Protein detection method, device, electronic device, and storage medium

Abstract

This invention provides a protein detection method, apparatus, electronic device, and storage medium. The method includes: identifying the graphic codes of multiple samples to be tested and determining the dairy product category of each sample from the identification information; determining scheduling information for testing each sample based on historical detection data of its dairy product category; identifying the position information of each dairy product sample based on a sample stage image; and, based on the scheduling information and position information, sequentially retrieving samples from the sample stage and feeding them into a nitrogen analyzer to perform protein detection on the samples in the sample stage. This method of sequentially retrieving samples from the sample stage and feeding them into the nitrogen analyzer based on the scheduling and position information achieves a highly automated detection process, improving the accuracy and reliability of the detection.

Description

Technical Field

[0001] This invention relates to the field of biochemistry, and in particular to a protein detection method, apparatus, electronic device, and storage medium. Background Technology

[0002] Existing methods for detecting proteins in dairy products rely on manual detection by testing personnel.

[0003] Existing methods rely entirely on human personnel to control the protein detection process. However, due to limited human resources, constrained ability to interpret results, and numerous testing items, protein detection efficiency decreases under multiple testing tasks, making it impossible to complete an efficient and accurate protein detection process. Summary of the Invention

[0004] This invention provides a protein detection method, apparatus, electronic device, and storage medium to achieve a highly efficient and accurate protein detection process.

[0005] This invention provides a protein detection method, comprising the following steps: Acquire an image of a sample stage on which multiple samples to be tested are placed, the multiple samples to be tested including samples of various different dairy product categories; Based on the sample stage image, the graphic codes of the multiple samples to be tested are identified, and the dairy product category of each sample to be tested is determined from the identification information. Based on the historical testing data of the dairy product category of each sample to be tested, the scheduling information for testing each sample to be tested is determined; Based on the sample stage image, the position information of each dairy product sample to be tested is identified, and the position information of each dairy product sample to be tested is determined. Based on the scheduling information and the location information, samples to be tested are obtained one by one from the sample stage and sent to the nitrogen analyzer to perform protein detection on the samples to be tested in the sample stage.

[0006] According to a protein detection method provided by the present invention, determining scheduling information for testing each sample based on historical testing data of the dairy product category of each sample to be tested includes: Based on the historical testing data of the dairy product categories of each sample to be tested, the testing time and testing resource requirements for each dairy product category of the sample to be tested are determined. Based on the detection duration and detection resource requirements, the priority of detecting each sample to be tested is determined, and the priority of detecting each sample to be tested is used as the scheduling information.

[0007] According to a protein detection method provided by the present invention, after performing the protein detection process on the sample to be detected in the sample stage, the method further includes: Once it is determined that the multiple samples to be tested have been tested and the test results have been generated, the environmental information of the sample stage is obtained based on the sensor network arranged in the sample stage. The environmental information includes at least temperature information, humidity information and airflow information. Based on the environmental information, correction information for each dairy product category is determined, and the test results are corrected based on the correction information.

[0008] According to a protein detection method provided by the present invention, after acquiring an image of a sample stage on which multiple samples to be detected are placed, the method further includes: Based on the sample stage image, each sample to be tested is identified, and abnormal samples with abnormal sample layering are determined from the plurality of samples to be tested. The abnormal sample was deleted.

[0009] According to a protein detection method provided by the present invention, the step of identifying each sample to be detected based on the sample stage image, and determining abnormal samples with abnormal sample stratification from the plurality of samples to be detected, includes: The sample stage image is converted from RGB color space to HSV color space, and the color mask of each sample to be detected in the sample stage image is analyzed to determine the target sample containing at least two color masks. The target sample is considered an abnormal sample due to sample stratification anomaly.

[0010] According to a protein detection method provided by the present invention, deleting the abnormal sample includes: The location information of the abnormal sample is determined, and based on the determined location information of the abnormal sample, the robotic arm is controlled to grab the abnormal sample and discard it.

[0011] The present invention also provides a protein detection device, comprising the following modules: The image acquisition module is used to acquire images of a sample stage on which multiple samples to be tested are placed, the multiple samples to be tested including samples of various different dairy product categories; The identification module is used to identify the graphic codes of the plurality of samples to be tested based on the sample stage image, and to determine the dairy product category of each sample to be tested from the identification information. The scheduling information determination module is used to determine the scheduling information for testing each sample based on the historical testing data of the dairy product category of each sample to be tested. The location information determination module is used to identify the location information of each dairy product sample to be tested based on the sample stage image, and to determine the location information of each dairy product sample to be tested. The detection module is used to acquire samples to be tested one by one from the sample stage based on the scheduling information and the location information and send them to the nitrogen analyzer to perform the protein detection process on the samples to be tested in the sample stage.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the program to implement the protein detection method as described above.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the protein detection method as described above.

[0014] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the protein detection method as described above.

[0015] The protein detection method, apparatus, electronic device, and storage medium provided by this invention determine the scheduling information for testing each sample by using historical detection data of the dairy product category of each sample to be tested, and determine the position information of each dairy product sample to be tested based on the sample stage image. Based on the scheduling information and position information, the samples to be tested are retrieved one by one from the sample stage and sent to the nitrogen analyzer for protein detection, realizing a highly automated and intelligent detection process. This automated process not only improves the accuracy and reliability of the detection, but also significantly enhances the detection efficiency and flexibility. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of the protein detection method provided by the present invention.

[0018] Figure 2 This is a schematic diagram of the detection process provided by the present invention.

[0019] Figure 3 This is a schematic diagram of the protein detection device provided by the present invention.

[0020] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] Figure 1 This is a schematic flowchart of the protein detection method provided by the present invention, as shown below. Figure 1 As shown, the method includes the following: Step 110: Obtain an image of the sample stage on which multiple samples to be tested are placed, wherein the multiple samples to be tested include samples of various different dairy product categories; Step 120: Based on the sample stage image, identify the graphic codes of the plurality of samples to be tested, and determine the dairy product category of each sample to be tested from the identification information; Step 130: Based on the historical testing data of the dairy product category of each sample to be tested, determine the scheduling information for testing each sample to be tested; Step 140: Based on the sample stage image, identify the position information of each dairy product sample to be tested and determine the position information of each dairy product sample to be tested. Step 150: Based on the scheduling information and the location information, the samples to be tested are obtained one by one from the sample stage and sent to the nitrogen analyzer to perform the protein detection process on the samples to be tested in the sample stage.

[0023] The execution subject of the protein detection method provided by this invention can be an electronic device, a component in an electronic device, an integrated circuit, or a chip. The electronic device can be a mobile electronic device or a non-mobile electronic device. For example, a mobile electronic device can be a mobile phone, tablet computer, laptop computer, PDA, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc., while a non-mobile electronic device can be a server, network attached storage (NAS), or personal computer (PC), etc. This invention does not impose specific limitations.

[0024] The technical solution of this invention will be described in detail below using the example of a computer executing the protein detection method provided by this invention.

[0025] In step 110, an image of a sample stage on which multiple samples to be tested are placed is acquired. The multiple samples to be tested include samples of various different dairy product categories.

[0026] Place the sample to be tested in a test tube for buffering, and then place the test tube in the sample stage. Once the number of samples to be tested in the sample stage reaches the preset quantity, the testing process will begin.

[0027] Each test tube containing the sample to be tested is labeled with a graphic code. After identifying the label, the basic information of the sample to be tested can be determined based on the identified number.

[0028] A camera can be used to photograph a sample stage containing multiple samples for testing, resulting in an image of the sample stage. Ensure that the outline, label, and graphic code of each sample are clearly visible in the image.

[0029] In step 120, based on the sample stage image, the graphic codes of the plurality of samples to be tested are identified, and the dairy product category of each sample to be tested is determined from the identification information.

[0030] Image processing technology is used to identify the graphic codes on the samples to be tested. Common graphic codes include one-dimensional barcodes and two-dimensional barcodes.

[0031] Based on the identified graphic code information, the dairy product category of each sample to be tested is obtained. Dairy product categories can include milk powder, liquid milk, yogurt, cheese, etc. It should be noted that the graphic code contains information including the dairy product category of the sample to be tested, the sample number, and other basic information.

[0032] In step 130, based on the historical testing data of the dairy product category of each sample to be tested, scheduling information for testing each sample to be tested is determined.

[0033] Collect historical testing data for each dairy product category, including testing time, testing cycle, testing results, and any problems or anomalies during the testing process.

[0034] In-depth analysis of historical testing data reveals the characteristics and patterns of different dairy product categories during the testing process, and analyzes the testing efficiency, demand, and urgency of different dairy product categories. Based on the determined analytical information, scheduling information for testing each sample to be tested is determined.

[0035] For example, certain categories of dairy products may require longer testing times and higher testing frequencies due to their complex composition or perishability. Since some categories of dairy products have longer testing cycles, their testing priority can be lowered.

[0036] Understandably, once the scheduling information is determined, the subsequent testing process for each sample based on the nitrogen analyzer can be carried out by rationally allocating resources such as testing equipment, personnel, and time according to testing needs and equipment capabilities. This optimized resource allocation method can avoid resource waste and improve resource utilization.

[0037] Scheduling information is used to rationally implement the testing process of multiple samples to be tested, thereby improving testing efficiency.

[0038] In step 140, based on the sample stage image, the position information of each dairy product sample to be tested is identified, and the position information of each dairy product sample to be tested is determined.

[0039] Based on the sample stage image, image processing technology is used to identify the location information of each dairy product sample to be tested.

[0040] The identified location information is matched with the actual position on the sample stage to ensure the correct use of the sample during subsequent testing.

[0041] In step 150, based on the scheduling information and the location information, samples to be tested are obtained one by one from the sample stage and sent to the nitrogen analyzer to perform the protein detection process on the samples to be tested in the sample stage.

[0042] Based on scheduling and location information, samples to be tested are retrieved one by one from the sample stage. A robotic arm or other automated equipment can be used to precisely grasp the samples. The scheduling information may include key details such as the testing order, priority, and testing time of the samples.

[0043] Specifically, after determining the scheduling information for each sample to be tested, the sample to be tested currently needs to be determined based on the scheduling information. Based on the location information, the position of the sample to be tested is determined, and the robotic arm is controlled to grasp the sample to be tested and place it into the nitrogen analyzer to complete the testing process. After the current sample to be tested is completed, the sample to be tested for the next testing process is determined based on the scheduling information.

[0044] The acquired sample is fed into a nitrogen analyzer to perform protein detection. The nitrogen analyzer determines the protein content in the sample through a series of chemical reactions and measurement steps.

[0045] Specifically, such as Figure 2The schematic diagram of the detection process provided by this invention is shown. The detection process can be completed by feeding the sample to be tested into the nitrogen analyzer using a PLC. The PLC starts the digestion furnace and the exhaust gas recovery, opens the OPSIS autosampler door, and notifies OPSIS to eject the test tube rack. The OPSIS autosampler is an intelligent and automated sample introduction instrument. The PLC monitors the ejection status of the test tube rack to determine whether it has ejected. If ejection is confirmed, the PLC, based on control, places the sample to be tested into the test tube rack, notifies to retrieve the test tube rack, and performs protein detection on the sample. After detection, the analysis results are queried, and the PLC is notified to open the nitrogen analyzer, eject the test tube rack, and remove the test tube rack, completing one detection cycle.

[0046] By integrating scheduling and location information and automating sample acquisition and delivery, the automation and intelligence of the testing process can be significantly improved. This not only reduces the time and cost of manual operations but also enhances the accuracy and reliability of the tests.

[0047] The test results for each sample were recorded and subsequently analyzed and processed to understand the distribution and trends of protein content in different types of dairy products.

[0048] The protein detection method provided by this invention determines the scheduling information for testing each sample by utilizing historical testing data of the dairy product category of each sample, and determines the location information of each dairy product sample based on the sample stage image. Based on the scheduling and location information, samples are sequentially retrieved from the sample stage and sent to a nitrogen analyzer for protein detection, achieving a highly automated and intelligent detection process. This automated process not only improves the accuracy and reliability of the detection but also significantly enhances detection efficiency and flexibility.

[0049] In one embodiment, based on historical testing data of the dairy product category of each sample to be tested, scheduling information for testing each sample to be tested is determined, including: determining the testing duration and testing resource requirements of the dairy product category of each sample to be tested based on the historical testing data of the dairy product category of each sample to be tested; determining the priority for testing each sample to be tested based on the testing duration and testing resource requirements, and using the priority for testing each sample to be tested as the scheduling information.

[0050] Specifically, the testing time for each sample can be predicted based on the average testing time of similar samples in historical data, combined with the current testing environment and equipment status. The resource requirements for each sample can be determined based on the type and quantity of resources required for similar samples in historical data, combined with current resource inventory and allocation.

[0051] After determining the testing duration and resource requirements, the testing priority of each sample can be further determined. Based on the current resource status and allocation, samples with high resource matching should be prioritized for testing to improve resource utilization. Samples with significant fluctuations in test results require more frequent testing and can therefore be given higher priority.

[0052] The detection priorities of each sample to be tested are integrated into scheduling information to facilitate the subsequent scheduling process of multiple samples to be tested.

[0053] In one embodiment, after performing the protein detection process on the samples to be tested in the sample stage, the method further includes: after determining that the detection of the plurality of samples to be tested has been completed and the detection results have been generated, acquiring environmental information of the sample stage based on the sensor network arranged in the sample stage, the environmental information including at least temperature information, humidity information and airflow information; determining correction information for each dairy product category based on the environmental information, and correcting the detection results based on the correction information.

[0054] The sensor network within the sample stage is fundamental for acquiring environmental information. These sensors can include temperature sensors, humidity sensors, light sensors, gas sensors, etc., used to monitor the status of the sample stage and its surrounding environment in real time.

[0055] Based on data from temperature sensors, correction information for the detection results of various dairy product categories at different temperatures is determined. For example, for some temperature-sensitive dairy products, the protein content detected at high temperatures may be too high, requiring adjustment using correction information. Similarly, based on data from humidity sensors, correction information for the detection results of various dairy product categories at different humidity levels is determined. Excessive humidity may lead to detection results that are too low or too high, depending on the type and properties of the dairy product. Corresponding correction information can also be determined based on data from other sensors such as light and gas. This correction information can be relatively complex because the effects of light and gas on dairy products are usually not linear, requiring the development of mathematical models based on experimental data for prediction.

[0056] Because environmental information changes in real time, it is possible to continuously monitor and update the information during the detection process.

[0057] Understandably, using environmental information to determine correction information and then correcting the test results can improve the accuracy and reliability of the results.

[0058] In one embodiment, after acquiring an image of a sample stage on which multiple samples to be tested are placed, the method further includes: identifying each sample to be tested based on the sample stage image, determining abnormal samples with abnormal sample layering from the multiple samples to be tested, and deleting the abnormal samples.

[0059] Each sample to be tested is identified to determine whether it exhibits any stratification anomalies. Stratification anomalies typically manifest as an uneven stratified structure within the sample, which may be caused by problems during sample preparation (such as uneven stirring or component stratification).

[0060] Once a sample is identified as having stratification anomalies, it needs to be removed from the sample list to be tested.

[0061] By identifying each sample to be tested based on the sample stage image, and determining abnormal samples with abnormal sample layering, and then deleting them, the accuracy of the detection can be ensured and false detections can be avoided.

[0062] In one embodiment, based on the sample stage image, each sample to be detected is identified, and abnormal samples with abnormal sample layering are determined from the plurality of samples to be detected, including: converting the sample stage image from RGB color space to HSV color space, analyzing the color mask of each sample to be detected in the sample stage image, and determining a target sample containing at least two color masks; and taking the target sample as an abnormal sample with abnormal sample layering.

[0063] The RGB color space defines colors based on the additive mixing principle of three primary colors: red, green, and blue. However, the RGB color space has limitations in color segmentation and feature extraction because the brightness and hue information are mixed together, increasing the complexity of the analysis. The HSV color space, on the other hand, is a color model based on hue, saturation, and value. In the HSV space, hue represents the type of color, saturation represents the purity of the color, and value represents the lightness or darkness of the color. This separation makes color analysis more intuitive and accurate.

[0064] In the HSV color space, a color mask can be defined for each sample to be detected. A color mask is a tool for extracting specific regions in an image based on color features.

[0065] For each sample to be detected, one or more color ranges (i.e., color masks) can be set in the HSV space based on its expected color characteristics. Then, by comparing the HSV value of each pixel in the image with the range of the color mask, it is determined whether the pixel belongs to the target sample.

[0066] In one embodiment, deleting the abnormal sample includes: determining the location information of the abnormal sample, and based on the determined location information of the abnormal sample, controlling a robotic arm to grab and discard the abnormal sample.

[0067] The location information of the anomalous sample can include the coordinates of the anomalous sample in the image (such as pixel coordinates or physical coordinates), as well as possible size and shape information.

[0068] After determining the location information of abnormal samples, and using this information to control the robotic arm to perform grasping and discarding operations, the efficiency and accuracy of detection can be further improved.

[0069] The protein detection device provided by the present invention is described below. The protein detection device described below and the protein detection method described above can be referred to in correspondence.

[0070] like Figure 3 As shown, the device includes: Image acquisition module 310 is used to acquire images of a sample stage on which multiple samples to be tested are placed, the multiple samples to be tested including samples of various different dairy product categories; The identification module 320 is used to identify the graphic codes of the plurality of samples to be tested based on the sample stage image, and to determine the dairy product category of each sample to be tested from the identification information. The scheduling information determination module 330 is used to determine the scheduling information for testing each sample based on the historical testing data of the dairy product category of each sample to be tested. The location information determination module 340 is used to identify the location information of each dairy product sample to be tested based on the sample stage image, and determine the location information of each dairy product sample to be tested. The detection module 350 is used to acquire samples to be tested one by one from the sample stage based on the scheduling information and the location information and send them to the nitrogen analyzer to perform the protein detection process on the samples to be tested in the sample stage.

[0071] The protein detection device provided by this invention determines the scheduling information for testing each sample by using historical detection data of the dairy product category of each sample to be tested, and determines the position information of each dairy product sample to be tested based on the sample stage image. Based on the scheduling information and position information, the sample to be tested is retrieved one by one from the sample stage and sent to the nitrogen analyzer for protein detection, realizing a highly automated and intelligent detection process. This automated process not only improves the accuracy and reliability of the detection, but also significantly enhances the detection efficiency and flexibility.

[0072] In one embodiment, the scheduling information determination module 330 is specifically used for: Based on historical testing data of the dairy product categories of each sample to be tested, scheduling information for testing each sample to be tested is determined, including: Based on the historical testing data of the dairy product categories of each sample to be tested, the testing time and testing resource requirements for each dairy product category of the sample to be tested are determined. Based on the detection duration and detection resource requirements, the priority of detecting each sample to be tested is determined, and the priority of detecting each sample to be tested is used as the scheduling information.

[0073] In one embodiment, the detection module 350 is specifically used for: After performing the protein detection process on the sample to be tested in the sample stage, the method further includes: Once it is determined that the multiple samples to be tested have been tested and the test results have been generated, the environmental information of the sample stage is obtained based on the sensor network arranged in the sample stage. The environmental information includes at least temperature information, humidity information and airflow information. Based on the environmental information, correction information for each dairy product category is determined, and the test results are corrected based on the correction information.

[0074] In one embodiment, the image acquisition module 310 is specifically used for: After acquiring the image of the sample stage where multiple samples to be tested are placed, the method further includes: Based on the sample stage image, each sample to be tested is identified, and abnormal samples with abnormal sample layering are determined from the plurality of samples to be tested. The abnormal sample was deleted.

[0075] In one embodiment, the image acquisition module 310 is further configured to: Based on the sample stage image, each sample to be tested is identified, and abnormal samples with abnormal sample layering are determined from the plurality of samples to be tested, including: The sample stage image is converted from RGB color space to HSV color space, and the color mask of each sample to be detected in the sample stage image is analyzed to determine the target sample containing at least two color masks. The target sample is considered an abnormal sample due to sample stratification anomaly.

[0076] In one embodiment, the image acquisition module 310 is further configured to: The deletion of the abnormal sample includes: The location information of the abnormal sample is determined, and based on the determined location information of the abnormal sample, the robotic arm is controlled to grab the abnormal sample and discard it.

[0077] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include a processor 410, a communications interface 420, a memory 430, and a communication bus 440, wherein the processor 410, communications interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a protein detection method, which includes: acquiring an image of a sample stage on which multiple samples to be tested are placed, the multiple samples to be tested including samples from various different dairy product categories; Based on the sample stage image, the graphic codes of the multiple samples to be tested are identified, and the dairy product category of each sample to be tested is determined from the identification information. Based on the historical testing data of the dairy product category of each sample to be tested, the scheduling information for testing each sample to be tested is determined; Based on the sample stage image, the position information of each dairy product sample to be tested is identified, and the position information of each dairy product sample to be tested is determined. Based on the scheduling information and the location information, samples to be tested are obtained one by one from the sample stage and sent to the nitrogen analyzer to perform protein detection on the samples to be tested in the sample stage.

[0078] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0079] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being able to be stored on a non-transitory computer-readable storage medium, the computer program being executed by a processor, the computer being able to execute the protein detection method provided by the above methods, the method including: acquiring an image of a sample stage on which multiple samples to be tested are placed, the multiple samples to be tested including samples of various different dairy product categories; Based on the sample stage image, the graphic codes of the multiple samples to be tested are identified, and the dairy product category of each sample to be tested is determined from the identification information. Based on the historical testing data of the dairy product category of each sample to be tested, the scheduling information for testing each sample to be tested is determined; Based on the sample stage image, the position information of each dairy product sample to be tested is identified, and the position information of each dairy product sample to be tested is determined. Based on the scheduling information and the location information, samples to be tested are obtained one by one from the sample stage and sent to the nitrogen analyzer to perform protein detection on the samples to be tested in the sample stage.

[0080] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the protein detection method provided by the methods described above, the method comprising: acquiring an image of a sample stage on which a plurality of samples to be tested are placed, the plurality of samples to be tested including samples of various different dairy product categories; Based on the sample stage image, the graphic codes of the multiple samples to be tested are identified, and the dairy product category of each sample to be tested is determined from the identification information. Based on the historical testing data of the dairy product category of each sample to be tested, the scheduling information for testing each sample to be tested is determined; Based on the sample stage image, the position information of each dairy product sample to be tested is identified, and the position information of each dairy product sample to be tested is determined. Based on the scheduling information and the location information, samples to be tested are obtained one by one from the sample stage and sent to the nitrogen analyzer to perform protein detection on the samples to be tested in the sample stage.

[0081] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. 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 the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0082] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for protein detection, characterized in that, include: Acquire an image of a sample stage on which multiple samples to be tested are placed, the multiple samples to be tested including samples of various different dairy product categories; Based on the sample stage image, the graphic codes of the multiple samples to be tested are identified, and the dairy product category of each sample to be tested is determined from the identification information. Based on the historical testing data of the dairy product category of each sample to be tested, the scheduling information for testing each sample to be tested is determined; Based on the sample stage image, the position information of each dairy product sample to be tested is identified, and the position information of each dairy product sample to be tested is determined. Based on the scheduling information and the location information, samples to be tested are obtained one by one from the sample stage and sent to the nitrogen analyzer to perform protein detection on the samples to be tested in the sample stage.

2. The protein detection method according to claim 1, characterized in that, The process of determining the scheduling information for testing each sample based on historical testing data of its dairy product category includes: Based on the historical testing data of the dairy product categories of each sample to be tested, the testing time and testing resource requirements for each dairy product category of the sample to be tested are determined. Based on the detection duration and detection resource requirements, the priority of detecting each sample to be tested is determined, and the priority of detecting each sample to be tested is used as the scheduling information.

3. The protein detection method according to claim 1, characterized in that, After performing the protein detection process on the sample to be tested in the sample stage, the method further includes: Once it is determined that the multiple samples to be tested have been tested and the test results have been generated, the environmental information of the sample stage is obtained based on the sensor network arranged in the sample stage. The environmental information includes at least temperature information, humidity information and airflow information. Based on the environmental information, correction information for each dairy product category is determined, and the test results are corrected based on the correction information.

4. The protein detection method according to claim 1, characterized in that, After acquiring the image of the sample stage where multiple samples to be tested are placed, the method further includes: Based on the sample stage image, each sample to be tested is identified, and abnormal samples with abnormal sample layering are determined from the plurality of samples to be tested. The abnormal sample was deleted.

5. The protein detection method according to claim 4, characterized in that, The step of identifying each sample to be tested based on the sample stage image, and determining abnormal samples with abnormal sample layering from the plurality of samples to be tested, includes: The sample stage image is converted from RGB color space to HSV color space, and the color mask of each sample to be detected in the sample stage image is analyzed to determine the target sample containing at least two color masks. The target sample is considered an abnormal sample due to sample stratification anomaly.

6. The protein detection method according to claim 4, characterized in that, The deletion of the abnormal sample includes: The location information of the abnormal sample is determined, and based on the determined location information of the abnormal sample, the robotic arm is controlled to grab the abnormal sample and discard it.

7. A protein detection device, characterized in that, include: The image acquisition module is used to acquire images of a sample stage on which multiple samples to be tested are placed, the multiple samples to be tested including samples of various different dairy product categories; The identification module is used to identify the graphic codes of the plurality of samples to be tested based on the sample stage image, and to determine the dairy product category of each sample to be tested from the identification information. The scheduling information determination module is used to determine the scheduling information for testing each sample based on the historical testing data of the dairy product category of each sample to be tested. The location information determination module is used to identify the location information of each dairy product sample to be tested based on the sample stage image, and to determine the location information of each dairy product sample to be tested. The detection module is used to acquire samples to be tested one by one from the sample stage based on the scheduling information and the location information and send them to the nitrogen analyzer to perform the protein detection process on the samples to be tested in the sample stage.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the protein detection method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the protein detection method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the protein detection method as described in any one of claims 1 to 6.

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