Sample state detection method applied to biological sample and computer equipment

Abstract

The embodiment of the invention discloses a sample state detection method applied to a biological sample and computer equipment. A specific embodiment of the method comprises the following steps: determining state detection configuration information for a target biological sample group; according to the storage configuration information, moving the target biological sample group into a sample storage device so as to store the target biological samples; in response to the arrival of the sample detection time point, executing the following processing steps on the target biological sample: sucking the target biological sample to a slide manufacturing device through a pipetting device to generate a to-be-detected slide; controlling the electron microscope to collect electron microscope images of the to-be-detected slide under different image scales to obtain an electron microscope image group; generating sub-sample detection information for the target biological sample; and generating sample detection information for the target biological sample group according to the obtained sub-sample detection information group. According to the embodiment, the consistency of sample detection is ensured, and meanwhile, the sample detection efficiency is greatly improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and specifically to a sample state detection method and a computer device applied to biological samples. Background Art

[0002] For biological samples, full validation, partial validation or cross validation is usually used to verify the performance of biological samples. Specifically, by setting up, including but not limited to, a control group, the experimental group is used to observe the performance of biological samples. However, for more complex experiments, there are multiple groups of biological samples that need to be manually observed, and the observation efficiency is low. At the same time, due to the use of manual observation, the observation results may be unreliable due to errors.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0004] The content of this application is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this application is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0005] Some embodiments of the present application propose a sample status detection method, a computer device, and a computer-readable storage medium applied to biological samples to solve one or more of the technical problems mentioned in the above background technology section.

[0006] In a first aspect, some embodiments of the present application provide a sample state detection method applied to biological samples, the method comprising: determining state detection configuration information for a target biological sample group, wherein the target biological sample group comprises at least three target biological samples arranged in parallel and stored in sample tubes, the target biological samples are biological samples to be subjected to sample state detection, the state detection configuration information comprises: sample storage configuration information and sample detection configuration information, the sample detection configuration information comprises: sample detection time point and sample detection index information; according to the storage configuration information, the target biological sample group is moved to a sample storage device to detect the state of the target biological sample group; The target biological sample is stored; in response to reaching the sample detection time point, for each target biological sample in the target biological sample group, the following processing steps are performed: the target biological sample is sucked into a slide making device by a pipetting device to generate a slide to be detected; the electron microscope is controlled to collect electron microscope images of the slide to be detected at different image scales to obtain an electron microscope image group; sub-sample detection information for the target biological sample is generated according to the sample detection index information, the electron microscope image group and a pre-trained sample detection model; and sample detection information for the target biological sample group is generated according to the obtained sub-sample detection information group.

[0007] In a second aspect, the present application also provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the method described in any implementation of the first aspect is implemented.

[0008] In a third aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the method described in any implementation of the first aspect is implemented.

[0009] The above-mentioned embodiments of the present application have the following beneficial effects: through the sample state detection method applied to biological samples of some embodiments of the present application, the sample storage and sample detection of the unified standard for the biological sample group are realized. Specifically, first, the state detection configuration information for the target biological sample group is determined, wherein the target biological sample group includes at least three target biological samples stored in a sample tube arranged in parallel, and the target biological sample is a biological sample to be subjected to sample state detection, and the state detection configuration information includes: sample storage configuration information and sample detection configuration information, and the sample detection configuration information includes: sample detection time point and sample detection index information. Secondly, according to the above-mentioned storage configuration information, the target biological sample group is moved to the sample storage device to perform sample storage on the target biological sample. In this way, the same storage for biological samples is realized. Then, in response to reaching the above-mentioned sample detection time point, for each target biological sample in the above-mentioned target biological sample group, the following processing steps are performed: the first step is to suck the target biological sample to the slide making device through the pipetting device to generate the slide to be detected. In this way, automatic quantitative pipetting and automatic slide making are realized. The second step is to control the electron microscope to collect electron microscope images of the above-mentioned glass slide to be tested at different image scales to obtain an electron microscope image group. In this way, electron microscope images under different fields of view are obtained. The third step is to generate sub-sample detection information for the above-mentioned target biological sample based on the above-mentioned sample detection index information, the above-mentioned electron microscope image group and the pre-trained sample detection model. By combining the sample detection model, automated sample detection for the sample detection index information is achieved. Finally, based on the obtained sub-sample detection information group, sample detection information for the above-mentioned target biological sample group is generated. In this way, an overall sample evaluation for the entire target biological sample group is obtained. This method ensures the consistency of sample detection and greatly improves the efficiency of sample detection. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0011] Figure 1 is a flow chart of some embodiments of a sample state detection method applied to a biological sample according to the present application; Figure 2 is a schematic diagram of a sample storage device; Figure 3 It is an overall schematic diagram of a sample storage device, a liquid transfer device and a slide making device; Figure 4 is a schematic diagram of the model structure of the sample detection model; Figure 5 It is a schematic diagram of the structure of a computer device suitable for implementing some embodiments of the present application. DETAILED DESCRIPTION

[0012] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0013] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0014] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0015] It should be noted that the modifications of "one" and "plurality" mentioned in the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0016] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0017] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0018] refer to Figure 1 , shows a process 100 of some embodiments of the sample state detection method applied to biological samples according to the present application. The sample state detection method applied to biological samples comprises the following steps: Step 101: determine status detection configuration information for a target biological sample group.

[0019] In some embodiments, an execution subject (e.g., a computing device) of a sample state detection method applied to a biological sample may determine state detection configuration information for a target biological sample group. The target biological sample group includes at least three target biological samples stored in a sample tube and arranged in parallel. The target biological sample is a biological sample to be subjected to sample state detection. The above-mentioned state detection configuration information includes: sample storage configuration information and sample detection configuration information. The above-mentioned sample detection configuration information includes: sample detection time point and sample detection index information. The sample storage configuration information may characterize the storage requirements of the target biological sample. The sample detection time point characterizes the time point at which the target biological sample begins to be detected. For example, the sample detection time point may be 48 hours after the target biological sample is configured. The sample detection index information may characterize the sample index to be collected. For example, sample activity, sample color, etc. In practice, an experimenter may configure the state detection configuration information for the target biological sample group through a graphical interface. Specifically, when the experiment is more complex, there may be multiple groups of multiple target biological samples, so the experimenter may configure the state monitoring configuration information in batches through a graphical interface.

[0020] It should be noted that the above-mentioned wireless connection methods may include but are not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0021] It should be noted that the above-mentioned computing device can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the computing device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here.

[0022] Step 102 : moving the target biological sample group to a sample storage device according to the storage configuration information to store the target biological samples.

[0023] In some embodiments, the execution subject may move the target biological sample group to the sample storage device according to the storage configuration information to store the target biological sample. In practice, the storage configuration information may include: sample storage humidity and sample storage brightness. Therefore, the execution subject may store the target biological sample in the sample storage device according to the sample storage humidity and sample storage brightness. For example, the sample storage device may be a light-proof sample storage box.

[0024] Optionally, the sample storage configuration information includes: sample storage temperature.

[0025] Optionally, the sample storage device includes: a sample storage box. The sample storage box includes: a first sample storage box and a second sample storage box. The first sample storage box includes: a first sample storage box body and a first sample storage box cover. The first sample storage box body is provided with: K sample tube placement compartments, a temperature sensor and an air pressure sensor. K≥1. The first sample storage box is arranged above the second sample storage box. The second sample storage box is used to store a heat-conducting medium. In practice, the heat-conducting medium may be pure water. The second sample storage box includes: a temperature sensor and a heating device.

[0026] As an example, see Figure 2 The schematic diagram of the sample storage device shown in the figure, wherein the sample storage device comprises: a first sample storage box and a second sample storage box 4. The first sample storage box comprises: a first sample storage box body 3 and a first sample storage box cover 1. Wherein, the first sample storage box body is provided with: K sample tube placement compartments 2. When the first sample storage box is in a sealed state, the first sample storage box cover 1 and the first sample storage box body 3 are fitted and sealed. The first sample storage box body 3 is filled with inert gas helium. The second sample storage box comprises a filling port 5 and a release port 6. Specifically, a heat-conducting medium can be added to the second sample storage box 4 through the filling port 5, and the heat-conducting medium can be released through the release port 6. A temperature sensor is arranged inside the first sample storage box body 3. An air pressure sensor is arranged between the first sample storage box cover 1 and the first sample storage box body 3, for example, arranged on the inner surface of the first sample storage box cover 1, so as to detect whether the air pressure inside the first sample storage box is stable (whether there is air leakage) when the first sample storage box is sealed. In addition, since the first sample storage box body 3 contains an inert gas and the second sample storage box 4 contains a heat-conducting medium, the thermal conductivity coefficients of the two are often different. Therefore, temperature sensors are respectively provided in the first sample storage box body 3 and the second sample storage box 4 to ensure stable and precise control. In addition, the second sample storage box 4 may also include: an electric heating wire to achieve heating of the heat-conducting medium.

[0027] In some optional implementations of some embodiments, the execution subject moves the target biological sample group to a sample storage device according to the storage configuration information to store the target biological samples, which may include the following steps: The first step is to control the robot arm to move the target biological sample group to the K sample tube placement compartments included in the first sample storage box body.

[0028] In practice, the execution entity may control a high-precision robotic arm to move the target biological sample group to the K sample tube placement compartments included in the first sample storage box body.

[0029] The second step is to control the lid of the first sample storage box to close in response to the successful movement, and to detect the air tightness of the box body through the air pressure sensor included in the box body of the first sample storage box.

[0030] In practice, first, the execution subject can control the lid of the first sample storage box to close by a high-precision mechanical arm. Then, the first sample storage box is placed in a negative pressure environment, and the air pressure change is detected by the air pressure sensor included in the box body of the first sample storage box. When the air pressure signal is stable and the same as the air pressure reading when it is not placed in the negative pressure environment, it indicates that the air tightness is stable. When the air pressure signal is different from the air pressure reading when it is not placed in the negative pressure environment, it indicates that the air tightness is unstable.

[0031] In the third step, in response to the stable air tightness of the first sample storage box, the heating device is controlled to heat the heat-conducting medium, and the temperature is monitored by the temperature sensor included in the first sample storage box and the temperature sensor included in the second sample storage box respectively to obtain the first temperature and the second temperature.

[0032] In practice, the above-mentioned execution entity can control the electric heating wire to heat the heat-conducting medium.

[0033] In the fourth step, in response to the first temperature being the sample storage temperature, the heating device is controlled to perform constant temperature heating at the second temperature.

[0034] Step 103, in response to reaching the sample detection time point, for each target biological sample in the target biological sample group, perform the following processing steps: Step 1031 , aspirating the target biological sample into a slide making device through a pipetting device to generate a slide to be tested.

[0035] In some embodiments, the execution subject can suck the target biological sample into the slide making device through a liquid transfer device to generate a slide to be tested. In practice, the liquid transfer device can be a liquid transfer gun.

[0036] Optionally, the liquid transfer device includes: a liquid transfer robot arm, an electric-controlled liquid transfer gun, and a positioning camera. The above-mentioned glass slide making device includes: a glass slide placement table and a cover glass cover slip machine. The above-mentioned cover glass cover slip machine is arranged above the above-mentioned glass slide placement table. The above-mentioned cover glass cover slip machine sucks the cover glass through a micro vacuum suction cup.

[0037] As an example, see Figure 3The overall schematic diagram of the sample storage device, the liquid transfer device and the glass slide making device shown in the figure includes: a sample storage device 7, a liquid transfer device and a glass slide making device 9. Specifically, a mobile frame is arranged above the sample storage device 7 and the glass slide making device 9. The mobile frame moves vertically along the guide rail. A liquid transfer robot arm 8 and a cover glass cover slip machine 13 are arranged above the mobile frame. The liquid transfer robot arm 8 and the cover glass cover slip machine 13 are driven to move by a chain arranged in the mobile frame. An electric-controlled liquid transfer gun 10 is connected below the liquid transfer robot arm 8, wherein the electric-controlled liquid transfer gun controls the amount of liquid suction by electric control. A positioning camera 11 facing downward is arranged on the outer side of the liquid transfer robot arm 8. The cover glass cover slip machine 13 includes a telescopic arm and a micro vacuum suction cup 14. The telescopic arm is arranged between the cover glass cover slip machine 13 and the micro vacuum suction cup 14. The micro vacuum suction cup 14 adsorbs a cover glass.

[0038] In some optional implementations of some embodiments, the execution subject sucks the target biological sample into the slide making device through the pipetting device to generate the slide to be detected, which may include the following steps: The first step is to control the lid of the first sample storage box to open.

[0039] In the second step, in response to the box cover being opened, a first image is captured by a positioning camera.

[0040] The first image is a top view image of the first sample storage box.

[0041] The third step is to determine the coordinates of the positioning point based on the first image.

[0042] The coordinates of the positioning points represent the position of the sample tube placement chamber corresponding to the target biological sample to be pipetted.

[0043] In practice, the above execution entity can use the Lite-Unet model, with the first image as input and the coordinates of the positioning points as output.

[0044] The fourth step is to generate the motion path of the first robot arm according to the above-mentioned positioning point coordinates and the initial position of the pipetting robot arm.

[0045] In practice, in order to ensure that the electric-controlled pipette gun is accurately inserted into the sample tube to achieve the absorption of the target biological sample, the positioning point coordinates can be used as the end point coordinates, and the initial position of the pipetting robot arm can be used as the starting point coordinates. The micro-motion route can be generated by trajectory smoothing as the first robot arm movement route.

[0046] The fifth step is to control the above-mentioned liquid transfer robot arm to move to the above-mentioned positioning point coordinates along the above-mentioned first robot arm movement route.

[0047] In practice, the execution entity can control the moving frame and the liquid transfer robot arm to move to the positioning point coordinates along the movement route of the first robot arm.

[0048] Step 6: In response to the successful movement, the above-mentioned electronically controlled pipette gun is controlled by the above-mentioned pipetting robot arm to quantitatively absorb the target raw sample.

[0049] Step 7: In response to successful suction, a second robot arm motion route is generated based on the above-mentioned positioning point coordinates and slide coordinates.

[0050] The above-mentioned slide coordinates represent the coordinates of the center point of the slide on the above-mentioned slide placement table.

[0051] In practice, in order to ensure that the target biological sample is accurately dropped into the center of the glass slide, the above-mentioned execution entity can use the center point coordinates of the glass slide placement table as the end point coordinates and the positioning point coordinates as the starting point coordinates, and generate a micro-motion route by trajectory smoothing as the movement route of the second robotic arm.

[0052] The eighth step is to control the above-mentioned liquid transfer robot arm to move to the above-mentioned slide coordinates along the movement route of the above-mentioned second robot arm.

[0053] The execution body can control the moving frame and the liquid transfer robot arm to move to the glass slide coordinates along the movement route of the second robot arm.

[0054] In the ninth step, in response to the successful movement, the electronically controlled pipette is controlled to move the sucked target raw sample onto the glass slide.

[0055] The tenth step, in response to the successful movement, controls the cover glass cover slide machine to cover the cover glass on the slide glass to obtain the above-mentioned slide to be inspected.

[0056] In practice, first, the execution body can control the moving frame and the cover glass cover slide machine to move above the glass slide. Then, the telescopic arm is controlled to extend so that the glass slide contacts the cover glass. Then, the suction of the micro vacuum suction cup is released so that the cover glass completely covers the glass slide by gravity, thereby obtaining the glass slide to be tested.

[0057] Step 1032 , controlling the electron microscope to collect electron microscope images of the glass slide to be inspected at different image scales to obtain an electron microscope image group.

[0058] In some embodiments, the execution subject can control an electron microscope to collect electron microscope images of the glass slide to be inspected at different image scales to obtain an electron microscope image group.

[0059] In practice, according to different image acquisition requirements, a conventional optical microscope can also be used to acquire images of the glass slide to be inspected at different image scales to obtain an electron microscope image group. Specifically, the glass slide to be inspected can be moved to the observation position of the electron microscope by manual transportation. The glass slide to be inspected can also be moved to the observation position of the electron microscope by a robotic arm.

[0060] Step 1033, generating sub-sample detection information for the target biological sample based on the sample detection index information, the electron microscope image group and the pre-trained sample detection model.

[0061] In some embodiments, the above-mentioned execution entity can generate sub-sample detection information for the target biological sample based on the sample detection index information, the electron microscope image group and the pre-trained sample detection model.

[0062] Optionally, the sample detection model includes: an image enhancement model, a sample image encoding model and a text encoding model.

[0063] In some optional implementations of some embodiments, the execution subject generates sub-sample detection information for the target biological sample according to the sample detection index information, the electron microscope image group and the pre-trained sample detection model, which may include the following steps: In the first step, each electron microscope image in the electron microscope image group is enhanced by using the image enhancement model to generate an enhanced electron microscope image and obtain an enhanced electron microscope image group.

[0064] In the second step, the enhanced electron microscope images in the enhanced electron microscope image group are subjected to image feature extraction through the sample image encoding model to generate image features and obtain an image feature group.

[0065] The third step is to extract text features from the sample detection index information through the text encoding model to generate text features.

[0066] The fourth step is to generate sub-sample detection information for the target biological sample based on the image feature group and the text feature.

[0067] Optionally, the image enhancement model includes: an image detection head group, a fusion model and an image enhancement head group.

[0068] In some optional implementations of some embodiments, the execution subject performs image enhancement on each electron microscope image in the electron microscope image group by using the image enhancement model to generate an enhanced electron microscope image, and obtaining the enhanced electron microscope image group may include the following steps: In the first step, the shallow image features are extracted from the electron microscope image group through the image detection head group to obtain a shallow image feature group.

[0069] In the second step, the above shallow image feature groups are fused through the fusion model to obtain fused image features.

[0070] The third step is to generate the enhanced electron microscope image group based on the fused image features and the image enhancement head group.

[0071] As an example, see Figure 4The model structure diagram of the sample detection model shown in the figure, wherein the sample detection model includes: an image enhancement model, a sample image coding model 406, and a text coding model 407. The image enhancement model includes: an image detection head group 402, a fusion model 403, and an image enhancement head group 404. The input of the text coding model 407 is sample detection information 405. The output of the sample image coding model 406 and the text coding model 407 is sub-sample detection information 408. Specifically, the image detection head group 402 includes: image detection head A1, image detection head A2, image detection head A3, image detection head A4, image detection head A5, image detection head A6, image detection head A7, image detection head A8, and image detection head A9. Among them, the input size of image detection head A1 is N1×M1×3. The input size of image detection head A2 is N2×M2×3. The input size of image detection head A3 is N3×M3×3. The input size of image detection head A4 is N4×M4×3. The input size of image detection head A5 is N5×M5×3. The input size of image detection head A6 is N6×M6×3. The input size of image detection head A7 is N7×M7×3. The input size of image detection head A8 is N8×M8×3. The input size of image detection head A9 is N9×M9×3. Among them, N6<N1<N2. N7<N2<N3. N8<N3<N4. N9<N4<N5. M6<M1<M2. M7<M2<M3. M8<M3<M4. M9<M4<M5. Image detection head A1, image detection head A2, image detection head A3, image detection head A4, image detection head A5, image detection head A6, image detection head A7, image detection head A8, image detection head A9. All use a convolutional layer containing 3 layers of serial connection. Specifically, taking image detection head A1, image detection head A2 and image detection head A6 as examples, when the image size of the electron microscope image is between N1×M1×3 and N2×M2×3, the electron microscope image can be expanded, wherein the image size of the expanded electron microscope image is N6×M6×3, and the expanded electron microscope image is directly input to the image detection head A6. In this way, the robustness of the model can be improved, and the number of invalid pixels after expansion can be reduced. Taking image detection head A1 as an example, when the image size of the electron microscope image is N1×M1×3, the electron microscope image is first input to the image detection head A1, and then the output of the image detection head A1 and the output of the image detection head A2 are superimposed as the output of the image detection head A6. The fusion model 403 includes: a fusion layer B1, a fusion layer B2 and a fusion layer B3. Among them, the fusion layer B1 is an overlay layer to perform feature superposition on the outputs of the image detection heads A6, A7, A8 and A9. The model structures of the fusion layers B2 and B3 are symmetrical. Specifically, the fusion layer B2 is a downsampling network, and the fusion layer B3 is an upsampling network.The image enhancement head group 404 includes: image enhancement head C1, image enhancement head C2, image enhancement head C3, image enhancement head C4, and image enhancement head C5. Among them, the image enhancement head C1, image enhancement head C2, image enhancement head C3, image enhancement head C4, and image enhancement head C5 are all upsampling networks, corresponding to 5 output sizes respectively. Among them, the output size of image enhancement head C1 is less than the output size of image enhancement head C2, less than the output size of image enhancement head C3, less than the output size of image enhancement head C4, and less than the output size of image enhancement head C5. The sample image coding model 406 includes: N image coding units. The image coding unit includes: SelfAttention layer, Add&Norm layer, Feed Forward network, and Add&Norm layer. The text coding model 407 takes the sample inspection index information as input, and includes: N text coding units, and the text coding unit includes: SelfAttention layer, Add&Norm layer, SelfAttention layer, Add&Norm layer, CrossAttention layer, and Add&Norm layer. Among them, a control unit is provided between the first Add&Norm layer and the CrossAttention layer included in the text encoding model 407 to control the output of the first Add&Norm layer included in the text encoding model 407 to be directly input into the second SelfAttention layer or CrossAttention layer included in the text encoding model 407. Specifically, when the sample detection index information is plain text, the output of the first Add&Norm layer included in the text encoding model 407 is directly input into the second SelfAttention layer included in the text encoding model 407. When the sample detection index information is multimodal data, the output of the first Add&Norm layer included in the text encoding model 407 is controlled to be directly input into the CrossAttention layer.

[0072] The above-mentioned sample detection model is an inventive point of the present application. First, multi-scale electron microscope image enhancement is realized through an image enhancement model. Secondly, the recognition of biological samples and the automatic detection of the matching degree between the recognition result and the sample detection index information are realized through a sample image encoding model and a text encoding model. In addition, considering that the data types of the sample detection index information are diverse, by setting a control unit, the extraction of text features is realized from unimodal text data types and multimodal data types, thereby ensuring the robustness of the recognition process.

[0073] Step 104: Generate sample detection information for the target biological sample group based on the obtained sub-sample detection information group.

[0074] In some embodiments, the above-mentioned execution subject can generate sample detection information for the target biological sample group based on the obtained sub-sample detection information group. In practice, the above-mentioned execution subject can generate sample detection information for the target biological sample group based on the obtained sub-sample detection information group through a voting mechanism. For example, the sub-sample detection information group includes: sub-sample detection information A, sub-sample detection information B, sub-sample detection information C, and sub-sample detection information D. The detection results represented by sub-sample detection information A, sub-sample detection information B, and sub-sample detection information C are consistent, so the sample detection information is any one of sub-sample detection information A, sub-sample detection information B, and sub-sample detection information C. In addition, when the maximum value of the voting result, that is, the number of sub-sample detection information representing the same detection result is less than the threshold value, sample detection information representing sample abnormality is generated.

[0075] The above-mentioned embodiments of the present application have the following beneficial effects: through the sample state detection method applied to biological samples of some embodiments of the present application, the sample storage and sample detection of the unified standard for the biological sample group are realized. Specifically, first, the state detection configuration information for the target biological sample group is determined, wherein the target biological sample group includes at least three target biological samples stored in a sample tube arranged in parallel, and the target biological sample is a biological sample to be subjected to sample state detection, and the state detection configuration information includes: sample storage configuration information and sample detection configuration information, and the sample detection configuration information includes: sample detection time point and sample detection index information. Secondly, according to the above-mentioned storage configuration information, the target biological sample group is moved to the sample storage device to perform sample storage on the target biological sample. In this way, the same storage for biological samples is realized. Then, in response to reaching the above-mentioned sample detection time point, for each target biological sample in the above-mentioned target biological sample group, the following processing steps are performed: the first step is to suck the target biological sample to the slide making device through the pipetting device to generate the slide to be detected. In this way, automatic quantitative pipetting and automatic slide making are realized. The second step is to control the electron microscope to collect electron microscope images of the above-mentioned glass slide to be tested at different image scales to obtain an electron microscope image group. In this way, electron microscope images under different fields of view are obtained. The third step is to generate sub-sample detection information for the above-mentioned target biological sample based on the above-mentioned sample detection index information, the above-mentioned electron microscope image group and the pre-trained sample detection model. By combining the sample detection model, automated sample detection for the sample detection index information is achieved. Finally, based on the obtained sub-sample detection information group, sample detection information for the above-mentioned target biological sample group is generated. In this way, an overall sample evaluation for the entire target biological sample group is obtained. This method ensures the consistency of sample detection and greatly improves the efficiency of sample detection. Figure 5A schematic block diagram of the structure of a computer device provided in an embodiment of the present application. The computer device may be a terminal.

[0076] like Figure 5 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0077] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any sample state detection method applied to a biological sample.

[0078] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0079] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any sample state detection method applied to biological samples.

[0080] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0081] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0082] In one embodiment, the processor is used to run a computer program stored in a memory to implement the following steps: determining state detection configuration information for a target biological sample group, wherein the target biological sample group includes at least three target biological samples arranged in parallel and stored in sample tubes, the target biological samples are biological samples to be subjected to sample state detection, the state detection configuration information includes: sample storage configuration information and sample detection configuration information, the sample detection configuration information includes: sample detection time point and sample detection index information; according to the storage configuration information, the target biological sample group is moved to a sample storage device to detect the state of the sample group; The target biological sample is stored; in response to reaching the sample detection time point, for each target biological sample in the target biological sample group, the following processing steps are performed: the target biological sample is sucked into a slide making device by a pipetting device to generate a slide to be detected; the electron microscope is controlled to collect electron microscope images of the slide to be detected at different image scales to obtain an electron microscope image group; sub-sample detection information for the target biological sample is generated according to the sample detection index information, the electron microscope image group and a pre-trained sample detection model; and sample detection information for the target biological sample group is generated according to the obtained sub-sample detection information group.

[0083] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions. The method implemented when the program instructions are executed can refer to the various embodiments of the sample status detection method applied to biological samples in the present application.

[0084] The computer-readable storage medium may be an internal storage unit of the computer device in the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., provided on the computer device.

[0085] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0086] The serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments. The above description is only a specific implementation mode of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for detecting a sample state of a biological sample, comprising: Determine state detection configuration information for a target biological sample group, wherein the target biological sample group includes at least three target biological samples arranged in parallel and stored in sample tubes, the target biological samples are biological samples to be subjected to sample state detection, the state detection configuration information includes: sample storage configuration information and sample detection configuration information, and the sample detection configuration information includes: sample detection time point and sample detection index information; According to the storage configuration information, the target biological sample group is moved to a sample storage device to store the target biological samples; In response to reaching the sample detection time point, for each target biological sample in the target biological sample group, the following processing steps are performed: The target biological sample is sucked into the slide making device by a pipetting device to generate a slide to be tested; Controlling the electron microscope to collect electron microscope images of the glass slide to be detected at different image scales to obtain an electron microscope image group; Generating sub-sample detection information for the target biological sample according to the sample detection index information, the electron microscope image group and a pre-trained sample detection model; According to the obtained sub-sample detection information group, sample detection information for the target biological sample group is generated.

2. The method according to claim 1, wherein: The sample storage configuration information includes: sample storage temperature; the sample storage device includes: a sample storage box; the sample storage box includes: a first sample storage box and a second sample storage box; the first sample storage box includes: a first sample storage box body and a first sample storage box cover; the first sample storage box body is provided with: K sample tube placement compartments, a humidity sensor and an air pressure sensor, K ≥ 1; the first sample storage box is provided above the second sample storage box; the second sample storage box is used to store a heat-conducting medium; the second sample storage box includes: a temperature sensor and a heating device; and according to the storage configuration information, moving the target biological sample group to the sample storage device to store the target biological sample, including: Controlling the robotic arm to move the target biological sample group to the K sample tube placement compartments included in the first sample storage box body; In response to the movement being successful, controlling the cover of the first sample storage box to close, and detecting the air tightness of the box body through an air pressure sensor included in the box body of the first sample storage box; In response to the first sample storage box having stable airtightness, controlling the heating device to heat the heat-conducting medium, and performing temperature monitoring respectively through a temperature sensor included in the first sample storage box and a temperature sensor included in the second sample storage box to obtain a first temperature and a second temperature; In response to the first temperature being the sample storage temperature, the heating device is controlled to perform constant temperature heating at the second temperature.

3. The method according to claim 2, wherein: The liquid transfer device comprises: a liquid transfer mechanical arm, an electric-controlled liquid transfer gun and a positioning camera, the glass slide making device comprises: a glass slide placement table and a cover glass cover slip machine, the cover glass cover slip machine is arranged above the glass slide placement table, and the cover glass cover slip machine sucks the cover glass through a micro vacuum suction cup; and the target biological sample is sucked to the glass slide making device by the liquid transfer device to generate a glass slide to be detected, comprising: Controlling the lid of the first sample storage box to open; In response to the box cover being opened, a first image is captured by a positioning camera, wherein the first image is a top view image of the first sample storage box; Determine the coordinates of the positioning point according to the first image, wherein the coordinates of the positioning point represent the position of the sample tube placement chamber corresponding to the target biological sample to be pipetted; Generate a first robot arm motion route according to the positioning point coordinates and the initial position of the liquid transfer robot arm; Controlling the liquid transfer robot arm to move to the positioning point coordinates along the movement route of the first robot arm; In response to the successful movement, the electronically controlled pipette gun is controlled by the pipetting robot arm to quantitatively aspirate the target raw sample; In response to successful suction, generating a second robot arm motion route according to the positioning point coordinates and the glass slide coordinates, wherein the glass slide coordinates represent the coordinates of the center point of the glass slide on the glass slide placement table; Controlling the liquid transfer robot arm to move to the glass slide coordinates along the motion path of the second robot arm; In response to successful movement, the electronically controlled pipette is controlled to move the sucked target raw sample onto a glass slide; In response to the successful movement, the cover glass cover slide machine is controlled to cover the cover glass on the slide to obtain the slide to be inspected.

4. The method according to claim 3, wherein: The sample detection model includes: an image enhancement model, a sample image encoding model and a text encoding model; and the sub-sample detection information for the target biological sample is generated according to the sample detection index information, the electron microscope image group and the pre-trained sample detection model, including: Performing image enhancement on each electron microscope image in the electron microscope image group by using the image enhancement model to generate an enhanced electron microscope image, thereby obtaining an enhanced electron microscope image group; Extracting image features of the enhanced electron microscope images in the enhanced electron microscope image group by using the sample image coding model to generate image features and obtain an image feature group; Performing text feature extraction on the sample detection index information through the text encoding model to generate text features; Sub-sample detection information for the target biological sample is generated according to the image feature group and the text feature.

5. The method according to claim 4, wherein: The image enhancement model includes: an image detection head group, a fusion model and an image enhancement head group; and the image enhancement model is used to perform image enhancement on each electron microscope image in the electron microscope image group to generate an enhanced electron microscope image, and the enhanced electron microscope image group is obtained, including: By using the image detection head group, shallow image feature extraction is performed on the electron microscope image group to obtain a shallow image feature group; By using a fusion model, the shallow image feature group is subjected to feature fusion to obtain fused image features; The enhanced electron microscope image group is generated according to the fused image features and the image enhancement head group.

6. A computer device, wherein: The computer device comprises a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium, wherein: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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