Automatic auxiliary inspection method and device

Abstract

The invention relates to the technical field of inspection medical treatment, in particular to an automatic auxiliary inspection method and device. After counting information of visible components contained in a preset number of shot images is obtained, a frequency value of each counting category is obtained, and the state or tracking performance of the urine detection equipment is determined based on the frequency value of each counting category and a preset model. Therefore, the user can analyze the urine sample when the state or tracking performance of the urine detection equipment is normal, and the accuracy of the analysis result is improved. In addition, according to the urine detection method, the tracking performance of the urine detection equipment can be determined through statistical laws, a user does not need to carry out other additional operations, the operation process is simplified, and time is saved.

Description

Technical Field

[0001] The present invention relates to the field of medical testing technology, and in particular to an automated auxiliary testing method and device. Background Art

[0002] Body fluid detection devices, especially urine detection equipment (such as urine formed element analyzers), can analyze formed elements (collectively referred to as particles) in urine samples by microscopic tracking. For example, the image obtained after photographing the urine sample through a microscope is tracked and analyzed. However, when photographing and tracking through a microscope, if the tracking performance of the urine detection equipment fails, it may cause the image obtained by the photographing to be abnormal (such as the photographed image has a high probability of overlapping), thereby affecting the accuracy of the subsequent analysis results of the urine sample. Summary of the Invention

[0003] In order to solve the above technical problems, the embodiments of the present application provide an automated auxiliary inspection method and device, which can obtain the working status of the detection device in real time, and determine whether the current detection device is operating well based on the working status, thereby reducing the problem of inaccurate detection results caused by failure of the detection device.

[0004] In order to achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0005] In a first aspect, an automated assisted inspection method is provided, the method comprising: controlling an image acquisition device to photograph samples located in a counting pool to obtain a preset number of photographed images corresponding to the samples; obtaining counting information of tangible components contained in the preset number of photographed images; counting the number of photographed images contained in each counting category to obtain a frequency value of each counting category, wherein different counting information corresponds to different counting categories; and determining the status of the detection device based on the frequency value of each counting category.

[0006] Furthermore, determining the state of the detection device based on the frequency value of each counting category includes: determining whether the frequency value of each counting category conforms to a preset model; when the frequency value of each counting category conforms to the preset model, determining that the state of the detection device is normal; when the frequency value of each counting category does not conform to the preset model, determining that the state of the detection device is abnormal.

[0007] Furthermore, determining whether the frequency value of each counting category conforms to the preset model includes: obtaining the expected value corresponding to each counting category in the preset model; and determining whether the frequency value of each counting category conforms to the preset model based on the degree of deviation between the frequency value of each counting category and the corresponding expected value.

[0008] Furthermore, the preset value of each counting category is the theoretical value of the corresponding counting category in the Poisson distribution model, and the determination of whether the frequency value of each counting category conforms to the preset model based on the degree of deviation between the frequency value of each counting category and the corresponding expected value includes: judging whether the frequency value of each counting category and the corresponding expected value pass the chi-square test; when the chi-square test is passed, determining that the tracking performance of the urine detection device is normal; when the chi-square test is not passed, determining that the tracking performance of the urine detection device is abnormal.

[0009] Furthermore, the counting pool includes multiple shooting positions, and obtaining a preset number of shooting images corresponding to the sample includes: selectively sampling and shooting some shooting positions of the counting pool through the image acquisition device to obtain the preset number of shooting images.

[0010] Furthermore, the method further includes: controlling the output of prompt information corresponding to the status of the urine testing device.

[0011] Furthermore, the method also includes: performing a first evaluation operation on the tracking performance of the detection device based on the captured images obtained by the first type of lens; judging whether the tracking performance of the detection device under the first evaluation operation is normal; if the tracking performance of the detection device under the first evaluation operation is normal, performing a second evaluation operation on the tracking performance of the detection device based on the captured images obtained by the second type of lens; judging whether the tracking performance of the detection device under the second evaluation operation is normal; if the tracking performance of the detection device under both the first evaluation operation and the second evaluation operation is normal, determining that the tracking performance of the detection device is normal; if the tracking performance of the detection device under at least one of the first evaluation operation and the second evaluation operation is abnormal, determining that the tracking performance of the detection device is abnormal.

[0012] Furthermore, the first evaluation operation of the tracking performance of the detection device based on the captured images obtained by the first type of lens includes: photographing the samples located in the counting pool through the first type of lens to obtain a first preset number of first captured images corresponding to the samples; obtaining counting information of the tangible components contained in each first captured image; counting the number of first captured images contained in each counting category to obtain the frequency value of each counting category, wherein different counting information corresponds to different counting categories; and determining the state of the detection device under the shooting of the first type of lens based on the frequency value of each counting category corresponding to the first captured image and a first preset model.

[0013] Furthermore, the determining whether the tracking performance of the detection device under the first evaluation operation is normal includes: obtaining an expected value of each counting category corresponding to the first captured image in the first preset model; determining the state of the urine detection device under the first type of lens capture based on the degree of deviation between the frequency value of each counting category corresponding to the first captured image and the corresponding expected value;

[0014] In a second aspect, an automated auxiliary inspection device is provided, which includes: an image acquisition module, used to control the image acquisition device to photograph samples located in a counting pool, and obtain a preset number of photographed images corresponding to the samples; an information acquisition module, used to obtain counting information of tangible components contained in the preset number of photographed images; a statistical module, used to count the number of photographed images contained in each counting category, and obtain frequency values of each counting category, wherein different counting information corresponds to different counting categories; and a state determination module, which determines the state of the detection device based on the frequency values of each counting category.

[0015] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the automated assisted inspection method described in any one of the above items is implemented.

[0016] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the automated assisted inspection method described in any one of the above items is implemented.

[0017] The technical solution provided in the embodiments of the present application obtains count information of visible components contained in a preset number of captured images, then obtains frequency values for each count category. Based on the frequency values for each count category and a preset model, the status or tracking performance of the urine testing device is determined. This allows the user to perform urine sample analysis when the status or tracking performance of the urine testing device is normal, thereby improving the accuracy of the analysis results. Furthermore, the urine testing method can determine the tracking performance of the urine testing device based on statistical laws, eliminating the need for the user to perform additional operations, simplifying the operation process and saving time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] The methods, systems, and / or programs in the accompanying drawings will be further described according to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example numerals represent similar structures in the various views of the drawings.

[0020] Figure 1 This is a flow chart of an automated auxiliary inspection method provided in an embodiment of the present application.

[0021] Figure 2 This is a flow chart of another automated assisted inspection method provided in an embodiment of the present application.

[0022] Figure 3 This is a schematic diagram of the structure of the automated auxiliary inspection device provided in an embodiment of the present application.

[0023] Figure 4 This is a schematic diagram of the structure of the automated auxiliary inspection equipment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to better understand the above technical solution, the technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0025] In the following detailed description, numerous specific details are set forth by way of example in order to provide a thorough understanding of the relevant teachings. However, it will be apparent to one skilled in the art that the present application can be practiced without these details. In other instances, well-known methods, procedures, systems, compositions, and / or circuits have been described at a relatively high level, without detail, to avoid unnecessarily obscuring aspects of the present application.

[0026] Flowcharts are used in this application to illustrate the execution processes performed by the system according to the embodiments of the present application. It should be clearly understood that the execution processes of the flowcharts may not be executed in sequence. Instead, these execution processes may be executed in reverse order or simultaneously. In addition, at least one additional execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0027] Before further explaining the embodiments of the present invention in detail, the nouns and terms involved in the embodiments of the present invention are explained. The nouns and terms involved in the embodiments of the present invention are subject to the following interpretations.

[0028] (1) In response to, it is used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.

[0029] (2) Based on, used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.

[0030] See Figure 1 This embodiment provides an automated auxiliary inspection method, and the processing method specifically includes the following steps:

[0031] Step S110 : Obtaining counting information of tangible components contained in the preset number of captured images.

[0032] Among the preset number of captured images, there may be some captured images that do not contain any tangible components, there may be some captured images that contain a small number of tangible components, and there may be some captured images that contain a large number of tangible components. If the tracking characteristics of the urine detection device are normal (or the state is normal), the distribution of the number of tangible components in the preset number of captured images obtained may have certain distribution characteristics. For example, when the number of particles of the tangible components is small (such as negative samples), the distribution of the number of tangible components in the captured images obtained conforms to the Poisson distribution; when the number of particles of the tangible components is moderate, the distribution of the number of tangible components in the captured images obtained can be regarded as a normal distribution; when the number of particles of the tangible components is high, the distribution of the number of tangible components in the captured images obtained can approach the mean distribution. Therefore, in order to determine whether the tracking characteristics of the urine detection device are normal, the processor can determine whether the distribution of the number of tangible components in the preset number of captured images conforms to the preset distribution characteristics based on the distribution of the number of tangible components in the preset number of captured images. If it meets the above distribution characteristics (including but not limited to Poisson distribution, normal distribution, mean distribution, etc.), it can be determined that the tracking characteristics of the urine testing device are normal, and thus the accuracy of the analysis results of the urine sample through the urine testing device 50 is guaranteed to a certain extent.

[0033] In this embodiment, the processor may obtain counting information kn of the tangible components contained in each captured image, where n=0, . . . , X, and X is the preset number.

[0034] For example, the processor may perform image recognition on each of the preset number of captured images to obtain count information (or the number) of tangible components in each captured image, wherein image recognition may be based on relevant image recognition, image segmentation, and other algorithms to achieve statistics on the count information of tangible components. For example, the preset number of captured images may include captured image A, captured image B, captured image C, captured image D, etc. (in other embodiments, the preset number of captured images may also include other captured images). When the processor performs image recognition, it obtains that the number of tangible components contained in captured image A is , the number of tangible components contained in captured image B is , the number of tangible components contained in captured image C is , and the number of tangible components contained in captured image D is , which means that the count information of captured image A is , the count information of captured image B is , the count information of captured image C is 30, and the count information of captured image D is . In other embodiments, the preset number of captured images may also include other numbers of tangible components, including but not limited to captured images containing a number of tangible components or captured images containing a number of tangible components.

[0035] Step 120: Count the number of captured images included in each counting category to obtain a frequency value of each counting category, wherein different counting information corresponds to different counting categories.

[0036] When determining the distribution characteristics of the shaped components in the preset number of captured images, the processor may obtain a frequency value f(m) of the captured images included in each counting category, where different counting information corresponds to different counting categories. The calculation formula of the frequency value may be expressed as:

[0037] Where m represents the counting category or the number of shaped elements in a captured image, and m∈[0,max(kn)]∩N, where N is a natural number, max(kn) represents the number of shaped elements with the largest number, and f(m) is the sum of the number of captured images with the same counting information m in a preset number X.

[0038] For example, the counting category of the aforementioned counting information includes the captured image A and the captured image C, and the frequency value of the counting information is 2, which can also be expressed as f(30)=2; the counting category of the aforementioned counting information includes the captured image B, and the frequency value of the counting information 20 is 1, which can also be expressed as f(20)=1; the counting category of the aforementioned counting information includes the captured image D, and the frequency value of the counting information 10 is 1, which can also be expressed as f(10)=1.

[0039] Step S130 : Determine the status of the urine testing device based on the frequency value of each counting category.

[0040] The processor can determine whether the frequency value of each counting category conforms to a preset model, wherein the preset model can be a Poisson distribution model, a normal distribution model, a mean distribution model, etc., which can characterize the distribution characteristics of the tangible components in the captured image when the tracking performance of the urine detection equipment is normal.

[0041] In this embodiment, the processor can determine whether the frequency value of each count category conforms to the Poisson distribution model. When the distribution characteristics of the tangible components conform to the Poisson distribution model, the processor can determine that the urine testing device is in a normal state; when the distribution characteristics of the tangible components do not conform to the Poisson distribution model, the processor can determine that the urine testing device is in an abnormal state.

[0042] In this embodiment, the processor can determine whether the frequency value of each counting category conforms to the Poisson distribution model based on the chi-square test. The processor can obtain the expected value g(m) of the Poisson distribution model corresponding to each counting category, where the expected value g(m) of the Poisson distribution model corresponding to each counting category can be expressed as:

[0043] where μ is expressed based on the following formula:

[0044]

[0045] The processor can determine the degree of deviation between the frequency value of each counting category and the corresponding expected value through the chi-square test to determine whether the frequency value of each counting category conforms to the preset model, wherein the degree of deviation between the frequency value of each counting category and the corresponding expected value can be expressed as the chi-square value χ 2 , where the chi-square value χ 2 It can be expressed as:

[0046]

[0047] The processor 500 also obtains a chi-square critical value corresponding to the calculated chi-square value χ2 from the chi-square table and determines the tracking performance of the urine testing device based on the chi-square value χ2 and the chi-square critical value:

[0048] If the chi-square test fails, the processor may determine that the tracking performance of the urine testing device is abnormal, and the processor may also output a prompt message indicating that the tracking performance of the urine testing device is abnormal;

[0049] If the chi-square test passes, the processor can determine that the tracking performance of the urine detection device is normal. The processor can also output a prompt message that the tracking performance of the urine detection device is normal, and the user can then perform urine sample detection or analysis based on the urine detection device.

[0050] In other embodiments, the processor may also determine the degree of deviation between the frequency value of each counting category and the corresponding expected value based on the P-value ratio method of the chi-square test.

[0051] In other embodiments, the processor may also use other testing methods to determine whether the distribution characteristics of the tangible components conform to the preset model, including but not limited to the KS test. The specific KS test can refer to the relevant testing methods.

[0052] In other embodiments, the preset model may also select a corresponding preset model based on the particles of the formed components in the urine sample. For example, the processor may count the total number of particles of the formed components in each captured image in a preset number, and determine the preset conditions satisfied by the total number of particles to select a Poisson distribution model, a normal distribution model, or a mean distribution model. For example, if the total number of particles is within a first preset range, the processor may determine the tracking performance of the urine detection device based on a Poisson distribution model (such as the first preset model); if the total number of particles is within a second preset range, the processor may determine the tracking performance of the urine detection device based on a normal distribution model (such as the second preset model); if the total number of particles is within a third preset range, the processor may determine the tracking performance of the urine detection device based on a mean distribution model (such as the third preset model).

[0053] In other embodiments, since the formed elements include red blood cells, white blood cells, bacteria, yeasts, crystals, casts and epithelial cells, the processor can also obtain the counting information of a type of formed elements (such as white blood cells) contained in a preset number of captured images, and count the number of captured images contained in each counting category to obtain the frequency value of each counting category, wherein different counting information corresponds to different counting categories of the formed elements of this type; the processor 500 can also determine the status of the urine detection device based on the frequency values of each counting category corresponding to the formed elements of this type. The specific determination process is similar to the above, so it is not repeated here.

[0054] After obtaining count information of visible components contained in a preset number of captured images, the above-described urine testing method obtains frequency values for each count category. Based on these frequency values and a preset model, the urine testing device's status or tracking performance is determined. This allows the user to analyze urine samples when the device's status or tracking performance is normal, thereby improving the accuracy of the analysis results. Furthermore, the urine testing method uses statistical principles to determine the device's tracking performance, eliminating the need for the user to perform additional operations, simplifying the process and saving time.

[0055] See also Figure 2 , shown is a flow chart of the steps of a urine detection method in another embodiment of the present application. The urine detection method includes the following steps:

[0056] Step S210 : Perform a first evaluation operation on the tracking performance of the urine testing device based on the captured image obtained by the first type of lens.

[0057] In this embodiment, a general urine testing device will analyze the urine sample through different types of lenses. Therefore, the processor can determine the tracking performance of the urine testing device based on the captured images obtained under each type of lens. When the tracking performance of the urine testing device determined by the captured images obtained under each type of lens is normal, it indicates that the user can perform urine testing and analysis through the urine testing device, and the accuracy of the obtained analysis results is guaranteed to a certain extent. When the tracking performance of the urine testing device determined by the captured images obtained under at least one type of lens is abnormal, a prompt message indicating that the tracking performance of the urine testing device is abnormal can be output.

[0058] In this embodiment, the first type of lens may be a low-magnification lens, and the image acquisition device may capture and process the urine sample using the low-magnification lens to obtain a first preset number of first captured images. In one embodiment, the processor selectively uses the first type of lens to sample and capture a portion of the imaging positions in the counting cell using the image acquisition device to obtain the first preset number of first captured images.

[0059] Step S220 , determining whether the tracking performance of the urine testing device under the first evaluation operation is normal; if normal, executing step S240 ; if abnormal, executing step S230 .

[0060] The processor can obtain the counting information of the tangible components contained in each first captured image, and count the number of first captured images contained in each counting category to obtain the frequency value of each counting category, wherein different counting information corresponds to different counting categories. The processor can also determine the evaluation result of the first evaluation operation based on the frequency value of each counting category corresponding to the first captured image and the first preset model, such as determining whether the status of the urine detection device under the first type of lens shooting is normal or abnormal. For example, the processor can obtain the counting information of the tangible components contained in each first captured image, such as the processor can obtain the number or counting information of the tangible components in each first captured image based on image recognition. The processor also determines whether the frequency value of each counting category of the first captured image conforms to the first preset model. If it conforms to the second preset model (such as the Poisson distribution model), it indicates that the tracking performance of the urine detection device under the first evaluation operation is normal. Otherwise, it does not conform to the preset model (such as the Poisson distribution model), which indicates that the tracking performance of the urine detection device under the first evaluation operation is abnormal.

[0061] In this embodiment, the processor can obtain the expected value of each counting category corresponding to the first captured image in the first preset model, and determine the state of the urine detection device under the first type of lens capture based on the degree of deviation between the frequency value of each counting category corresponding to the first captured image and the corresponding expected value, wherein the expected value of a counting category is the theoretical value of the corresponding counting category in the Poisson distribution model. The processor determines the degree of deviation between the frequency value of each counting category and the corresponding expected value through a chi-square test. For example, the processor determines whether the frequency value of each counting category corresponding to the first captured image and the corresponding expected value pass the chi-square test; when the chi-square test is passed, the processor determines that the tracking performance of the urine detection device under the first evaluation operation is normal; when the chi-square test is not passed, the processor determines that the tracking performance of the urine detection device under the first evaluation operation is abnormal. The specific inspection process is similar to the above, so it will not be repeated here.

[0062] If it is determined that the tracking performance of the urine testing device under the first evaluation operation is normal, the processor may execute step S240 to evaluate the tracking performance of the urine testing device based on the second type of lens.

[0063] Step S230: outputting a prompt message indicating that the tracking characteristic of the urine testing device is abnormal.

[0064] In this embodiment, if it is determined that the tracking performance of the urine testing device under the first evaluation operation is abnormal, the processor may output a prompt message indicating that the tracking performance of the testing device is abnormal, so that the user can promptly know the status or tracking performance of the urine testing device.

[0065] Step S240 performs a second evaluation operation on the tracking performance of the urine testing device based on the captured image obtained by the second type of lens.

[0066] In this embodiment, when it is determined that the status of the urine testing device captured by the first type of lens is normal, the processor captures the urine sample in the counting pool through the second type of lens to obtain a second preset number of second captured images corresponding to the urine sample.

[0067] In this embodiment, the second type of lens may be a high-power lens, and the image acquisition device may capture and process the urine sample using the high-power lens to obtain a second preset number of second captured images. In one embodiment, the processor selectively uses the second type of lens to sample and capture a portion of the imaging positions in the counting cell using the image acquisition device to obtain the second preset number of second captured images.

[0068] Step S250 , determining whether the tracking performance of the urine testing device under the second type of lens is normal; if normal, executing step S260 ; if abnormal, executing step S230 .

[0069] The processor can obtain the counting information of the tangible components contained in each second captured image, and count the number of second captured images contained in each counting category to obtain the frequency value of each counting category, wherein different counting information corresponds to different counting categories. The processor can also determine the evaluation result of the second evaluation operation based on the frequency value of each counting category corresponding to the second captured image and the second preset model, such as determining whether the status of the urine detection device under the second type of lens shooting is normal or abnormal. For example, the processor can obtain the counting information of the tangible components contained in each second captured image, such as the processor can obtain the number or counting information of the tangible components in each second captured image based on image recognition. The processor also determines whether the frequency value of each counting category of the second captured image conforms to the second preset model. If it conforms to the second preset model (such as the Poisson distribution model), it indicates that the tracking performance of the urine detection device under the second evaluation operation is normal. Otherwise, it does not conform to the preset model (such as the Poisson distribution model), which indicates that the tracking performance of the urine detection device under the second evaluation operation is abnormal.

[0070] In this embodiment, the processor can obtain the expected value of each counting category corresponding to the second captured image in the second preset model, and determine the state of the urine detection device under the second type of lens shooting based on the degree of deviation between the frequency value of each counting category corresponding to the second captured image and the corresponding expected value, wherein the expected value of a counting category is the theoretical value of the corresponding counting category in the Poisson distribution model. The processor determines the degree of deviation between the frequency value of each counting category and the corresponding expected value through a chi-square test. For example, the processor determines whether the frequency value of each counting category corresponding to the second captured image and the corresponding expected value pass the chi-square test; when the chi-square test is passed, the processor determines that the tracking performance of the urine detection device under the second evaluation operation is normal; when the chi-square test is not passed, the processor determines that the tracking performance of the urine detection device under the second evaluation operation is abnormal. The specific inspection process is similar to the above, so it will not be repeated here.

[0071] If it is determined that the tracking performance of the urine testing device under the second evaluation operation is normal, the processor may execute step S260 to evaluate the tracking performance of the urine testing device based on the second type of lens.

[0072] In this embodiment, since the tracking performance of the urine testing device under the second evaluation operation is determined after the tracking performance of the urine testing device under the first evaluation operation is normal, if it is determined that the tracking performance of the urine testing device under the second evaluation operation is abnormal, the processor can output a prompt information that the tracking performance of the urine testing device is abnormal.

[0073] Step S260: Outputting a prompt message indicating that the tracking characteristic of the urine testing device is normal.

[0074] In this embodiment, since the tracking performance of the urine testing device under the second evaluation operation is determined after the tracking performance of the urine testing device under the first evaluation operation is normal, if it is determined that the tracking performance of the urine testing device under the second evaluation operation is also normal, the processor can determine that the status of the urine testing device is normal or the tracking performance is normal.

[0075] See Figure 3 In an embodiment of the present application, there is also provided an automated auxiliary inspection device 300, characterized in that the device comprises:

[0076] The image acquisition module 310 is used to control the image acquisition device to capture the sample in the counting cell to obtain a preset number of captured images corresponding to the sample;

[0077] The information acquisition module 320 is used to obtain the counting information of the tangible components contained in the preset number of captured images;

[0078] A statistics module 330 is configured to count the number of captured images included in each counting category to obtain a frequency value of each counting category, wherein different counting information corresponds to different counting categories;

[0079] The state determination module 340 determines the state of the detection device based on the frequency value of each counting category.

[0080] See Figure 4 , the above method can also be integrated into the provided automated auxiliary inspection device 400. In view of the fact that the device may have relatively large differences due to different configurations or performances, it can include one or more processors 401 and memory 402, and the memory 402 can store one or more storage applications or data. Among them, the memory 402 can be a temporary storage or a persistent storage. The application stored in the memory 402 may include one or more modules (not shown in the figure), and each module may include a series of computer executable instructions in the electronic device. Furthermore, the processor 401 can be configured to communicate with the memory 402, and the electronic device executes a series of computer executable instructions in the memory 402. The electronic device may also include one or more power supplies 403, one or more wired / wireless network interfaces 404, one or more input / output interfaces 405, one or more keyboards 406, etc.

[0081] In a specific embodiment, the electronic device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the nursing monitoring device, and the one or more programs are configured to be executed by one or more processors, including computer-executable instructions for performing the following:

[0082] Obtaining counting information of tangible components contained in the preset number of captured images;

[0083] Counting the number of captured images included in each counting category to obtain a frequency value of each counting category, wherein different counting information corresponds to different counting categories;

[0084] The status of the urine testing device is determined based on the frequency value of each count category.

[0085] The following is a detailed introduction to the various components of the processor:

[0086] In this embodiment, the processor is an application specific integrated circuit (ASIC), or is configured to implement one or more integrated circuits of the embodiments of the present application, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs).

[0087] Optionally, the processor can execute various functions by running or executing the software program stored in the memory and calling the data stored in the memory, such as executing the above Figure 2 The method shown.

[0088] In a specific implementation, as an embodiment, the processor may include one or more microprocessors.

[0089] The memory is used to store the software program for executing the solution of the present application, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0090] Alternatively, the memory may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and be coupled to the processing unit through the interface circuit of the processor, and the embodiments of the present application do not specifically limit this.

[0091] It should be noted that the structure of the processor shown in this embodiment does not constitute a limitation on the device. The actual device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0092] In addition, the technical effects of the processor can refer to the technical effects of the method described in the above method embodiment, and will not be repeated here.

[0093] It should be understood that the processor in the embodiments of the present application may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0094] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0095] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (such as infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0096] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0097] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0098] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0099] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0100] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0101] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0102] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0103] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0104] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An automated auxiliary inspection method, characterized in that: The method comprises: Controlling the image acquisition device to photograph the sample in the counting pool to obtain a preset number of photographed images corresponding to the sample; Obtaining counting information of tangible components contained in the preset number of captured images; Counting the number of captured images included in each counting category to obtain a frequency value of each counting category, wherein different counting information corresponds to different counting categories; The status of the detection device is determined based on the frequency value of each counting category.

2. The automated assisted inspection method according to claim 1, characterized in that: The determining of the state of the detection device based on the frequency value of each counting category includes: determining whether the frequency value of each counting category conforms to a preset model; when the frequency value of each counting category conforms to the preset model, determining that the state of the detection device is normal; when the frequency value of each counting category does not conform to the preset model, determining that the state of the detection device is abnormal.

3. The automated assisted inspection method according to claim 2, characterized in that: Determining whether the frequency value of each counting category conforms to the preset model includes: obtaining the expected value corresponding to each counting category in the preset model; and determining whether the frequency value of each counting category conforms to the preset model based on the degree of deviation between the frequency value of each counting category and the corresponding expected value.

4. The automated assisted inspection method according to claim 3, characterized in that: The preset value of each counting category is the theoretical value of the corresponding counting category in the Poisson distribution model. The method of determining whether the frequency value of each counting category conforms to the preset model based on the degree of deviation between the frequency value of each counting category and the corresponding expected value includes: judging whether the frequency value of each counting category and the corresponding expected value pass the chi-square test; when the chi-square test is passed, determining that the tracking performance of the urine detection device is normal; when the chi-square test is not passed, determining that the tracking performance of the urine detection device is abnormal.

5. The automated assisted inspection method according to claim 1, characterized in that: The counting pool includes multiple shooting positions, and obtaining a preset number of shooting images corresponding to the sample includes: selectively sampling and shooting some shooting positions of the counting pool by the image acquisition device to obtain the preset number of shooting images.

6. The automated assisted inspection method according to any one of claims 1 to 5, characterized in that: The method further includes controlling output of prompt information corresponding to the status of the urine testing device.

7. The automated assisted inspection method according to claim 1, characterized in that: The method also includes: performing a first evaluation operation on the tracking performance of the detection device based on the captured images obtained by the first type of lens; judging whether the tracking performance of the detection device under the first evaluation operation is normal; if the tracking performance of the detection device under the first evaluation operation is normal, performing a second evaluation operation on the tracking performance of the detection device based on the captured images obtained by the second type of lens; judging whether the tracking performance of the detection device under the second evaluation operation is normal; if the tracking performance of the detection device under both the first evaluation operation and the second evaluation operation is normal, determining that the tracking performance of the detection device is normal; if the tracking performance of the detection device under at least one of the first evaluation operation and the second evaluation operation is abnormal, determining that the tracking performance of the detection device is abnormal.

8. The automated assisted inspection method according to claim 7, characterized in that: The first evaluation operation of the tracking performance of the detection device based on the captured images obtained by the first type of lens includes: capturing samples located in the counting pool through the first type of lens to obtain a first preset number of first captured images corresponding to the samples; obtaining counting information of tangible components contained in each first captured image; counting the number of first captured images contained in each counting category to obtain frequency values of each counting category, wherein different counting information corresponds to different counting categories; and determining the state of the detection device under the capture of the first type of lens based on the frequency values of each counting category corresponding to the first captured image and a first preset model.

9. The automated assisted inspection method according to claim 8, characterized in that: Determining whether the tracking performance of the detection device under the first evaluation operation is normal includes: obtaining an expected value of each counting category corresponding to the first captured image in the first preset model; and determining the state of the urine detection device under the first type of lens capture based on the degree of deviation between the frequency value of each counting category corresponding to the first captured image and the corresponding expected value.

10. An automated auxiliary inspection device, characterized in that: The device comprises: An image acquisition module, configured to control the image acquisition device to capture the sample in the counting pool and obtain a preset number of captured images corresponding to the sample; An information acquisition module, configured to acquire counting information of tangible components contained in the preset number of captured images; A statistics module, configured to count the number of captured images included in each counting category to obtain a frequency value of each counting category, wherein different counting information corresponds to different counting categories; The state determination module determines the state of the detection device based on the frequency value of each counting category.

Images