiple grayscale values, and each rectangular region includes multiple pixels. For example, in the above formula... It can be 1, and the corresponding detection image is a rectangular grayscale image array.

[0154] In one possible implementation, the data processing component 50 includes, but is not limited to, a separate processor, discrete components, or a combination of a processor and discrete components. The processor may include a controller in an electronic device with instruction execution capabilities. The processor may be implemented in any suitable manner, for example, by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components. Within the processor, the executable instructions may be executed by hardware circuitry such as logic gates, switches, ASICs, programmable logic controllers, and embedded microcontrollers.

[0155] In one possible implementation, the data processing component 50 may include a terminal device, a server, or other processing equipment. The terminal device may be a user equipment (UE), mobile device, user terminal, terminal, handheld device, computing device, or in-vehicle device, etc. Examples of terminals include: mobile phones, tablets, laptops, PDAs, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, and wireless terminals in vehicle-to-everything (V2X) networks. For example, the server may be a local server or a cloud server.

[0156] This disclosure does not limit the specific implementation of the data processing component 50 obtaining the detection result based on the detected image or the imaging information. Those skilled in the art can adopt appropriate technical means to implement it according to actual conditions and needs.

[0157] In one possible implementation, obtaining the detection result based on the probe image or the imaging information may include:

[0158] The detection image or the imaging information is input into the detection model, and the detection result is obtained by using the output of the detection model, wherein the detection model has a mapping relationship between the detection image or the imaging information and the detection result.

[0159] For example, in transmission color measurement, embodiments of this disclosure may first measure a rectangular grayscale image array without solution; then obtain a rectangular grayscale image array with solution; subtracting the two rectangular grayscale image arrays yields a rectangular grayscale image array of solution absorption.

[0160] For example, the filter assembly of this disclosure embodiment can be integrated with terminal devices such as smartphones, using natural light or the smartphone's flashlight as the light source 10, or the terminal device's camera as the detection component to achieve a portable color sensing solution. Furthermore, the data processing component 50 can be implemented as software for the terminal device, making operation more convenient and simple. Simultaneously, leveraging the terminal device's Bluetooth and network communication functions, data sharing between multiple devices can be achieved. For instance, any smartphone, when paired with the filter assembly and data processing component 50, can become a color sensor, realizing a portable, low-cost, and compact color sensing solution that meets the needs of various fields such as clinical medicine and environmental monitoring.

[0161] The detection system of this disclosure can accurately sense color changes to qualitatively and quantitatively identify the composition and content of substances. Each part of the detection system can be customized for specific applications. For example, for pesticide detection, it can acquire a rectangular grayscale image array of absorbed colors in liquid phase; for urine component identification, it can acquire a rectangular grayscale image array of reflected colors from a paper-based colorimetric array. Specifically, each color corresponds to a rectangular grayscale image array, and the rectangular grayscale image array combined with a pattern recognition algorithm can be used directly to qualitatively and quantitatively identify color changes to determine the composition and content of substances. Compared to micro-spectrometer technology, whose calibration process relies on expensive instruments and complex experimental procedures, the rectangular grayscale image array technology implemented in this disclosure using filter components and detector components does not require experimental calibration of the filter components and detector components. The rectangular grayscale image array technology is lower in cost, resulting in a correspondingly lower-cost and simpler detection system.

[0162] In one possible implementation, the data processing component can also be used for:

[0163] Acquire a first detection image and a second detection image output by the color sensing device. The first detection image is the detection image output by the color sensing device when no test substance is added to the color reaction component. The second detection image is the detection image output by the color sensing device when the test substance is added to the color reaction component.

[0164] The intensity of the corresponding pixel in the second detection image is subtracted from that in the first detection image to obtain the third detection image;

[0165] The third detection image is input into the detection model, and the detection results of the components of the analyte or the components of the analyte and the content of each component are obtained by using the output results of the detection model. The detection model has a mapping relationship between the detection image or the imaging information and the detection results.

[0166] For example, in transmission color measurement, embodiments of this disclosure may first measure a rectangular grayscale image array without solution; then obtain a rectangular grayscale image array with solution; subtracting the two rectangular grayscale image arrays yields a rectangular grayscale image array of solution absorption.

[0167] This disclosure does not limit the specific type of the analyte, the type of detection result, or the specific implementation method of the detection model. Those skilled in the art can determine the analyte and select appropriate detection parameters and models based on actual conditions and needs. For example, in one possible implementation, the analyte is a liquid, and the detection result includes components and the content of each component. For instance, the analyte can be pesticides, blood, urine, or other liquid analytes, and the detection result can be the composition of the analyte and the content of each component, or it can be other classification results. In one possible implementation, the detection model can be based on at least one of least squares method, neural network, support vector machine, etc. This disclosure does not limit the specific methods for establishing and training the detection model; those skilled in the art can use appropriate means to implement it based on actual conditions and needs.

[0168] In one possible implementation, the color reaction component includes a reflective component, a transmissive component, and a fluorescent component, wherein the incident light is any one of the reflected light generated by the reflective component based on the probe light, the transmissive light generated by the probe light penetrating the transmissive component, or the fluorescence generated by the incident light irradiating the fluorescent component.

[0169] In one possible implementation, the filter assembly in the color sensing device is capable of encoding incident light in the 380nm-750nm range.

[0170] Compared with visual inspection and RGB methods in related technologies, the detection system of this disclosure can achieve more accurate color sensing. In the process of acquiring specific color information, by acquiring a detection image (such as a rectangular grayscale image array) instead of spectral image data, the effect of eliminating the need for calibration and spectral reconstruction can be achieved, simplifying the testing process and requirements.

[0171] According to one aspect of this disclosure, a urine detection system is provided, the urine detection system comprising the color sensor or the detection system described above.

[0172] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further. Those skilled in the art will understand that in the above methods of specific implementation, the specific execution order of each step should be determined by its function and possible internal logic.

[0173] Furthermore, the detection method corresponds to the aforementioned detection system, and its specific description can be found in the previous description of the detection system, which will not be repeated here.

[0174] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0175] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the above-described method.

[0176] This disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0177] Electronic devices can be provided as terminals, servers, or other forms of devices.

[0178] Please see Figure 10 , Figure 10 A block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0179] For example, electronic device 800 can be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, and other terminals.

[0180] Reference Figure 10 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0181] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0182] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0183] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0184] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0185] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0186] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0187] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 may detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include an optical sensor, such as a complementary metal-oxide-semiconductor (CMOS) or charge-coupled device (CCD) image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0188] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, second-generation mobile communication technology (2G), or third-generation mobile communication technology (3G), or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0189] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0190] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions that can be executed by a processor 820 of an electronic device 800 to perform the above-described method.

[0191] Please see Figure 6 , Figure 6 A block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0192] For example, electronic device 1900 can be provided as a server. (See reference...) Figure 6 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0193] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Microsoft Server operating system (Windows Server). TM Apple's graphical user interface-based operating system (Mac OSX) TM ), a multi-user, multi-process computer operating system (Unix) TM Linux is a free and open-source Unix-like operating system. TM ), the open-source Unix-like operating system (FreeBSD) TM (or similar.)

[0194] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0195] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for determining the filter of a color sensing device, characterized in that, The method includes: Among the detection results corresponding to filter assemblies with different numbers of filters, the minimum number of filters is selected from the detection results that reach the first preset detection result, and the minimum number is used as the number of filters in the filter assembly. The filter assembly is used to encode incident light into imaging information, and the imaging information includes the light intensity value of the incident light. The filter assembly includes multiple different types of filters, and different filters can encode incident light to obtain imaging information. Among the detection results corresponding to different combinations of filter elements with the same number of filter elements, the combination of filter elements that achieves the second preset detection result is selected as the combination method of filter elements in the filter element assembly; wherein, each filter element combination includes the minimum number of filter elements, and the types and / or arrangements of filter elements in each filter element combination are different; The method further includes: detecting the imaging information to obtain a rectangular grayscale image array, the rectangular grayscale image array including multiple rectangular regions, each type of filter corresponding to one rectangular region, the multiple rectangular regions corresponding to multiple grayscale values, and each rectangular region including multiple pixels; Both the first preset detection result and the second preset detection result are related to the accuracy of the color sensing.

2. The method according to claim 1, characterized in that, The method further includes: The imaging information is input into the detection model, and the detection result is obtained by using the output of the detection model, wherein the detection model has a mapping relationship between the imaging information and the detection result.

3. The method according to claim 1 or 2, characterized in that, The detection results include the components of the analyte or the components of the analyte and the content of each component. The detection model is based on at least one of the following: least squares method, neural network, support vector machine, Naive Bayes classification, decision tree, k-nearest neighbor algorithm, linear discriminant analysis, linear regression, logistic regression, classification and regression tree, learning vector quantization, bagging method and random forest.

4. The method according to claim 1 or 2, characterized in that, The types of filters include at least one type of metasurface filter, photonic crystal filter, perovskite quantum dot filter, and colloidal quantum dot filter, with each type of filter encompassing multiple different varieties.

5. The method according to claim 1, characterized in that, In the filter combination, the filter is a colloidal quantum dot filter. Each colloidal quantum dot filter has a different spectral transmission relationship. The filter encodes the incident light based on the spectral transmission relationship and the spectral sensitivity relationship of the detection component corresponding to each filter, thereby obtaining the imaging information of the incident light. The spectral sensitivity relationship represents the relationship between photoresponsivity and light wavelength.

6. The method according to claim 1, characterized in that, The method includes: Provides multiple filter assemblies with different numbers of filters, including: selecting multiple different numbers of filters from N types of filters to form multiple filter assemblies, wherein each of the N types of filters has a different spectral transmission relationship, and the N types of filters can encode colors within a target wavelength range, where N is a positive integer; Provide multiple filter assemblies with different filter combination methods, including: determining the minimum number of consecutive or skip distribution combinations from N types of filters multiple times.

7. A color sensing device, characterized in that, The device includes: A filter assembly obtained by the filter determination method of the color sensing device according to any one of claims 1 to 6, wherein the filter assembly is used to encode incident light into imaging information, the imaging information including the light intensity value of the incident light, and the filter assembly includes multiple different types of filters, and different filters can encode incident light to obtain imaging information.

8. The apparatus according to claim 7, characterized in that, The device further includes: A detection component is used to detect the imaging information, which is input into a detection model to obtain a detection result using the output of the detection model, wherein the detection model has a mapping relationship between the imaging information and the detection result.

9. The apparatus according to claim 8, characterized in that, The detection assembly includes at least one of a complementary metal-oxide-semiconductor element, a charge-coupled device, an ultraviolet detection element, and an indium gallium arsenide near-infrared detection element.

10. A detection system, characterized in that, The detection system includes: The color sensing device as described in any one of claims 7 to 9; A light source used to emit detection light; A color reaction component is configured to obtain one or more of the following after illumination by the probe light: transmitted light, reflected light, or fluorescence, and to incident the light obtained after illumination onto the filter assembly. A data processing component is used to obtain detection results based on the detection image or imaging information generated by the color sensing device.

11. The system according to claim 10, characterized in that, The data processing component is also used for: Acquire a first detection image and a second detection image output by the color sensing device. The first detection image is the detection image output by the color sensing device when no test substance is added to the color reaction component. The second detection image is the detection image output by the color sensing device when the test substance is added to the color reaction component. The intensity of the corresponding pixel in the second detection image is subtracted from that in the first detection image to obtain the third detection image; The third detection image is input into the detection model, and the detection results of the components of the analyte or the components of the analyte and the content of each component are obtained by using the output results of the detection model. The detection model has a mapping relationship between the detection image or the imaging information and the detection results.

12. The system according to claim 10, characterized in that, The color reaction component includes a reflective component, a transmissive component, and a fluorescent component, wherein the incident light is any one of the reflected light generated by the reflective component based on the probe light, the transmissive light generated by the probe light penetrating the transmissive component, or the fluorescence generated by the incident light irradiating the fluorescent component.

13. The system according to claim 10, characterized in that, The filter assembly in the color sensing device can encode incident light in the 380nm-750nm range.

14. A urine detection system, characterized in that, The urine detection system comprises the color sensing device according to any one of claims 7-9 or the detection system according to any one of claims 10-13.

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