Test paper interpretation and analysis method and system

Abstract

The application provides a test paper interpretation analysis method and system, the method comprises the following steps: obtaining test paper parameter information; obtaining a test paper image after reaction with a sample to be tested; the test paper image comprises a control line area and a detection line area; dividing the detection line area into an effective detection area and an ineffective detection area according to the test paper parameter information; extracting the gray value of the pixel points in the effective detection area and calculating a first gray value; the first gray value is the accurate gray value of the effective detection area; obtaining a second gray value according to the test paper parameter information; the second gray value is the accurate gray value of the control line area; comparing the first gray value and the second gray value to obtain a detection result. The test paper interpretation analysis method of the application improves the existing interpretation method, can obtain more accurate detection results, and can obtain more accurate quantitative detection results.

Description

Technical Field

[0001] The present application relates to the technical field of test paper interpretation and analysis, and in particular to a test paper interpretation and analysis method and system. Background Art

[0002] With the advancement of science and technology, test paper reading software that is used in conjunction with test paper has gradually appeared on the market. The test paper recognized by the software is mainly colloidal gold test paper. The recognition of the test paper results is mainly based on the presence or absence of the T line (test line) and the C line (control line), and the color depth contrast value relationship to judge the test result. The key to image scanning and processing lies in analyzing the characteristic information of the C and T line areas on the test paper by scanning and identifying the C line (control line) and T line (test line) of the test paper, and obtaining qualitative test results based on analysis of the recognition results.

[0003] In the prior art, when obtaining the color value of the T-line (test line), most methods extract a small number (e.g., 3-4) pixels at a fixed position (e.g., the center line of the T-line detection area) or a random position (i.e., any position within the T-line detection area) within the T-line detection area and then calculate the average value. However, the color within the T-line detection area is often not stable, i.e., there is a gradient area, and the detection results obtained by the prior art methods are not accurate. To this end, this application proposes a test paper interpretation and analysis method and system. Summary of the Invention

[0004] The purpose of this application is to provide a test paper interpretation and analysis method and system to address the above problems.

[0005] In a first aspect, the present application provides a test strip interpretation and analysis method, the method comprising the following steps:

[0006] Get test paper parameter information;

[0007] Acquire an image of the test paper after reacting with the sample to be tested; the image of the test paper includes a control line area and a detection line area;

[0008] Dividing the detection line area into a valid detection area and an invalid detection area according to the test paper parameter information;

[0009] Extracting the grayscale values ​​of the pixels in the effective detection area and calculating a first grayscale value; the first grayscale value is the precise grayscale value of the effective detection area;

[0010] Acquire a second grayscale value according to the test paper parameter information; the second grayscale value is the precise grayscale value of the control line area;

[0011] The first grayscale value and the second grayscale value are compared to obtain a detection result.

[0012] According to the technical solutions provided in certain embodiments of the present application, the test paper parameter information includes product function information and product model information.

[0013] According to the technical solutions provided in certain embodiments of the present application, extracting the grayscale values ​​of the pixels in the effective detection area and calculating the first grayscale value includes:

[0014] Extracting the grayscale values ​​of all pixels in the effective detection area to obtain an initial grayscale value set;

[0015] Calculating an average value of the initial grayscale value set to obtain a first average grayscale value;

[0016] Setting a valid grayscale value range according to the first average grayscale value;

[0017] Traversing the initial grayscale value set, screening grayscale values ​​within the valid grayscale value range, and obtaining a valid grayscale value set;

[0018] An average value of the valid grayscale value set is calculated to obtain the first grayscale value.

[0019] According to the technical solutions provided in certain embodiments of the present application, a method for setting a valid grayscale value range according to the first average grayscale value includes:

[0020] Setting an upper threshold value; the upper threshold value is a times the first average grayscale value;

[0021] Setting a lower threshold value; the lower threshold value is b times the first average grayscale value;

[0022] Among them, the value range of a is 1.05 <a<1.25;b的取值范围为0.75<b<0.95。

[0023] According to the technical solutions provided in certain embodiments of the present application, comparing the first grayscale value and the second grayscale value to obtain a detection result includes:

[0024] Determine a judgment curve according to the test paper parameter information;

[0025] Calculating a ratio of the first grayscale value to the second grayscale value to obtain a first ratio;

[0026] The first ratio is input into the judgment curve to obtain the concentration of the test solution.

[0027] In a second aspect, the present application provides a test strip interpretation and analysis system, comprising:

[0028] A scanning module configured to obtain test paper parameter information;

[0029] An image acquisition module configured to acquire an image of the test paper after reacting with the sample to be tested; the image of the test paper includes a control line area and a detection line area;

[0030] An image recognition and division module configured to obtain a corresponding image division rule according to the test paper parameter information, and divide the detection line area into a valid detection area and an invalid detection area according to the image division rule;

[0031] An extraction and calculation module configured to extract the grayscale values ​​of the pixels in the effective detection area and calculate a first grayscale value;

[0032] a comparison and calculation module, configured to compare and calculate the first grayscale value and the second grayscale value to obtain a detection result;

[0033] An output module is configured to output the detection result.

[0034] According to the technical solutions provided in certain embodiments of the present application, the test paper interpretation and analysis system further includes a storage module; the storage module is used to store image segmentation rules and interpretation curves corresponding to test papers of different functions and models.

[0035] According to the technical solutions provided in some embodiments of the present application, the extraction and calculation module includes an extraction submodule, a first calculation submodule, a setting submodule and a screening submodule;

[0036] The extraction submodule is configured to extract the grayscale values ​​of all pixels in the effective detection area to obtain an initial grayscale value set;

[0037] The first calculation submodule is configured to calculate an average value of the initial grayscale value set to obtain a first average grayscale value;

[0038] The setting submodule is configured to set a valid grayscale value range according to the first average grayscale value;

[0039] The screening submodule is configured to screen the grayscale values ​​within the valid grayscale value range from the initial grayscale value set to obtain a valid grayscale value set;

[0040] The first calculation submodule is further configured to calculate an average value of the valid grayscale value set to obtain the first grayscale value.

[0041] According to the technical solutions provided in certain embodiments of the present application, the setting submodule includes an upper limit setting unit and a lower limit setting unit;

[0042] The upper limit setting unit is configured to set an upper limit threshold of the valid grayscale value range;

[0043] The lower limit setting unit is configured to set a lower limit threshold of the valid grayscale value range.

[0044] According to the technical solutions provided in certain embodiments of the present application, the comparison and calculation module includes an acquisition submodule and a second calculation submodule;

[0045] The acquisition submodule is configured to acquire a corresponding interpretation curve according to the test paper parameter information;

[0046] The second calculation submodule is configured to calculate the ratio of the first gray value to the second gray value to obtain a first ratio, and is further configured to input the first ratio into the judgment curve to obtain the concentration of the detection liquid

[0047] Compared with the existing technology, the beneficial effects of this application are as follows: the test strip interpretation and analysis method proposed in this application divides the acquired test strip image detection line area into effective and invalid ones, and further analyzes and calculates within the effective detection area, thereby obtaining a more accurate first grayscale value representing the effective detection area. Finally, by comparing and calculating the first grayscale value and the second grayscale value, a more accurate qualitative detection result and a more accurate quantitative detection result are obtained. The test strip interpretation and analysis method of this application improves on the existing interpretation method and can obtain more accurate detection results and more accurate quantitative detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Flowchart of the test paper interpretation and analysis method provided in the embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solution of the present application, the present application is described in detail below with reference to the accompanying drawings. The description in this section is only exemplary and explanatory and should not have any limiting effect on the scope of protection of the present application.

[0050] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0051] Example 1

[0052] This embodiment provides a test paper interpretation and analysis system, which is based on a mobile terminal and is presented as an APP installed on the mobile terminal. The test paper interpretation and analysis system includes a scanning module, an image acquisition module, an image recognition and division module, an extraction and calculation module, a comparison and calculation module, an output module, and a storage module.

[0053] The storage module is used to store image division rules and interpretation curves corresponding to test papers of different functions and models.

[0054] The scanning module is configured to obtain test paper parameter information; the test paper parameter information includes product function information and product model information. The test paper interpretation and analysis system is used with special test paper. An identification code is printed on the test paper. The identification code can be a bar code or a QR code. When in use, the scanning function of the software is activated from the mobile phone terminal. The scanning module scans the identification code on the test paper through the mobile phone camera and obtains the test paper parameter information. The test paper parameter information includes product function information and product model information. The product function information represents the purpose of the current test paper, which is divided into ovulation test paper and early pregnancy test paper. The product model information represents the model of the current test paper.

[0055] The image acquisition module is configured to capture an image of the test paper after it reacts with the sample to be tested; the test paper image includes a control line area and a detection line area. When the user collects the sample with the test paper and leaves it for a reaction time, an image of the test paper after the reaction with the sample to be tested is captured. The image acquisition module uses the mobile phone camera to capture the image of the test paper, and the captured image of the test paper includes the control line area and the detection line area.

[0056] The image recognition and segmentation module is configured to obtain a corresponding image segmentation rule based on the test paper parameter information, and to divide the detection line area into a valid detection area and an invalid detection area based on the image segmentation rule. The color distribution of the detection line area on test papers with different functions and different signals varies. The image recognition and segmentation module can obtain a corresponding image segmentation rule from the storage module based on the test paper parameter information, and divide the detection line area into a valid detection area and an invalid detection area based on the rule.

[0057] The extraction and calculation module is configured to extract the grayscale values ​​of the pixels in the effective detection area and calculate the first grayscale value. Specifically, the extraction and calculation module includes an extraction submodule, a first calculation submodule, a setting submodule, and a screening submodule;

[0058] The extraction submodule is configured to extract the grayscale values ​​of all pixels in the effective detection area to obtain an initial grayscale value set;

[0059] The first calculation submodule is configured to calculate an average value of the initial grayscale value set to obtain a first average grayscale value;

[0060] The setting submodule is configured to set a valid grayscale value range according to the first average grayscale value; specifically, the setting submodule includes an upper limit setting unit and a lower limit setting unit; the upper limit setting unit is configured to set an upper limit threshold of the valid grayscale value range; the lower limit setting unit is configured to set a lower limit threshold of the valid grayscale value range; the upper limit threshold and the lower limit threshold together form a valid grayscale value range;

[0061] The screening submodule is configured to screen the grayscale values ​​within the valid grayscale value range from the initial grayscale value set to obtain a valid grayscale value set;

[0062] The first calculation submodule is further configured to calculate an average value of the valid grayscale value set to obtain the first grayscale value.

[0063] The comparison calculation module is configured to compare and calculate the first grayscale value and the second grayscale value to obtain a detection result. Specifically, the comparison calculation module includes an acquisition submodule and a second calculation submodule; the acquisition submodule is configured to obtain a corresponding judgment curve from the storage module according to the test paper parameter information; the variable value of the judgment curve is the ratio of the first grayscale value to the second grayscale value, and the function value is the concentration of the detection liquid; the second calculation submodule is configured to calculate the ratio of the first grayscale value to the second grayscale value to obtain a first ratio, and is also configured to convert the first grayscale value to the second grayscale value. A ratio is input into the interpretation curve to obtain the concentration of the detection fluid, and the concentration of the detection fluid is the quantitative detection result; for the early pregnancy test paper, the quantitative detection result is the concentration of human chorionic gonadotropin (HCG), and for the ovulation test paper, the quantitative detection result is the concentration of luteinizing hormone (LH); the second calculation submodule is further configured to obtain a qualitative detection result based on the ratio of the first grayscale value to the second grayscale value. For the early pregnancy test paper, the qualitative detection result includes weak positive, positive and strong positive, and for the ovulation test paper, the qualitative detection result includes fertile period and non-fertile period.

[0064] The output module is configured to output the detection results, and the detection results will be intuitively displayed to the user.

[0065] Example 2

[0066] This embodiment provides a test paper interpretation and analysis method, which uses the test paper interpretation and analysis system as described in Example 1. The flow chart of the test paper interpretation and analysis method is as follows: Figure 1 As shown, the test paper interpretation and analysis method includes the following steps:

[0067] S1. Obtain test paper parameter information;

[0068] The test paper interpretation and analysis method of this embodiment is used in conjunction with special test paper. An identification code is printed on the test paper. The identification code can be a bar code or a QR code. When in use, the scanning function of the software is started from the mobile phone terminal to scan the identification code on the test paper to obtain the test paper parameter information; the test paper parameter information includes product function information and product model information, wherein the product function information represents the purpose of the current test paper, which is divided into ovulation test paper and early pregnancy test paper, and the product model information represents the model of the current test paper.

[0069] S2, obtaining an image of the test paper after reacting with the sample to be tested; the image of the test paper includes a control line area and a detection line area;

[0070] After obtaining the test paper parameter information, the camera function of the mobile phone terminal will be automatically called to capture an image of the test paper after reacting with the sample to be tested. Prior to this, the user follows the instructions for use of the test paper to collect and add samples, and let it stand for the reaction time. When capturing the image, the user needs to hold the phone horizontally with both hands, keeping the phone parallel to the test paper on the table. The distance between the phone and the test paper is generally 10-15 cm. The obtained test paper image must include the control line area and the detection line area, where the control line is the C line on the test paper, and the detection line is the T line on the test paper.

[0071] S3, dividing the detection line area into a valid detection area and an invalid detection area according to the test paper parameter information;

[0072] Due to different functions and models, the color distribution of the test lines on different test strips is different. For example, the color of the test line of a certain model (denoted as model A) gradually deepens from the end close to the control line to the end far from the control line in its own width direction, and then tends to stabilize; while the color of the test line of another model of early pregnancy test strip gradually deepens from the end close to the control line to the end far from the control line in its own width direction, then tends to stabilize, and then gradually deepens again.

[0073] The storage module of the test paper interpretation and analysis system stores image division rules corresponding to test papers of different functions and models. Taking model A early pregnancy test paper as an example, the width of the detection line display area on the test paper is 1 mm and the height is 4 mm. The width of its color gradient area is approximately 0.5 mm, and the width of the color stable area is approximately 0.5 mm. The number of pixels in the width direction of the detection line display area on the test paper corresponding to the detection line area on the test paper image is 12, and the number of pixels in the length direction is 48, that is, the number of pixels in the entire detection line display area is 12*48=576. The image division rule of model A test paper stored in the storage module is: divide the test paper image into two parts along its height direction, and divide it into two areas with equal area and the same number of pixels, both of which are 288. The area relatively close to the control line area is defined as an invalid detection area, and the area relatively far from the control line area is defined as a valid detection area.

[0074] S4. Extract the grayscale values ​​of the pixels in the effective detection area and calculate a first grayscale value; the first grayscale value is the precise grayscale value of the effective detection area. This step specifically includes the following steps:

[0075] S41, extracting the grayscale values ​​of all pixels in the effective detection area to obtain an initial grayscale value set;

[0076] In this embodiment, it is first necessary to extract the grayscale values ​​of 288 pixels in the effective detection area. The extraction method is any extraction method in the existing technology. After the extraction, an initial grayscale value set is obtained, and the initial grayscale value set contains 288 elements.

[0077] S42, calculating the average value of the initial grayscale value set to obtain a first average grayscale value;

[0078] The average value of the 288 grayscale values ​​in the initial grayscale value set obtained in step S41 is calculated to obtain a first average grayscale value.

[0079] S43, setting a valid grayscale value range according to the first average grayscale value;

[0080] Considering that there may be a small number of pixels with relatively large grayscale value deviations within the divided effective detection area, it is necessary to set the effective grayscale value range. The specific setting method includes:

[0081] S431, setting an upper threshold; the upper threshold is a times the first average grayscale value;

[0082] S432, setting a lower threshold; the lower threshold is b times the first average grayscale value;

[0083] Among them, the value range of a is 1.05 <a<1.25;b的取值范围为0.75<b<0.95。

[0084] In this embodiment, the preferred value of a is 1.1, and the preferred value of b is 0.9, that is, the effective grayscale value range is 0.9 to 1.1 times the first average grayscale value.

[0085] S44, traversing the initial grayscale value set, screening grayscale values ​​within the valid grayscale value range, and obtaining a valid grayscale value set;

[0086] The 288 grayscale values ​​of the initial grayscale value set are traversed, and each grayscale value is compared with the upper threshold and the lower threshold respectively. The grayscale values ​​of the pixels within the range of the upper threshold and the lower threshold are screened out to obtain the valid grayscale value set.

[0087] S45. Calculate the average value of the valid grayscale value set to obtain the first grayscale value.

[0088] The average value of all elements in the effective gray value set is calculated to obtain a first gray value, which is the precise gray value of the effective detection area.

[0089] It should be noted that since the grayscale values ​​of each pixel point in the height direction of the detection line are relatively stable, in order to reduce the amount of calculation and improve calculation efficiency, when extracting the pixel points in the effective detection area, it is not necessary to extract the grayscale values ​​of all pixel points. When extracting partial pixel points, all pixel points must be taken in the width direction of the effective detection area, and pixel points can be randomly extracted in the length direction of the effective detection area.

[0090] In this embodiment, the effective detection area contains 288 pixels in 48 rows and 6 columns, and n rows and 6 columns of pixels can be randomly extracted, where the value range of n is preferably 3 to 6. For example, if n is 5, it is necessary to extract 30 pixels in 5 rows and 6 columns from the effective detection area, that is, the obtained initial grayscale value set contains a total of 30 elements.

[0091] S5. Obtain a second grayscale value according to the test paper parameter information; the second grayscale value is the precise grayscale value of the control line area;

[0092] The storage module of the test paper interpretation and analysis system stores the precise grayscale values ​​of the control line areas corresponding to test papers of different functions and models. These values ​​are obtained through multiple tests and inspections after the production of the test paper of this model, and are stored in the storage module.

[0093] S6. Compare the first grayscale value and the second grayscale value to obtain a detection result. This step specifically includes the following steps:

[0094] S61, determining a judgment curve according to the test paper parameter information;

[0095] The storage module of the test strip interpretation and analysis system stores interpretation curves corresponding to test strips of different functions and models. The variable value of the interpretation curve is the ratio of the first grayscale value to the second grayscale value (i.e., the T / C ratio), and the function value is the concentration of the test fluid. For early pregnancy test strips, the concentration of the test fluid is the concentration of human chorionic gonadotropin (HCG), and for ovulation test strips, the concentration of the test fluid is the concentration of luteinizing hormone (LH).

[0096] In this embodiment, the expression of the interpretation curve corresponding to the test paper is: y = 213.73x 3 -207.27x 2 +94.646x-7.8066, this expression is obtained through multiple sampling tests after the production of this model of test paper, where the variable value x corresponds to the first grayscale value, and the function value y corresponds to the second grayscale value. For intuitive display, the above judgment curve is expressed as shown in Table 1.

[0097] Table 1

[0098]

[0099] S62. Calculate the ratio of the first grayscale value to the second grayscale value to obtain a first ratio;

[0100] S63. Input the first ratio into the judgment curve to obtain the concentration of the test solution.

[0101] The calculation result of step S62 is input as a variable value into the judgment curve obtained in step S61, and the obtained function value is the concentration of the test liquid. In this embodiment, the obtained concentration is the concentration of human chorionic gonadotropin (HCG).

[0102] The existing interpretation method only outputs the detection results qualitatively. The interpretation method of the present application can output not only the qualitative detection results but also the quantitative detection results.

[0103] Regarding the qualitative test results, they are output according to the first ratio calculated in step S62. In this embodiment, when the first ratio is <15%, it indicates that the test result is weakly positive; when 15% ≤ the first ratio ≤ 250%, it indicates that the test result is positive; when the first ratio is >250%, it indicates that the test result is strongly positive.

[0104] Regarding the quantitative test results, it is output according to the test liquid concentration calculated in step S63, that is, the test liquid concentration will be displayed. For early pregnancy testing, while displaying the HCG concentration, it will also display the correspondence between the human HCG concentration and the number of weeks of pregnancy for the user's reference. Table 2 shows the correspondence between the human HCG concentration and the number of weeks of pregnancy. This correspondence is also stored in the storage module of the test paper interpretation and analysis system.

[0105] Table 2

[0106]

[0107] The test paper interpretation and analysis method provided in the embodiment of the present application divides the acquired test paper image detection line area into effective and invalid ones, and further analyzes and calculates within the effective detection area, thereby obtaining a more accurate first grayscale value representing the effective detection area. Then, by comparing and calculating the first grayscale value and the second grayscale value, a more accurate qualitative detection result can be obtained. In addition, by introducing the interpretation curve, a more accurate detection liquid concentration can be obtained, which provides users with more accurate quantitative detection results, greatly facilitating users.

[0108] The test strip interpretation and analysis system and method of this application can accurately perform the interpretation and analysis function of test strips, overcoming the problems of professional equipment that cannot be held handheld, requires high professional operation skills, and is expensive. In addition, the system and method can upload records to the Internet, allowing the detector to perform test strip testing not only in the laboratory, but also in more scenarios such as at home, and store and manage test results and information data in the cloud. This greatly reduces manpower, material and financial resources and expands the development of the home health testing market.

[0109] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. The above is only the preferred implementation method of this application. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can also make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. A test paper interpretation and analysis method, characterized in that: The method includes the following steps: Obtain test strip parameter information; Obtain an image of the test strip after reacting with the sample to be tested; the test strip image includes a control line area and a detection line area; Divide the detection line area into a valid detection area and an invalid detection area according to the test strip parameter information; Extract and calculate the gray values of the pixel points in the valid detection area to obtain a first gray value; the first gray value is the accurate gray value of the valid detection area; Obtain a second gray value according to the test strip parameter information; the second gray value is the accurate gray value of the control line area; Compare the first gray value and the second gray value to obtain a test result; Among them, the color distribution of the detection line area on test strips with different functions and different models is different. The image recognition and division module can obtain the corresponding image division rule from the storage module according to the test strip parameter information, and divide the detection line area into a valid detection area and an invalid detection area according to this rule; Extracting and calculating the gray values of the pixel points in the valid detection area to obtain a first gray value includes: Extract the gray values of all pixel points in the valid detection area to obtain an initial gray value set; Calculate the average value of the initial gray value set to obtain a first average gray value; Set a valid gray value range according to the first average gray value; Traverse the initial gray value set, filter the gray values within the valid gray value range, and obtain a valid gray value set; Calculate the average value of the valid gray value set to obtain the first gray value.

2. The test paper interpretation and analysis method according to claim 1, wherein The test strip parameter information includes product function information and product model information.

3. The test paper interpretation and analysis method according to claim 1, wherein The method for setting a valid gray value range according to the first average gray value includes: Set an upper threshold; the upper threshold is a times the first average gray value; Set a lower threshold; the lower threshold is b times the first average gray value; Among them, the value range of a is 1.05 < a < 1.25; the value range of b is 0.75 < b < 0.

95.

4. The test paper interpretation and analysis method according to claim 1, wherein Comparing the first gray value and the second gray value to obtain a test result includes: Determine an interpretation curve according to the test strip parameter information; Calculate the ratio of the first gray value and the second gray value to obtain a first ratio; Input the first ratio into the interpretation curve to obtain the concentration of the test liquid.

5. A test paper interpretation and analysis system, characterized in that: Includes: A scanning module configured to obtain test strip parameter information; An image acquisition module configured to obtain an image of the test strip after reacting with the sample to be tested; the test strip image includes a control line area and a detection line area; An image recognition and division module configured to obtain the corresponding image division rule according to the test strip parameter information, and divide the detection line area into a valid detection area and an invalid detection area according to the image division rule; An extraction and calculation module configured to extract and calculate the gray values of the pixel points in the valid detection area to obtain a first gray value; and obtain a second gray value according to the test strip parameter information; the first gray value is the accurate gray value of the valid detection area; the second gray value is the accurate gray value of the control line area; a comparison and calculation module, configured to compare and calculate the first grayscale value and the second grayscale value to obtain a detection result; an output module configured to output the detection result; Among them, the color distribution of the detection line area on the test strips of different functions and different models is different. The image recognition and division module can obtain the corresponding image division rules from the storage module according to the test strip parameter information, and divide the detection line area into a valid detection area and an invalid detection area according to the rule; The extraction and calculation module includes an extraction submodule, a first calculation submodule, a setting submodule and a screening submodule; The extraction submodule is configured to extract the grayscale values ​​of all pixels in the effective detection area to obtain an initial grayscale value set; The first calculation submodule is configured to calculate an average value of the initial grayscale value set to obtain a first average grayscale value; The setting submodule is configured to set a valid grayscale value range according to the first average grayscale value; The screening submodule is configured to screen the grayscale values ​​within the valid grayscale value range from the initial grayscale value set to obtain a valid grayscale value set; The first calculation submodule is further configured to calculate an average value of the valid grayscale value set to obtain the first grayscale value.

6. The test paper interpretation and analysis system according to claim 5, characterized in that: It also includes a storage module; the storage module is used to store image division rules and interpretation curves corresponding to test papers of different functions and models.

7. The test paper interpretation and analysis system according to claim 5, characterized in that: The setting submodule includes an upper limit setting unit and a lower limit setting unit; The upper limit setting unit is configured to set an upper limit threshold of the valid grayscale value range; The lower limit setting unit is configured to set a lower limit threshold of the valid grayscale value range.

8. The test paper interpretation and analysis system according to claim 6, characterized in that: The comparison and calculation module includes an acquisition submodule and a second calculation submodule; The acquisition submodule is configured to acquire a corresponding interpretation curve according to the test paper parameter information; The second calculation submodule is configured to calculate the ratio of the first grayscale value to the second grayscale value to obtain a first ratio, and is further configured to input the first ratio into the interpretation curve to obtain the concentration of the detection liquid.

Images