Sample analysis system

Abstract

The invention discloses a sample analysis system, the sample analysis system comprises an input module group, a recognition module and an output module group, and the output module group is used for setting the sample analysis system to be in a first output state in response to the fact that the recognition module does not recognize a user; in response to the identification module identifying the user, setting the sample analysis system to a second output state, the first output state being different from the second output state; and / or the output module group is used for setting the sample analysis system to be in a third output state in response to the fact that the user category identified by the identification module is the first user category; and in response to the user category identified by the identification module as a second user category, setting the sample analysis system to a fourth output state, where the first user category is different from the second user category and the third output state is different from the fourth output state. Based on the mode, the sample analysis efficiency can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of in vitro detection technology, and in particular to a sample analysis system. Background Art

[0002] In the prior art, a sample analysis system usually performs corresponding detection and analysis based on the user's needs and on the sample placed in a test tube or other container for containing the sample, so as to output the corresponding analysis results to the user after the detection and analysis is completed, thereby completing the sample analysis.

[0003] The defect of the prior art is that when the user approaches the sample analysis system, the user usually needs to input more selection instructions or other types of instructions to control the sample analysis system to enter the corresponding sample processing mode, or, after the sample analysis system completes the detection, the user usually needs to input more selection instructions or other types of instructions to obtain the part of the analysis results that needs to be viewed, which makes the user's efficiency low when using the existing sample analysis system for sample analysis. Summary of the invention

[0004] The main technical problem solved by this application is how to improve the efficiency of sample analysis.

[0005] In order to solve the above technical problems, the technical solution adopted in the present application is: a sample analysis system, characterized in that it includes an input module group, an identification module and an output module group; the input module group includes a contact sensor module group and / or a non-contact sensor module group; the contact sensor module group is used to output a first information group, the first information group is used to characterize the contact action between the user and the sample analysis system; the non-contact sensor module group is used to output a second information group, the second information group is used to characterize the non-contact action of the user; the identification module is used to determine whether a user is identified and / or determine the category of the identified user based on at least one of the first information group and the second information group; the output module group is used to: in response to the identification module not identifying the user, set the sample analysis system to a first output state; in response to the identification module identifying the user When a user is identified by the identification module, the sample analysis system is set to a second output state, and the first output state is different from the second output state; and / or the output module group is used for: in response to the user category identified by the identification module being the first user category, the sample analysis system is set to a third output state; in response to the user category identified by the identification module being the second user category, the sample analysis system is set to a fourth output state, the first user category is different from the second user category, and the third output state is different from the fourth output state; the first output state, the second output state, the third output state, and the fourth output state all include at least one of the audio output state, the indicator light output state, the display screen output state, the display screen posture state, the reagent compartment posture state, the test tube unloading position state, the test tube rack unloading position state, and the injection position state of the sample analysis system.

[0006] Among them, the sample analysis system also includes a whole machine status sensor module group, a test tube sensor module group and a test tube rack sensor module group; the whole machine status sensor module group is used to output a third information group, and the third information group is used to characterize the current device status of the sample analysis system; the test tube sensor module group is used to output a fourth information group, and the fourth information group is used to characterize whether there are test tubes in one or more specific positions of the sample analysis system; the test tube rack sensor module group is used to output a fifth information group, and the fifth information group is used to characterize whether there are test tube racks in one or more specific areas of the sample analysis system; the identification module is used to determine whether a user is identified and / or determine the identified user category based on at least one of the first information group and the second information group, and at least one of the third information group, the fourth information group and the fifth information group, or the identification module is used to determine whether a user is identified and / or determine the identified user category based on the first information group, the second information group, the third information group, the fourth information group and the fifth information group.

[0007] Wherein, when the identification module performs category identification on the user based on at least one of the first information group and the second information group, or, when the identification module performs category identification on the user based on at least one of the first information group and the second information group and at least one of the third information group, the fourth information group and the fifth information group, the identification module is also used to: determine the initial moment of the user and / or the sample analysis system when performing the current sample analysis operation based on the sample analysis system, the action moment and action type information of each action performed by the user and / or the sample analysis system in the current sample analysis operation based on at least one of the first information group and the second information group, or, based on at least one of the first information group and the second information group and at least one of the third information group, the fourth information group and the fifth information group, the current sample analysis operation includes at least one of a contact interaction action, a non-contact interaction action and an execution state of a component of the sample analysis system; determine the interval between each action moment and the initial moment to obtain multiple interval durations; generate an action feature vector corresponding to the user based on the multiple interval durations and action type information; determine the identified user category based on the action feature vector.

[0008] Among them, the recognition module is also used to: normalize the action feature vector; calculate the distance values ​​between the action feature vector and multiple preset action feature vector templates; based on multiple distance values, use statistical pattern recognition to determine that the user category corresponding to the action feature vector is the user category corresponding to the preset action feature vector template that is closest to the action feature vector.

[0009] Among them, the sample analysis system also includes a pre-training module; the pre-training module is used to: pre-acquire a preset number of sample action feature vectors; classify and normalize the preset number of sample action feature vectors; and obtain preset action feature vector templates corresponding to multiple user categories.

[0010] Among them, after the identification module determines the user category, the output module is also used to: determine, based on the user category, the interface category of the current display interface of the display screen corresponding to the current user corresponding to the user category, the content arrangement method of the display screen, and the jump time interval of the display interface of the display screen; based on the interface category of the current display interface of the display screen, the content arrangement method of the display screen, and the jump time interval of the display interface of the display screen, set the display screen in the sample analysis system to the corresponding display output state.

[0011] Among them, the first user category is a first height group category, the second user category is a second height group category, and the minimum height of people in the first height group category is greater than the maximum height of people in the second height group category; the output module group is used for: in response to the user category identified by the recognition module being the first height group category, setting the sample analysis system to the first display screen posture state and / or the first display screen output state; in response to the user category identified by the recognition module being the second height group category, setting the sample analysis system to the second display screen posture state and / or the second display screen output state; the first display screen posture state is different from the second display screen posture state, and the first display screen output state is different from the second display screen output state.

[0012] Among them, the first user category is the first user combination category, the second user category is the second user combination category, the first user combination category and the second user combination category are respectively used to represent the combination of the corresponding number of users and their corresponding user position distribution, and the first user combination category is different from the second user combination category; the output module group is used to: in response to the user category identified by the identification module being the first user combination category, the sample analysis system is set to the first test tube rack unloading position state and / or the third display screen output state; in response to the user category identified by the identification module being the second user combination category, the sample analysis system is set to the second test tube rack unloading position state and / or the fourth display screen output state; the first test tube rack unloading position state is different from the second test tube rack unloading position state, the first test tube rack unloading position state and the second test tube rack unloading position state are respectively used to indicate that the corresponding number and corresponding position of unloading positions are used for test tube unloading, and the third display screen output state is different from the fourth display screen output state.

[0013] The sample analysis system includes at least one sample analyzer, the sample analyzer includes a transport component, the transport component is movably installed in the sample analyzer, the transport component is used for transporting test tubes, and an emergency injection position is arranged on the transport component; the first user category is a non-emergency category, the second user category is an emergency category, and the output module group is used for: in response to the user category identified by the identification module as a non-emergency category, the sample analysis system is set to a first injection position state and / or a fifth display screen output state; in response to the user category identified by the identification module as an emergency category, the sample analysis system is set to a second injection position state and / or a sixth display screen output state; the sample analysis system in the first injection position state can start the non-emergency injection position for the user to perform non-emergency test tube injection when a user of the non-emergency category is identified by the identification module; the sample analysis system in the second injection position state can start the transport component when a user of the emergency category is identified by the identification module, so as to extend the emergency injection position out of the sample analyzer through the transport component, so that the user can perform emergency test tube injection through the emergency injection position; the fifth display screen output state is different from the sixth display screen output state.

[0014] Among them, the sample analysis system includes a sample analyzer, the sample analyzer includes a built-in reagent chamber and a chamber cover for closing the reagent chamber; the first user category is a maintenance user, and the second user category is a non-maintenance user; the output module group is used for: in response to the user category identified by the identification module as a maintenance user, the sample analysis system is set to the chamber cover open and / or the seventh display screen output state, and the seventh display screen output state includes a reagent replacement setting interface; in response to the user category identified by the identification module as a non-maintenance user, the sample analysis system is set to the chamber cover closed and / or the eighth display screen output state; the seventh display screen output state is different from the eighth display screen output state.

[0015] The beneficial effect of the present application is that, different from the prior art, in the technical solution of the present application, the output module group can be used to set the sample analysis system to different output states when a user is identified and when a user is not identified, or the output module group can be used to set the sample analysis system to different output states when different user categories of the user are identified. The output states may include any one or any combination of the audio output state, indicator light output state, display screen output state, display screen posture state, reagent bin posture state, test tube unloading position state, test tube rack unloading position state and injection position state of the sample analysis system. Based on the above method, when various conditions such as the presence or absence of a user or different user categories are detected, the sample analysis system can be set to output states with a higher possibility of adapting to the corresponding conditions, so that the user can realize rapid processing of sample analysis based on the sample analysis system, eliminating overly complicated operations and improving the efficiency of sample analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a structural schematic diagram of an embodiment of the sample analysis system of the present application;

[0018] Figure 2 It is a structural schematic diagram of another embodiment of the sample analysis system of the present application. DETAILED DESCRIPTION

[0019] The present application is further described in detail below in conjunction with the accompanying drawings and examples. It is particularly noted that the following examples are only used to illustrate the present application, but are not intended to limit the scope of the present application. Similarly, the following examples are only some embodiments of the present application rather than all embodiments, and all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0020] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0021] In the description of this application, it is necessary to explain that, unless otherwise clearly specified and limited, the terms "install", "set", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or connected through an intermediate medium. For ordinary technicians in this field, the above-mentioned specific meanings belonging to this application can be connected according to specific circumstances.

[0022] This application proposes a sample analysis system, see Figure 1 , Figure 1 is a schematic diagram of the structure of an embodiment of the sample analysis system of the present application. Figure 1 As shown, the sample analysis system includes an input module group 11 , an identification module 12 and an output module group 13 .

[0023] The input module group 11 includes a contact sensor module group and / or a non-contact sensor module group.

[0024] The contact sensing module group is used to output a first information group, which is used to characterize the contact action between the user and the sample analysis system. The non-contact sensing module group is used to output a second information group, which is used to characterize the non-contact action of the user.

[0025] The user may be a person who intends to use the sample analysis system / has an operating tendency, is currently using the sample analysis system, is passing by the sample analysis system, or is any other type of person who can be sensed by the contact sensing module group and / or the non-contact sensing module group, and is not limited here.

[0026] For example, the contact sensing module group may include at least one of a button, a touch key, a keyboard, a mouse, a display screen and other types of contact sensing modules. The user can input corresponding instructions to the sample analysis system through the above-mentioned contact sensing modules, such as touching the touch screen to control the sample analysis system, and also enable the sample analysis system to obtain corresponding contact action information.

[0027] The non-contact sensing module group may include at least one of an infrared sensor, a voice sensing module, an image sensing module, a video sensing module and other types of non-contact sensing modules. When a user passes by the non-contact sensing module, or approaches the non-contact sensing module, or enters the sensing range of the non-contact sensing module in other ways and performs corresponding actions (such as moving, waving limbs, making sounds or speaking, etc.), the non-contact sensing module can sense it, so that the sample analysis system can obtain the corresponding non-contact action information.

[0028] For example, the infrared sensor can be used to detect the position and displacement speed of users within its detection range. The subsequent recognition module 12 can determine that the user has an operating tendency when it detects that the distance between the user and the sample analysis system is reduced and the user's displacement speed is reduced based on the position detection and displacement speed, and set the output state of the sample analysis system based on this judgment result; otherwise, the user is determined to be a passerby with no operating tendency, and based on this judgment result, the output state of the sample analysis system is set, or the screen is kept in an off or standby state.

[0029] The voice sensor module can also acquire audio information of users within its detection range and perform corresponding voice recognition on the audio information, and determine whether the user has a work tendency based on the result of voice recognition. The result of voice recognition depends on various voice features (such as energy, short-time energy, sound intensity, sound intensity level, zero crossing rate, fundamental frequency, fundamental frequency period, pitch, etc.), and the subsequent recognition module 12 can determine that the user has a work tendency based on the voice features, and set the output state of the sample analysis system based on this judgment result; otherwise, the user is determined to be a passerby and has no work tendency, and the output state of the sample analysis system is set based on this judgment result, or the screen is kept off or in standby state.

[0030] The image sensing module can also perform face recognition on users within its detection range, and determine whether the user is a user who needs to perform sample analysis based on the result of face recognition. The subsequent recognition module 12 can determine that the user is a user who has an operating tendency when the user is a user who needs to perform sample analysis, and set the output state of the sample analysis system based on this judgment result; otherwise, the user is determined to be a passerby with no operating tendency, and based on this judgment result, the output state of the sample analysis system is set, or the screen is kept in off or standby mode.

[0031] The video sensor module can also perform gait recognition on users within its detection range, and gait recognition can be used to determine whether the corresponding user is approaching the sample analysis system. The subsequent recognition module 12 can determine that the user is a person with a working tendency according to the gait when the user is approaching the sample analysis system, and set the output state of the sample analysis system based on this judgment result; otherwise, the user is determined to be a passerby with no working tendency, and based on this judgment result, the output state of the sample analysis system is set, or the screen is kept in an off or standby state.

[0032] The above applications of various modules in the non-contact sensing module group are only a few examples. Other methods may be used to obtain corresponding non-contact action information, and / or other analysis and processing methods may be used for corresponding processing, which are not limited here.

[0033] The identification module 12 is used to determine whether a user is identified and / or determine the category of the identified user based on at least one of the first information group and the second information group.

[0034] The identification module 12 may specifically be a module with processing capabilities, such as a processing chip.

[0035] The identification module 12 may determine whether there is a user or the user category of the existing user at the sample analysis system based on the information obtained by the input module group 11 .

[0036] For example, the identification module 12 can determine whether there is a user operating the sample analysis system or is within a preset distance range of the sample analysis system based on its contact sensor module group, thereby determining whether there is a user or the user is a user with an operating tendency or a user is a passerby (with no operating tendency).

[0037] The identification module 12 can also determine the user category of the user by comprehensive processing and analysis based on the information input by the user through the contact sensor module group, or the user information perceived by the non-contact sensor module group, or other types of user information perceived by the input module group 11. The user category can specifically refer to the user's rank category (such as the director of the laboratory department or the general laboratory doctor of the laboratory department), or the user's occupation category (such as the laboratory doctor or the emergency doctor, such as the full-time doctor in charge of the biochemical equipment of the laboratory department or the full-time doctor in charge of the immunological equipment of the laboratory department or the full-time doctor in charge of the blood cell equipment of the laboratory department), or the user's physical condition category (such as height), or the user's emotional category (such as impatient and slow), or the user's habit category, or other categories, which are not limited here.

[0038] After the identification module 12 identifies the information of whether there is a user and / or the user category of the identified user, the output module group 13 can be used to:

[0039] In response to the recognition module 12 failing to recognize the user, the sample analysis system is set to the first output state.

[0040] In response to the recognition module 12 recognizing the user, the sample analysis system is set to a second output state, wherein the first output state is different from the second output state.

[0041] And / or, the output module group 13 can be used to:

[0042] In response to the user category identified by the identification module 12 being the first user category, the sample analysis system is set to the third output state.

[0043] In response to the user category identified by the identification module 12 being the second user category, the sample analysis system is set to a fourth output state, wherein the first user category is different from the second user category, and the third output state is different from the fourth output state.

[0044] Among them, the first output state, the second output state, the third output state, and the fourth output state are all output states, and a single output state among the first output state, the second output state, the third output state, and the fourth output state may include a combination of one or more of the audio output state, the indicator light output state, the display screen output state, the display screen posture state, the reagent compartment posture state, the test tube unloading position state, the test tube rack unloading position state, and the injection position state of the sample analysis system, which is not limited here. For example, the first output state may be to make the display screen in the off state, and the second output state, the third output state, and the fourth output state are to make the display screen in the bright screen state and display corresponding different contents. The above is only an example, and the specific form of expression of each output state is not limited.

[0045] For example, the audio output state may specifically refer to the output state of the sample analysis system based on the corresponding audio output module. Under different audio output states, the sample analysis system may output different types of audio.

[0046] The indicator light output state may specifically refer to the sample analysis system controlling different combinations of indicator lights to light up, flash, or go out or perform other operations under different indicator light output states through the indicator lights arranged thereon.

[0047] The display screen output state may specifically refer to the state in which the sample analysis system outputs images through the display screen. Under different display screen output states, the sample analysis system may output different types of images.

[0048] The display screen posture state may specifically refer to the posture state of the display screen of the sample analysis system relative to the user. Under different display screen posture states, the sample analysis system may set the posture of the display screen accordingly to suit users of different user types.

[0049] The reagent tank posture state may specifically refer to the posture state of the reagent tank in the sample analysis system for carrying the reagents required for sample analysis relative to the user. The reagent may include at least one of a dye reagent, a dilution reagent, a hemolysis reagent and other types of reagents, which are not limited here. In different reagent tank posture states, the sample analysis system can set the posture of the reagent tank accordingly to suit users of different user types.

[0050] The test tube unloading position state can specifically be the position for unloading the test tube for storage or for user retrieval after the sample analysis system completes the detection and analysis of the corresponding test tube. The test tube rack unloading position state can specifically be the position for unloading the test tube rack for storage or for user retrieval after the sample analysis system completes the detection and analysis of the test tube in the corresponding test tube rack. In the corresponding different unloading position states, the sample analysis system can set the posture of the test tube or test tube rack accordingly to suit users of different user types.

[0051] The injection position status may specifically be the status of different injection position combinations that the sample analysis system can provide to the user. For example, the sample analysis system may include multiple types of injection positions. Under different injection position states, all injection positions may be closed or different combinations of injection positions may be opened depending on whether there are users or different types of users.

[0052] Based on the above method, by identifying the user information at the sample analysis system (whether there is a user or the user category if there is a user), and based on the user information, the sample analysis system can provide different output states under different conditions, so that the user can quickly enter the interface of the required operation when operating the sample analysis system, or quickly complete the test tube sampling operation, or more conveniently complete the placement of the test tube or test tube rack, or more conveniently realize information acquisition and information input, thereby improving the efficiency of sample analysis when the user uses the sample analysis system.

[0053] Different from the prior art, in the technical solution of the present application, the output module group can be used to set the sample analysis system to different output states when the user is identified and when the user is not identified, or the output module group can be used to set the sample analysis system to different output states when the user category of the identified user is different. The output state may include any one or any combination of the audio output state, indicator light output state, display screen output state, display screen posture state, reagent compartment posture state, test tube unloading position state, test tube rack unloading position state and injection position state of the sample analysis system. Based on the above method, when various conditions such as the presence or absence of a user or different user categories are detected, the sample analysis system can be set to an output state with a higher possibility of adapting to the corresponding condition, so that the user can realize rapid processing of sample analysis based on the sample analysis system, eliminating overly complicated operations and improving the efficiency of sample analysis.

[0054] In one embodiment, see Figure 2 , Figure 2 is a schematic diagram of the structure of another embodiment of the sample analysis system of the present application. Figure 2 As shown, the sample analysis system may further include a whole machine state sensor module group 14 , a test tube sensor module group 15 and a test tube rack sensor module group 16 .

[0055] The whole machine state sensing module group 14 is used to output a third information group, and the third information group is used to represent the current device state of the detection sample analysis system.

[0056] The test tube sensing module group 15 is used to output a fourth information group, and the fourth information group is used to indicate whether a test tube exists at one or more specific positions of the sample analysis system.

[0057] The test tube rack sensing module group 16 is used to output a fifth information group, and the fifth information group is used to indicate whether a test tube rack exists in one or more specific areas of the sample analysis system.

[0058] The identification module 12 is used to determine whether a user is identified and / or determine the category of the identified user based on at least one of the first information group, the second information group, and at least one of the third information group, the fourth information group, and the fifth information group.

[0059] Specifically, the whole machine status sensing module group 14 can be used to perform corresponding detection on each component in the sample analysis system, so as to obtain the current status of each part of the sample analysis system, and obtain the current device status of the sample analysis system through comprehensive analysis and processing.

[0060] For example, in the sample analysis system, the whole machine status sensor module group 14 can monitor the storage volume of reagents required for sample analysis to determine whether the reagents are available or whether the reagent inventory is sufficient. It can also determine whether there is an overflow condition at the corresponding position through the overflow sensor at the corresponding position. It can also monitor whether the hole is blocked during sample transportation or detection to realize the whole machine judgment of the hole blockage. It can also monitor whether each component is dormant, faulty, idle, operating mode, executing actions and other related information. It can also monitor other parts of the sample analysis system to obtain corresponding information, and process the above information to obtain the current equipment status of the sample analysis system, so that users can understand the current status of the sample analysis system in real time, so as to better meet their needs and achieve better control of the sample analysis system, thereby improving the efficiency of sample analysis.

[0061] In addition, the output module group 13 includes at least one of an indicator light, a speaker module and other types of sensor modules. The indicator light, speaker module and other indication modules can change the operating state when the state of the corresponding component to which it is connected changes. For example, the indicator light can be controlled to light up or go out in different ways when the connected component is in different states, or different audio can be output when the connected component is in different states, so as to more intuitively show the user the current device status of the sample analysis system.

[0062] The test tube sensor module group 15 is used to output information about whether a test tube exists at one or more specific positions preset for carrying the test tube in the sample analysis system.

[0063] The test tube rack sensor module group 16 is used to output information about whether a test tube rack exists at one or more specific positions preset for carrying the test tube rack in the sample analysis system. The test tube rack may contain one or more test tubes corresponding to the same batch or different batches of sample analysis operations, which is not limited here.

[0064] In one example, if at least one of the first information group and the second information group can be used to determine that the user is a user with sample testing needs, but it is not possible to determine whether the user is specifically a user of the laboratory department or a user outside the laboratory department based on the information, then the current operating status of the sample analysis system (including whether each device is still in operation, the number of test tubes or test tube racks on the unloading position of the test tubes or test tube racks that have completed sample testing) can be further analyzed and determined based on at least one of the third information group, the fourth information group, and the fifth information group. Since the people in the laboratory department are more familiar with the testing time of various samples, the users of the laboratory department usually go to the sample analysis system to obtain the analysis results before and after the end of the sample analysis operation, while the users outside the laboratory department do not have this behavioral pattern. Therefore, data analysis can be performed on the current operating status obtained by analyzing at least one of the third information group, the fourth information group, and the fifth information group to estimate the user's behavioral pattern, so as to combine at least one of the first information group and the second information group to comprehensively judge the user category from information of multiple dimensions. Compared with judging the user category based on only at least one of the first information group and the second information group, the accuracy is greatly improved, and then the accuracy of the subsequent steps performed based on the user category can be improved, so as to ultimately improve the efficiency of sample analysis.

[0065] This can also be seen from some rules in the discipline of pattern recognition. Generally, the higher the dimension of the feature vector, the greater the difficulty of training and the stronger the feature description performance. The higher the dimension of the aforementioned action feature vector, the more information group categories it contains, and the higher the accuracy of the corresponding user category recognition.

[0066] In one embodiment, when the identification module 12 identifies the user by category based on at least one of the first information group and the second information group, or when the identification module 12 identifies the user by category based on at least one of the first information group and the second information group and at least one of the third information group, the fourth information group, and the fifth information group in the above embodiment, the identification module 12 is further configured to:

[0067] Based on at least one of the first information group and the second information group, determine the initial moment when the user and / or the sample analysis system performs the current sample analysis operation based on the sample analysis system, the action moment and action type information of each action performed by the user and / or the sample analysis system in the current sample analysis operation; or, based on at least one of the first information group and the second information group and at least one of the third information group, the fourth information group and the fifth information group, determine the initial moment when the user and / or the sample analysis system performs the current sample analysis operation based on the sample analysis system, the action moment and action type information of each action performed by the user and / or the sample analysis system in the current sample analysis operation.

[0068] The current sample analysis operation may include at least one of a contact interaction action of a user, a non-contact interaction action of a user, and an execution state of a component of the sample analysis system.

[0069] The intervals between each action moment and the initial moment are determined to obtain multiple interval durations.

[0070] Based on the multiple interval durations and action type information, an action feature vector corresponding to the user is generated.

[0071] The identified user category is determined based on the action feature vector.

[0072] Specifically, based on the initial moment when the user performs the current sample analysis operation based on the sample analysis system, and the action moment of each action performed by the user in the current sample analysis operation, the interval between each action moment and the initial moment can be determined to obtain multiple interval durations, which can be used to represent the duration between the moment of each action and the initial moment.

[0073] The interval duration of each action and the action category information can be combined to generate action feature vectors corresponding to each action to represent the feature information of each action.

[0074] The user category of the user is determined by analyzing all action feature vectors of the identified user, for example, by inputting all action feature vectors into a pre-trained neural network model.

[0075] For example, when the categories of the required information groups are different, corresponding processing can be performed based on information groups of different combinations. In the first case, the initial moment when the user and / or the sample analysis system performs the current sample analysis operation based on the sample analysis system, the action moment and action type information of each action performed by the user and / or the sample analysis system in the current sample analysis operation can be determined based on the first information group and the second information group. At this time, the current sample analysis operation may include the user's contact interaction action and the user's non-contact interaction action.

[0076] In the second case, the initial moment when the user and / or the sample analysis system performs the current sample analysis operation based on the sample analysis system, the action moment and action type information of each action performed by the user and / or the sample analysis system in the current sample analysis operation can be determined based on the third information group, the fourth information group and the fifth information group. At this time, the current sample analysis operation may include the execution status of the components of the sample analysis system.

[0077] In the third case, the initial moment when the user and / or the sample analysis system performs the current sample analysis operation based on the sample analysis system, the action moment and action type information of each action performed by the user and / or the sample analysis system in the current sample analysis operation can be determined based on the first information group, the second information group, the third information group, the fourth information group and the fifth information group. At this time, the current sample analysis operation may include the user's contact interaction action, the user's non-contact interaction action and the execution status of the components of the sample analysis system.

[0078] The above three situations are only examples and may also be combinations of other information groups to determine the initial moment when the user and / or the sample analysis system performs the current sample analysis operation based on the sample analysis system, the action moment of each action performed by the user and / or the sample analysis system in the current sample analysis operation, and the action type information, which are not limited here.

[0079] When determining the above information, the fewer the number of categories of the information groups used, the faster the data processing speed, and the greater the number of categories of the information groups used, the higher the accuracy of the data processing.

[0080] Based on the above method, by collecting appropriate information, further processing can be performed to obtain a motion feature vector containing the user's motion features, and by analyzing and processing the data of all the user's motion feature vectors, the user category can be obtained, thereby improving the accuracy of the identified user category.

[0081] Furthermore, the identification module 12 is also used for:

[0082] Normalize the action feature vector.

[0083] The distance values ​​between the action feature vector and a plurality of preset action feature vector templates are calculated respectively.

[0084] Based on the multiple distance values, statistical pattern recognition is used to determine that the user category corresponding to the action feature vector is the user category corresponding to the preset action feature vector template that is closest to the action feature vector.

[0085] The distance here can be Euclidean distance, Mahalanobis distance, Manhattan distance, neighborhood distance, cosine similarity, Chebyshev distance, Minkowski distance, Jacquard index, haversine distance, One or more of the coefficient and other distance metrics.

[0086] Specifically, after obtaining all the action feature vectors of the user, all the action feature vectors of the user may be normalized first, so that each action feature vector can be integrated to obtain a normalized integrated vector.

[0087] By setting multiple preset action feature vector templates, each preset action feature vector template corresponds to the action features of a user category, and by determining the preset action feature vector template whose vector endpoint is closest to the vector endpoint of the integrated vector among all the preset action feature vector templates, the user category corresponding to the user can be determined based on the closest preset action feature vector template.

[0088] Based on the above method, the user category of the user can be accurately determined based on the collected information in the form of quantitative feature information, which further improves the reliability of the determined user category, thereby improving the adaptability of the subsequent output state set based on the user category to the user and improving the efficiency of sample analysis.

[0089] Furthermore, the sample analysis system also includes a pre-training module.

[0090] Pre-trained modules are used for:

[0091] A preset number of sample action feature vectors are obtained in advance.

[0092] A preset number of sample action feature vectors are classified, labeled and normalized.

[0093] Preset action feature vector templates corresponding to multiple user categories are obtained.

[0094] Specifically, before calculating the distance values ​​between the above-mentioned action feature vectors and multiple preset action feature vector templates, a preset number of sample action feature vectors can be classified and marked and normalized in advance through a pre-training module to pre-generate a preset action feature vector template corresponding to each user category in the classification mark, which is used for subsequent calculation and processing with the obtained action feature vector, so that the reliability of the preset action feature vector template is higher, and the accuracy of the determined user category is higher, thereby improving the efficiency of sample analysis.

[0095] In one embodiment, after the identification module 12 determines the user category, the output module is further configured to:

[0096] Based on the user category, the interface category of the current display interface of the display screen corresponding to the current user corresponding to the user category, the content arrangement mode of the display screen, and the jump time interval of the display interface of the display screen are determined.

[0097] The display screen in the sample analysis system is set to a corresponding display output state based on the interface category of the current display interface of the display screen, the content arrangement mode of the display screen, and the jump time interval of the display interface of the display screen.

[0098] Specifically, different types of interfaces are usually required to be displayed for users of different user categories, so that users of different user categories can complete the required sample analysis operations more quickly and conveniently.

[0099] For example, for elderly users and non-elderly users, since elderly users have slower reaction speeds and poorer eyesight, the interface displayed to elderly users usually needs to be configured with a longer jump time interval, larger text or pictures, and a simpler display module layout compared to non-elderly users, so as to improve the operating efficiency of users of different age groups.

[0100] For high-level users and low-level users, since the interfaces that high-level users and low-level users usually need to consult are different, for example, high-level users usually need to enter the review interface to review related tasks, and low-level users usually need to enter the task report interface to fill in related work content, different interfaces can be displayed to users of different levels to improve work efficiency.

[0101] For maintenance personnel and non-maintenance personnel, when a fault is identified in the sample analysis system, if the user who is at or near the sample analysis system is also identified as a maintenance personnel, the maintenance processing interface of the sample analysis system can be quickly displayed so that the maintenance personnel can quickly carry out maintenance and improve maintenance efficiency.

[0102] Based on the above method, the efficiency of corresponding operations can be improved by displaying interfaces with different interface categories, content arrangement methods and jump time intervals of the display interface to users of different user categories.

[0103] In one embodiment, the first user category is a first height group category, the second user category is a second height group category, and the minimum height of people in the first height group category is greater than the maximum height of people in the second height group category.

[0104] Output module group 13 is used for:

[0105] In response to the user category identified by the identification module 12 as the first height group category, the sample analysis system is set to the first display screen posture state and / or the first display screen output state.

[0106] In response to the user category identified by the identification module 12 as the second height group category, the sample analysis system is set to the second display screen posture state and / or the second display screen output state.

[0107] Among them, the posture state of the first display screen is different from the posture state of the second display screen, and the output state of the first display screen is different from the output state of the second display screen.

[0108] Specifically, the posture state of the display screen may include the position state and tilt state of the display screen. By setting the position state and / or tilt state, the display screen can have a better display effect on the user's line of sight.

[0109] The output status of the display screen may include display screen content, brightness, and contrast. By setting one or more of the display screen content, brightness, and contrast, the display screen can have a better display effect for the user's vision.

[0110] For example, for a first height group of people with larger heights, the tilt angle of the display screen can be made larger, while for a second height group of people with smaller heights, the tilt angle of the display screen can be made smaller, so that the display screen can point its display surface toward the user's head when facing users of different heights.

[0111] In addition, the display screen output status can be set accordingly for users of different heights to achieve better display effects.

[0112] Based on the above method, the convenience of users operating the sample analysis system can be effectively improved, thereby improving the efficiency of sample analysis.

[0113] In one embodiment, the first user category is a first user combination category, and the second user category is a second user combination category. The first user combination category and the second user combination category are respectively used to represent a combination of corresponding user quantities and their corresponding user location distributions, and the first user combination category is different from the second user combination category.

[0114] Output module group 13 is used for:

[0115] In response to the user category identified by the identification module 12 being the first user combination category, the sample analysis system is set to the first test tube rack unloading position state and / or the third display screen output state.

[0116] In response to the user category identified by the identification module 12 being the second user combination category, the sample analysis system is set to the second test tube rack unloading position state and / or the fourth display screen output state.

[0117] Among them, the first test tube rack unloading position state is different from the second test tube rack unloading position state, the first test tube rack unloading position state and the second test tube rack unloading position state are respectively used to indicate that the test tubes are unloaded using the corresponding number and corresponding position of unloading positions, and the third display screen output state is different from the fourth display screen output state.

[0118] Specifically, when there are more than two users in the sample analysis system, different users have their corresponding positions, and when there are one or more users in the sample analysis system, the categories corresponding to the combinations of different numbers of users and user positions are the categories mentioned in the above-mentioned first user combination category and second user combination category.

[0119] For example, there is only one user A at position A in front of the sample analysis system, and there is only one user A at position B in front of the sample analysis system, which are different user combination categories.

[0120] There is only one user A located at position A in front of the sample analysis system, and there is only one user B located at position A in front of the sample analysis system, which are different user combination categories.

[0121] There is only one user A at position A in front of the sample analysis system, and there is one user A at position A and one user B at position B in front of the sample analysis system. These are different user combination categories.

[0122] As long as there is any difference between the two combinations, including different locations of the same user, different users at the same location, or whether the users at the same location are different, the two combinations can be considered to be different combinations.

[0123] The sample analysis system may include multiple test tube rack unloading positions, and different test tube rack unloading position combinations may be configured for different user categories, so that each type of user can complete the extraction of the test tube rack at a position closer to themselves, thereby improving the efficiency of sample analysis. In one example, the test tube rack unloading position for the job processed by a high-level user may be an unloading position closer to the corresponding low-level user, while the test tube rack unloading position for the job processed by a low-level user may be an unloading position closer to themselves.

[0124] In addition, different combinations of screens in the sample analysis system can be controlled with different display screen output states for different user combination categories, so that each user under the corresponding user combination category can view the content they need, thereby improving the efficiency of sample analysis.

[0125] In one example, the laboratory often includes multiple types of equipment, such as hematology, biochemistry, immunology, molecular diagnosis, pathology, etc. When the doctor who is responsible for the hematology equipment and the doctor who is responsible for the biochemistry equipment sit at different positions in the sample analysis system (such as an assembly line), the dedicated screens of different equipment on the assembly line will display different contents.

[0126] In one embodiment, the sample analysis system includes at least one sample analyzer, the sample analyzer includes a transport component, the transport component is movably installed in the sample analyzer, the transport component is used for the transport of the test tube, and an emergency sample injection position is provided on the transport component. The first user category is a non-emergency category, and the second user category is an emergency category.

[0127] Output module group 13 is used for:

[0128] In response to the user category identified by the identification module 12 as a non-emergency category, the sample analysis system is set to the first sample input position state and / or the fifth display screen output state.

[0129] In response to the user category identified by the identification module 12 as the emergency category, the sample analysis system is set to the second sample input position state and / or the sixth display screen output state.

[0130] The sample analysis system in the first injection position state can start the non-emergency injection position to allow the user to inject non-emergency test tubes when the identification module 12 identifies a user of the non-emergency category. The sample analysis system in the second injection position state can start the transport component to extend the emergency injection position out of the sample analyzer through the transport component when the identification module 12 identifies a user of the emergency category, so that the user can inject emergency test tubes through the emergency injection position.

[0131] Moreover, the identification module 12 can set different lengths of the emergency injection position extending out of the sample analyzer according to different identified users. That is, the identification module 12 can determine the length of the emergency injection position extending out of the sample analyzer corresponding to the user based on the identified user, thereby further facilitating different users to perform emergency test tube sampling through the emergency injection position.

[0132] The fifth display screen output state is different from the sixth display screen output state.

[0133] Specifically, in practice, the emergency sample injection position of the sample analyzer is usually in a closed state or a hidden state. Users of the emergency category usually need to perform corresponding control operations on the sample analyzer to open the emergency sample injection position, which is inefficient. However, based on the above-mentioned method of the present application, when the identification module 12 identifies that the user category is the emergency category, the user does not need to input any instructions, and the transport component can be abandoned to extend the emergency sample injection position out of the sample analyzer, so that users of the emergency category can perform emergency test tube injection, thereby improving the timeliness and efficiency of the sample analysis of the emergency test tubes.

[0134] In addition, when an emergency user is identified, the emergency-related operation interface can be displayed directly through the display screen output status setting, compared with non-emergency users, thereby further improving the efficiency of sample analysis.

[0135] In one embodiment, the sample analysis system includes a sample analyzer, and the sample analyzer includes a built-in reagent chamber and a chamber cover for closing the reagent chamber. The first user category is a maintenance user, and the second user category is a non-maintenance user.

[0136] Output module group 13 is used for:

[0137] In response to the user category identified by the identification module 12 being a maintenance user, the sample analysis system is set to a compartment cover open state and / or a seventh display screen output state, and the seventh display screen output state includes a reagent replacement setting interface.

[0138] In response to the user category identified by the identification module 12 as a non-maintenance user, the sample analysis system is set to a compartment cover closed state and / or an eighth display screen output state.

[0139] Among them, the output state of the seventh display screen is different from the output state of the eighth display screen.

[0140] Specifically, when the whole machine status sensing module group 14 senses that the reserves of one or more reagents in the reagent compartment are lower than the corresponding preset reserve threshold, and at the same time the identification module 12 identifies that the user approaching the sample analysis system is a maintenance user, the compartment cover of the sample analysis system is opened, and / or the corresponding reagent compartment maintenance interface is displayed, so that the maintenance user can quickly replace or replenish the reagents, thereby reducing the possibility of errors in the sample analysis operation due to insufficient reagents.

[0141] Optionally, when it is identified that the reserves of one or more reagents in the reagent compartment are lower than the corresponding preset reserve threshold, and the user approaching the sample analysis system is a maintenance user, and the maintenance user carries a reagent storage container and / or the displacement speed of the maintenance user is lower than the preset displacement speed threshold, the compartment cover of the sample analysis system may be opened, and / or the corresponding reagent compartment maintenance interface may be displayed, so that the maintenance user can quickly replace or replenish the reagents, thereby reducing the possibility of sample analysis operation errors due to insufficient reagents, and reducing the possibility of maintenance personnel opening the compartment cover when they are not performing reagent replacement or replenishment operations, thereby reducing the maintenance efficiency of the maintenance personnel.

[0142] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradicting each other.

[0143] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0144] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0145] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or system (which can be a personal computer, server, network system or other system that can fetch instructions from the instruction execution system, device or system and execute the instructions), or in combination with these instruction execution systems, devices or systems. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by or in combination with these instruction execution systems, devices or systems. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0146] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A sample analysis system, characterized in that: It includes an input module group, an identification module and an output module group; The input module group includes a contact sensing module group and / or a contactless sensing module group; The contact sensing module group is used to output a first information group, and the first information group is used to represent the contact action between the user and the sample analysis system; The non-contact sensing module group is used to output a second information group, and the second information group is used to characterize the non-contact action of the user; The identification module is used to determine whether a user is identified and / or determine the category of the identified user based on at least one of the first information group and the second information group; The output module group is used for: In response to the recognition module failing to recognize the user, setting the sample analysis system to a first output state; In response to the recognition module recognizing the user, setting the sample analysis system to a second output state, wherein the first output state is different from the second output state; and / or, The output module group is used for: In response to the user category identified by the identification module being the first user category, setting the sample analysis system to a third output state; In response to the user category identified by the identification module being a second user category, setting the sample analysis system to a fourth output state, wherein the first user category is different from the second user category, and the third output state is different from the fourth output state; The first output state, the second output state, the third output state, and the fourth output state all include at least one of the audio output state, the indicator light output state, the display screen output state, the display screen posture state, the reagent compartment posture state, the test tube unloading position state, the test tube rack unloading position state, and the sample injection position state of the sample analysis system.

2. The sample analysis system according to any one of claim 1, characterized in that: The sample analysis system also includes a whole machine status sensor module group, a test tube sensor module group and a test tube rack sensor module group; The whole machine state sensing module group is used to output a third information group, and the third information group is used to characterize and detect the current device state of the sample analysis system; The test tube sensing module group is used to output a fourth information group, and the fourth information group is used to indicate whether a test tube exists at one or more specific positions of the sample analysis system; The test tube rack sensor module group is used to output a fifth information group, and the fifth information group is used to indicate whether a test tube rack exists in one or more specific areas of the sample analysis system; The identification module is used to determine whether a user is identified and / or determine the category of the identified user based on at least one of the first information group, the second information group, and at least one of the third information group, the fourth information group, and the fifth information group.

3. The sample analysis system according to claim 1 or 2, characterized in that: When the identification module performs category identification on the user based on at least one of the first information group and the second information group, or when the identification module performs category identification on the user based on at least one of the first information group and the second information group and at least one of the third information group, the fourth information group, and the fifth information group, the identification module is further used to: Based on at least one of the first information group and the second information group, or based on at least one of the first information group and the second information group and at least one of the third information group, the fourth information group, and the fifth information group, determine an initial moment when the user and / or the sample analysis system performs a current sample analysis operation based on the sample analysis system, an action moment and action type information of each action performed by the user and / or the sample analysis system in the current sample analysis operation, wherein the current sample analysis operation includes at least one of the contact interaction action, the non-contact interaction action, and an execution state of a component of the sample analysis system; Determine the interval between each of the action moments and the initial moment to obtain multiple interval durations; Based on the plurality of interval durations and the action type information, generating an action feature vector corresponding to the user; The identified user category is determined based on the action feature vector.

4. The sample analysis system according to claim 3, characterized in that: The identification module is also used for: Normalizing the action feature vector; Calculating distance values ​​between the action feature vector and a plurality of preset action feature vector templates respectively; Based on the multiple distance values, statistical pattern recognition is used to determine that the user category corresponding to the action feature vector is the user category corresponding to the preset action feature vector template that is closest to the action feature vector.

5. The sample analysis system according to claim 4, characterized in that: The sample analysis system also includes a pre-training module; The pre-training module is used to: Obtaining a preset number of sample action feature vectors in advance; Classifying, labeling and normalizing the preset number of sample motion feature vectors; The preset action feature vector templates respectively corresponding to multiple user categories are obtained.

6. The sample analysis system according to any one of claims 1, 2, 4 and 5, characterized in that: After the identification module determines the user category, the output module is further used to: Based on the user category, determine the interface category of the current display interface of the display screen corresponding to the current user corresponding to the user category, the content arrangement mode of the display screen, and the jump time interval of the display interface of the display screen; The display screen in the sample analysis system is set to a corresponding display output state based on the interface category of the current display interface of the display screen, the content arrangement mode of the display screen, and the jump time interval of the display interface of the display screen.

7. The sample analysis system according to any one of claims 1, 2, 4 and 5, characterized in that: The first user category is a first height group category, the second user category is a second height group category, and the minimum height of people in the first height group category is greater than the maximum height of people in the second height group category; The output module group is used for: In response to the user category identified by the identification module being the first height group category, setting the sample analysis system to a first display screen posture state and / or a first display screen output state; In response to the user category identified by the identification module being the second height group category, setting the sample analysis system to a second display screen posture state and / or a second display screen output state; Among them, the first display screen posture state is different from the second display screen posture state, and the first display screen output state is different from the second display screen output state.

8. The sample analysis system according to any one of claims 1, 2, 4 and 5, characterized in that: The first user category is a first user combination category, and the second user category is a second user combination category. The first user combination category and the second user combination category are respectively used to represent a combination of the number of corresponding users and the distribution of their corresponding user positions. The first user combination category is different from the second user combination category. The output module group is used for: In response to the user category identified by the identification module being the first user combination category, setting the sample analysis system to a first test tube rack unloading position state and / or a third display screen output state; In response to the user category identified by the identification module being the second user combination category, setting the sample analysis system to a second test tube rack unloading position state and / or a fourth display screen output state; Among them, the first test tube rack unloading position state is different from the second test tube rack unloading position state, the first test tube rack unloading position state and the second test tube rack unloading position state are respectively used to indicate that the test tubes are unloaded using the corresponding number and corresponding positions of unloading positions, and the third display screen output state is different from the fourth display screen output state.

9. The sample analysis system according to any one of claims 1, 2, 4 and 5, characterized in that: The sample analysis system comprises at least one sample analyzer, the sample analyzer comprises a transport component, the transport component is movably installed in the sample analyzer, the transport component is used for transporting test tubes, and an emergency sample injection position is arranged on the transport component; the first user category is a non-emergency category, the second user category is an emergency category, The output module group is used for: In response to the user category identified by the identification module being the non-emergency category, setting the sample analysis system to a first sample feed position state and / or a fifth display screen output state; In response to the user category identified by the identification module being the emergency category, setting the sample analysis system to a second sample feed position state and / or a sixth display screen output state; Wherein, when the user of the non-emergency category is identified by the identification module, the sample analysis system in the first sample injection position state can start the non-emergency sample injection position for the user to inject non-emergency test tubes; when the user of the emergency category is identified by the identification module, the sample analysis system in the second sample injection position state can start the transport component to extend the emergency sample injection position out of the sample analyzer through the transport component, so that the user can inject emergency test tubes through the emergency sample injection position; The fifth display screen output state is different from the sixth display screen output state.

10. The sample analysis system according to any one of claims 1, 2, 4 and 5, characterized in that: The sample analysis system includes a sample analyzer, and the sample analyzer includes a built-in reagent chamber and a chamber cover for closing the reagent chamber; the first user category is a maintenance user, and the second user category is a non-maintenance user; The output module group is used for: In response to the user category identified by the identification module being the maintenance user, the sample analysis system is set to the state where the compartment cover is open and / or the seventh display screen output state includes a reagent replacement setting interface; In response to the user category identified by the identification module being the non-maintenance user, setting the sample analysis system to the state where the compartment cover is closed and / or the eighth display screen is output; Among them, the output state of the seventh display screen is different from the output state of the eighth display screen.

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