A method for automatically cleaning and detecting common laboratory equipment

Abstract

The present invention discloses an automatic cleaning and detection method for general laboratory equipment, which relates to the field of automated cleaning. The method comprises: obtaining a mixed cleaning mechanism; parsing a reagent application label of a first equipment to generate first reagent residual information; calling a cleaning attribute database to perform detergent matching and cleaning parameter analysis to generate a multi-stage cleaning scheme; establishing a cleaning transmission path to transmit the first equipment to a corresponding cleaning module in the multi-stage cleaning scheme for cleaning, and after completion, transmitting the first equipment to an automatic detection platform; performing spectral detection, performing cleanliness detection based on a first detection spectrum, and sending the equipment to a storage area when the cleanliness detection passes. The method solves the technical problem that the existing cleaning and detection of general laboratory equipment lacks intelligent management, resulting in insufficient efficiency and accuracy of cleaning and detection, and achieves the technical effect of improving the efficiency and accuracy of cleaning and detection of general laboratory equipment.

Description

Technical Field

[0001] The present application relates to the field of automated cleaning, and in particular to an automated cleaning and detection method for general laboratory equipment. Background Art

[0002] In scientific research and laboratory work, the cleaning and testing of laboratory equipment is a key link to ensure the accuracy of experimental data. Laboratory equipment, such as test tubes, beakers, pipettes, etc., will leave a variety of chemical reagents during frequent use. These residues may not only contaminate subsequent experiments, but also threaten the health of experimenters. Therefore, efficient and accurate equipment cleaning and testing methods are essential. At present, the cleaning of laboratory equipment mostly relies on manual operation, including manual selection of detergents, control of cleaning time and process, etc. This method is not only inefficient, but also due to human factors, the cleaning effect is often difficult to ensure consistency. In addition, for complex residues, manual selection of detergents may lack scientific basis, resulting in incomplete cleaning or the introduction of new pollutants. In terms of detection, traditional methods mostly rely on visual inspection or simple chemical reagent testing. These methods have problems such as strong subjectivity, low sensitivity, and easy misjudgment. In particular, for the detection of trace residues, traditional methods are often powerless.

[0003] In the current relevant technologies, there is a lack of intelligent management in the cleaning and testing of general laboratory instruments, which leads to technical problems such as insufficient efficiency and accuracy of cleaning and testing. Summary of the invention

[0004] The present application provides an automatic cleaning and inspection method for general laboratory instruments, analyzes the reagent application labels of the instruments to be cleaned, generates reagent residue information, matches cleaning agents and analyzes cleaning parameters based on a cleaning attribute database, generates a multi-stage cleaning plan, and transports the instruments to the corresponding cleaning modules for cleaning via an automatic transport mechanism. An automatic inspection platform is used to perform spectral inspection of the cleaned instruments to ensure that the cleanliness meets the standards, and other technical means are used. By automatically recording the cleaning process and inspection results, intelligent management is achieved, thereby achieving the technical effect of improving the efficiency and accuracy of cleaning and inspection of general laboratory instruments.

[0005] The present application provides a method for automatic cleaning and detection of general laboratory instruments, comprising:

[0006] Acquire a mixed cleaning mechanism of a target laboratory, wherein the mixed cleaning mechanism includes multiple cleaning modules corresponding to multiple cleaning agents, and each cleaning agent has a cleaning attribute database; parse the reagent application label of a first instrument to be cleaned to generate first reagent residual information; call the cleaning attribute database, perform cleaning agent matching and cleaning parameter analysis based on the first reagent residual information, and generate a multi-stage cleaning scheme; establish a cleaning transmission path through the multi-stage cleaning scheme, and convey the first instrument to the corresponding cleaning module in the multi-stage cleaning scheme according to the cleaning transmission path through an automatic conveying mechanism for cleaning, and when the multi-stage cleaning scheme is completed, convey the first instrument to an automatic detection platform; perform spectral detection on the first instrument through the automatic detection platform, and perform cleanliness detection based on the first detection spectrum, and when the cleanliness detection passes, convey the first instrument to a storage area.

[0007] In a possible implementation, the cleaning attribute database is called, cleaning agent matching and cleaning parameter analysis are performed based on the first reagent residue information, a multi-stage cleaning scheme is generated, and the following processing is performed:

[0008] Based on the first reagent residual information, the residual reagent attributes are identified to generate multiple attribute information of multiple residual reagents; the multiple attribute information is input into the cleaning attribute database for traversal matching to generate multiple mapping results about residual reagents and cleaning agents; based on the multiple mapping results, the cleaning solution concentration and the cleaning reaction time are configured to generate the multi-stage cleaning plan.

[0009] In a possible implementation, the cleaning solution concentration and the cleaning reaction time are configured based on the multiple mapping results, the multi-stage cleaning scheme is generated, and the following processing is performed:

[0010] Extract the first cleaning agent and multiple matching residual reagents in the first mapping result; perform wall analysis on the first utensil based on the multiple matching residual reagents to generate multiple predicted residual amounts; call the historical cleaning record data of the first cleaning agent, configure the cleaning solution concentration and the cleaning reaction time based on the multiple predicted residual amounts, and generate a first cleaning plan; perform sequence impact analysis on the cleaning agents corresponding to the multiple mapping results to generate a cleaning agent sequence; combine the cleaning agent sequence and the first cleaning plan to generate the multi-stage cleaning plan.

[0011] In a possible implementation, the first device is subjected to wall analysis based on the multiple matching residual reagents to generate multiple predicted residual amounts, and the following processing is performed:

[0012] The viscosity of the multiple matching residual reagents is identified to generate a reagent viscosity index; the size of the first utensil is identified, and the size identification result and the reagent viscosity index are input into a wall hanging prediction network for prediction analysis to generate the multiple predicted residual amounts, wherein the wall hanging prediction network is trained by calling a prediction sample library; the prediction sample library includes multiple groups of prediction samples, and any prediction sample includes a reagent viscosity index sample, a utensil size sample and corresponding identification information including the wall hanging amount.

[0013] In a possible implementation, a cleaning transport path is established through the multi-stage cleaning scheme, and the following processes are performed:

[0014] Positioning multiple target cleaning modules corresponding to the multi-stage cleaning scheme in the mixed cleaning mechanism; acquiring a transmission path connection network of the mixed cleaning mechanism, performing segmented path positioning on the multiple target cleaning modules according to the stage sequence of the multi-stage cleaning scheme, and generating the cleaning transmission path; wherein the transmission path connection network includes transmission paths between the multiple cleaning modules, and the segmented path endpoints in the cleaning transmission path are cleaning tanks of the multiple target cleaning modules.

[0015] In a possible implementation, the first device is subjected to spectrum detection by the automatic detection platform, and cleanliness detection is performed based on the first detection spectrum. When the cleanliness detection passes, the first device is sent to a storage area, and the following processing is performed:

[0016] The automatic detection platform includes a spectral detection module and a spectral analysis module, wherein the spectral detection module includes an infrared spectrometer and an ATR accessory; the first device is dried and then transferred to the ATR accessory, and the infrared spectrometer is started according to preset spectral irradiation conditions to generate the first detection spectrum; the first detection spectrum is subjected to cleanliness detection by the spectral analysis module.

[0017] In a possible implementation, the spectrum analysis module performs cleanliness detection on the first detection spectrum and performs the following processing:

[0018] An absorption spectrum database of instrument samples that have passed the cleanliness test is established, wherein the absorption spectrum database includes absorption spectra corresponding to a plurality of instruments and has a spectrum acquisition condition identifier; the absorption spectrum database is called by the spectrum analysis module to match the absorption spectrum corresponding to the first instrument as a first preset spectrum, and the corresponding spectrum acquisition condition identifier is obtained to generate the preset spectrum irradiation condition; a deviation comparison is performed between the first preset spectrum and the first detection spectrum, and if the spectrum deviation meets the preset deviation, the cleanliness test is passed.

[0019] In a possible implementation, the following processing is performed:

[0020] When the cleanliness test fails, the absorption peak structure of the functional group is extracted based on the first detection spectrum; the cleaning residues are identified based on the absorption peak structure and the first reagent residue information to generate second reagent residue information; based on the second reagent residue information, detergent matching, cleaning parameter analysis and equipment cleaning are performed again until the cleanliness test passes.

[0021] An automatic cleaning and detection method for general laboratory instruments proposed in this application first obtains a mixed cleaning mechanism of the target laboratory, wherein the mixed cleaning mechanism includes multiple cleaning modules corresponding to multiple cleaning agents, and each cleaning agent has a cleaning attribute database, then the reagent application label of the first instrument to be cleaned is parsed to generate first reagent residual information, then the cleaning attribute database is called, cleaning agent matching and cleaning parameter analysis are performed based on the first reagent residual information, and a multi-stage cleaning scheme is generated, and then a cleaning transmission path is established through the multi-stage cleaning scheme, and the first instrument is conveyed to the corresponding cleaning module in the multi-stage cleaning scheme according to the cleaning transmission path through the automatic conveying mechanism for cleaning, when the multi-stage cleaning scheme is completed, the first instrument is conveyed to the automatic detection platform, and finally the first instrument is spectrally detected by the automatic detection platform, and the cleanliness detection is performed based on the first detection spectrum, and when the cleanliness detection passes, the first instrument is sent to the storage area, thereby achieving the technical effect of improving the efficiency and accuracy of cleaning and detection of general laboratory instruments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the method according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0023] Figure 1 A schematic flow chart of an automatic cleaning and testing method for common laboratory instruments provided in an embodiment of the present application.

[0024] Figure 2 A schematic diagram of a flow chart for generating a multi-stage cleaning scheme in a method for automatically cleaning and detecting common laboratory instruments provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0026] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0027] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0028] The present application embodiment provides a method for automatically cleaning and detecting common laboratory instruments, such as Figure 1 As shown, the method includes:

[0029] Step S100, obtaining the mixed cleaning mechanism of the target laboratory, wherein the mixed cleaning mechanism includes multiple cleaning modules corresponding to multiple cleaning agents, and each cleaning agent has a cleaning attribute database. Specifically, the laboratory management system starts, checks whether the network connection with the mixed cleaning mechanism is stable, initializes the communication protocol with the mixed cleaning mechanism, and ensures that both parties can exchange data and control instructions normally. The mixed cleaning mechanism is an automated cleaning device that integrates multiple cleaning agents (such as dilute acid, dilute alkali, sodium dodecyl sulfate, etc.), and can select suitable cleaning agents for cleaning according to different reagent residues to improve cleaning efficiency and effect. The configuration information of the mixed cleaning mechanism is read from the configuration file of the laboratory management system, including the number, type (such as ultrasonic cleaning, spray cleaning, etc.), position layout, type of each cleaning agent, storage location, and corresponding cleaning attribute database location of the cleaning module. The cleaning module is an independent cleaning unit in the mixed cleaning mechanism, and each unit is equipped with a specific cleaning agent and cleaning method to complete a specific type of cleaning task. The cleaning attribute database is a database that stores the cleaning characteristics of each cleaning agent, including the cleaning ability, applicable scope, optimal use conditions (such as concentration, temperature, time, etc.), compatibility with other chemicals, etc. of each cleaning agent, which is used to guide the selection of cleaning agents and the setting of cleaning parameters. Send a status query command to the mixed cleaning mechanism to check whether it is in standby or operational state, and confirm whether each cleaning module is ready, including checking whether the cleaning agent supply is sufficient, whether the cleaning tank is clean and free of impurities, and whether the mechanical parts are operating normally. If any problems are found, such as insufficient cleaning agent or equipment failure, an alarm is issued and the maintenance personnel are prompted to handle it.

[0030] Step S200, the reagent application label of the first utensil to be cleaned is parsed to generate the first reagent residual information. Specifically, the reagent application label pasted on the first utensil is scanned using a device such as an RFID reader, a barcode scanner or a two-dimensional code scanner. The reagent application label is a label pasted on the laboratory utensil to identify the reagent type, concentration, use date, utensil number and other information used by the utensil last time, and is in the form of RFID, barcode or two-dimensional code, so as to facilitate quick reading and identification. The scanning device reads the data on the reagent application label and converts it into a computer-recognizable format (such as numbers, text, etc.). The laboratory management system receives the label information sent by the scanning device and performs parsing processing. During the parsing process, key data related to the reagent residue is extracted from the label information according to the preset rules and formats. According to the parsed information such as the reagent type and concentration, the first reagent residual information (the information remaining on the surface of the laboratory utensil due to contact with various chemical reagents during use, including the reagent type, concentration, degree of contamination, etc.) is generated, and the first reagent residual information is used for subsequent detergent matching and cleaning parameter analysis.

[0031] Step S300, calling the cleaning attribute database, performing cleaning agent matching and cleaning parameter analysis based on the first reagent residual information, and generating a multi-stage cleaning scheme. Specifically, from the first reagent residual information, the type, concentration, possible degree of contamination and other information of the residual reagent are analyzed in detail. Access the cleaning attribute database of each cleaning agent in the mixed cleaning mechanism, which contains key parameters such as the cleaning ability, optimal use concentration, temperature requirements, and cleaning time of various cleaning agents for different reagents. According to the analyzed reagent residual information, find the cleaning agent that can effectively remove these residual reagents in the cleaning attribute database. In order to achieve the best cleaning effect, multiple cleaning agents can be used in combination. After determining the cleaning agent, further analyze the cleaning parameters, including the concentration of the cleaning agent, the cleaning time, the cleaning temperature, whether pre-soaking is required, whether subsequent rinsing is required, etc. The setting of these parameters is based on the recommended values ​​in the cleaning attribute database, and is adjusted in combination with factors such as the material, shape, and degree of contamination of the actual appliance. Combining the above information, a cleaning protocol is generated that contains multiple cleaning stages (e.g., pre-soak, main wash, rinse, dry, etc.), each of which specifies the cleaning agents used, cleaning parameters, and additional steps that may be required, with specific goals and requirements.

[0032] In a possible implementation, the cleaning attribute database is called, and cleaning agent matching and cleaning parameter analysis are performed based on the first reagent residual information to generate a multi-stage cleaning scheme. Step S300 further includes step S310, which performs residual reagent attribute identification based on the first reagent residual information to generate multiple attribute information of multiple residual reagents. Specifically, the type of each residual reagent (such as organic matter, inorganic matter, grease, protein, etc.) is extracted from the first reagent residual information, and the specific chemical properties, stability, solubility and other properties of each type of residual reagent are further analyzed, and this information is organized into structured data, including reagent name, type, chemical properties, possible interaction reactions, etc., to generate multiple attribute information of multiple residual reagents. Step S320, the multiple attribute information is input into the cleaning attribute database for traversal matching, and multiple mapping results about residual reagents and cleaning agents are generated. Specifically, the generated multiple attribute information is input into the cleaning attribute database, and the database traverses all available cleaning agents based on these attribute information to find cleaning agents that match the attributes of each residual reagent. Record the results of each match, including the type of matched detergent, the recommended concentration, and the required special conditions (such as temperature, pH value, etc.), and generate one or more mapping results about residual reagents and detergents, each of which contains one or more recommended usage plans for detergents. Step S330, configure the cleaning solution concentration and the cleaning reaction time based on the multiple mapping results to generate the multi-stage cleaning plan. Specifically, analyze the multiple mapping results generated, combine the advantages and disadvantages, compatibility, and feasibility of actual operation of each detergent, select the most suitable detergent combination, and determine the order of use of each detergent. Configure the use concentration, temperature, pH value and other parameters for each detergent, as well as the expected cleaning reaction time. Integrate this information into a multi-stage cleaning plan, and each stage details the detergent used, parameter settings, and expected effects. This implementation method accurately analyzes the difficulties and key points of cleaning by identifying the properties of residual reagents, realizes the targeted selection of detergents, and achieves the technical effect of improving the accuracy of detergent matching.

[0033] like Figure 2As shown, in a possible implementation, the cleaning solution concentration and the cleaning reaction time are configured based on the multiple mapping results to generate the multi-stage cleaning scheme, and step S330 further includes step S331, extracting the first cleaning agent and multiple matching residual reagents in the first mapping result. Specifically, any one of the multiple mapping results is extracted as the first mapping result, and this mapping result contains the first cleaning agent that matches the residual reagent attribute, and the matching residual reagent is the reagent remaining on the first utensil. Step S332, based on the multiple matching residual reagents, the first utensil is subjected to wall hanging analysis to generate multiple predicted residual amounts. Specifically, the wall hanging analysis is the process of analyzing the residual situation of the chemical reagent on the inner wall of the first utensil. The phenomenon that the liquid adheres to the solid surface and forms a film or residue is called wall hanging. The wall hanging analysis model (which can be a model based on experimental data, historical records or theoretical calculations) can be used to predict the residual amount that may be formed on the surface of the utensil according to the identified multiple residual reagents, that is, the predicted residual amount, which is used to determine the required cleaning intensity. Step S333, calling the historical cleaning record data of the first cleaning agent, configuring the cleaning solution concentration and the cleaning reaction time based on the multiple predicted residual amounts, and generating a first cleaning scheme. Specifically, query the cleaning effect record of the first cleaning agent under similar conditions in the past, and the historical cleaning record data includes information such as the cleaning effect of the cleaning agent under different concentrations, temperatures, pH values, etc., and the potential impact on the equipment. Combined with the current predicted residual amount, through data analysis and model prediction, adjust the concentration and reaction time of the first cleaning agent, and generate a first cleaning scheme for the current situation. The first cleaning scheme aims to maximize the cleaning effect and minimize damage to the equipment. Step S334, perform a sequential impact analysis on the cleaning agents corresponding to the multiple mapping results, and generate a cleaning agent sequence. Specifically, considering the interaction between different cleaning agents (such as chemical reaction, precipitation formation, etc.), analyze the corresponding cleaning agents in the multiple mapping results, evaluate the potential impact of the use order of different cleaning agents on the cleaning effect and the safety of the equipment, and comprehensively consider the compatibility, cleaning effect, cost and other factors of the cleaning agents to determine the order of use of the cleaning agents to maximize the cleaning effect and reduce potential adverse effects. Step S335, combining the detergent sequence and the first cleaning scheme, generates the multi-stage cleaning scheme. Specifically, the first cleaning scheme generated in step S333 is combined with the detergent sequence determined in step S334 to form a complete cleaning scheme containing multiple stages (each stage uses different detergents or the same detergent at different concentrations). This scheme details the detergents to be used in each stage, configuration parameters (such as concentration), cleaning reaction time, and expected effects, so as to gradually remove various residues on the equipment and achieve the purpose of thorough cleaning. This implementation method takes into account the compatibility of detergents and their potential impact on equipment, and achieves the technical effect of ensuring the safety and effectiveness of the cleaning process by performing sequential impact analysis.

[0034] In a possible implementation, the first utensil is subjected to wall hanging analysis based on the multiple matching residual reagents to generate multiple predicted residual amounts, and step S332 further includes step S3321, performing viscosity identification on the multiple matching residual reagents to generate a reagent viscosity index. Specifically, viscosity is a physical quantity that describes the resistance to liquid flow. For residual reagents in the cleaning process, its viscosity will affect the ease with which it adheres to the surface of the utensil. Viscosity measurement is performed on each matched residual reagent or known viscosity data is searched to generate a corresponding reagent viscosity index. Step S3322, the first utensil is subjected to size identification, and the size identification result and the reagent viscosity index are input into the wall hanging prediction network for prediction analysis to generate the multiple predicted residual amounts, wherein the wall hanging prediction network is trained by calling a prediction sample library, the prediction sample library includes multiple groups of prediction samples, and any prediction sample includes a reagent viscosity index sample, a utensil size sample, and corresponding identification information containing the wall hanging amount. Specifically, the size information of the first utensil, including surface area, shape complexity, etc., is obtained by image recognition, three-dimensional scanning or other technical means. The identified instrument size and reagent viscosity index are input into the trained wall hanging prediction network, and the network outputs the predicted residual amount of each residual reagent on the surface of the instrument. Among them, the wall hanging prediction network is a prediction model based on machine learning, which is used to predict the amount of residual reagent hanging on the surface of the instrument based on the viscosity index of the reagent and the size information of the instrument. During the training phase, the wall hanging prediction network will call a prediction sample library containing multiple groups of prediction samples, which include reagent viscosity index samples, instrument size samples, and corresponding identification information containing the amount of wall hanging. Through continuous iterative training, the network can learn the complex relationship between viscosity, size and wall hanging. This implementation method establishes a complex relationship model between the physical properties (viscosity) of the residual reagent and the geometric characteristics (size) of the instrument and the amount of wall hanging through the method of machine learning. Compared with traditional empirical judgment or simple rules, this method can more accurately predict the wall hanging of the residual reagent on the surface of the instrument, and achieve the technical effect of improving the efficiency and accuracy of wall hanging analysis.

[0035] Step S400, a cleaning transmission path is established through the multi-stage cleaning scheme, and the first utensil is transmitted to the corresponding cleaning module in the multi-stage cleaning scheme for cleaning through the automatic transmission mechanism according to the cleaning transmission path. When the multi-stage cleaning scheme is completed, the first utensil is transmitted to the automatic detection platform. Specifically, according to the generated multi-stage cleaning scheme, the parameters such as the type of cleaning agent, cleaning time, temperature, etc. of each stage are read. According to the layout of the cleaning module and the requirements of the multi-stage cleaning scheme, the optimal transmission path from the starting position (such as the temporary storage area of ​​the utensil) to each cleaning module and then to the final automatic detection platform is automatically planned. Once the path planning is completed, the automatic transmission mechanism (mechanical equipment used to automatically transport and transmit laboratory instruments within the laboratory, such as a conveyor belt, a robotic arm, etc.) starts to move according to the planned path, takes out the first utensil to be cleaned from the starting position, and according to the order of the multi-stage cleaning scheme, the automatic transmission mechanism transmits the utensil one by one to the cleaning modules of each stage. In each cleaning module, according to the instructions of the multi-stage cleaning program, the corresponding cleaning agent is used, and the appropriate temperature and cleaning time are set to clean the utensils. The cleaning modules include spray devices, immersion tanks, ultrasonic cleaning machines, etc. During the cleaning process, each cleaning module is equipped with a sensor to monitor parameters such as the concentration, temperature, and cleaning time of the cleaning agent to ensure that the cleaning process meets the preset requirements. If any abnormality is detected (such as insufficient cleaning agent concentration, too high or too low temperature), an alarm will be immediately issued and the cleaning process will be suspended, waiting for manual intervention or automatic adjustment to normal status. After all cleaning stages are completed, the automatic conveying mechanism removes the utensils from the last cleaning module and conveys them to the automatic detection platform according to the planned path.

[0036] In a possible implementation, a cleaning transmission path is established through the multi-stage cleaning scheme, and step S400 further includes step S410, positioning multiple target cleaning modules corresponding to the multi-stage cleaning scheme in the mixed cleaning mechanism. Specifically, read the generated multi-stage cleaning scheme to determine the type of cleaning modules required for each stage and their order. According to the requirements in the multi-stage cleaning scheme, traverse all cleaning modules in the mixed cleaning mechanism, find the target cleaning modules that match each cleaning stage and are used to perform the cleaning tasks of the stage, and arrange all the matched target cleaning modules in the order of the cleaning scheme to form a target cleaning module list. Step S420, obtain the transmission path connection network of the mixed cleaning mechanism, locate the multiple target cleaning modules in segmented paths according to the stage order of the multi-stage cleaning scheme, and generate the cleaning transmission path, wherein the transmission path connection network includes the transmission paths between the multiple cleaning modules, and the segmented path endpoints in the cleaning transmission path are the cleaning tanks of the multiple target cleaning modules. Specifically, the transmission path connection network of the hybrid cleaning mechanism is obtained from a database or configuration file. This network describes the physical connection relationship between each cleaning module, including the transmission paths of conveyor belts, tracks, robotic arms, etc. According to the order of the target cleaning module list, a graph theory algorithm (such as Dijkstra algorithm, A* algorithm, etc.) is used to find the shortest or optimal path from the starting position (such as the equipment temporary storage area) to the first target cleaning module, then to each subsequent target cleaning module, and finally to the automatic detection platform in the transmission path connection network. These paths are divided into multiple segments, and each segmented path refers to a partial path in the cleaning transmission path that connects two adjacent target cleaning modules (or the starting position and the first target cleaning module, the last target cleaning module and the automatic detection platform). The end point of each segment is the cleaning tank (a space for accommodating detergents and utensils for cleaning) or the automatic detection platform (for the last segmented path) of the corresponding target cleaning module. All segmented paths are connected in sequence to form a complete cleaning transmission path, which describes in detail the entire transmission process of the utensil from the starting position to the completion of all cleaning stages and finally reaching the detection platform. This implementation method achieves the technical effect of efficiently converting multi-stage cleaning solutions into specific cleaning transmission paths by accurately locating the target cleaning modules that match each cleaning stage and generating a cleaning transmission path based on the transmission path connection network.

[0037] Step S500, the first utensil is subjected to spectral detection by the automatic detection platform, and cleanliness detection is performed based on the first detection spectrum. When the cleanliness detection is passed, the first utensil is sent to the storage area. Specifically, the automatic detection platform confirms that the first utensil after cleaning has been correctly placed in the detection position and remains stable through a mechanical device or a sensor. The spectral detection module is started, and light of a specific wavelength (such as visible light, ultraviolet light or infrared light) is emitted to irradiate the surface of the first utensil. The light reflected or scattered by the surface of the utensil is captured by the receiver of the spectrometer and converted into an electrical signal for recording. The collected first detection spectrum data is analyzed by software to identify the possible residue characteristic peaks or spectral patterns. The analyzed spectrum is compared with the preset cleanliness standard spectrum, and the standard spectrum is formulated based on the spectrum data of the cleaning utensil without residue. According to the comparison result, it is judged whether the cleanliness of the first utensil meets the requirements. If the spectrum is highly consistent with the standard spectrum, or the residue characteristic peak is lower than the set threshold, it is considered that the cleanliness detection is passed. If the cleanliness detection is passed, the automatic conveying mechanism is controlled to take the first utensil out of the automatic detection platform and send it to the storage area for storage. If the cleanliness test fails, an alarm can be sounded and the cleaning process can be re-executed (such as returning to step S400 for more in-depth cleaning), or the utensil can be marked for manual processing. The embodiment of the present application adopts the method of parsing the reagent application label of the utensil to be cleaned, generating reagent residue information, performing cleaning agent matching and cleaning parameter analysis based on the cleaning attribute database, generating a multi-stage cleaning plan, and transmitting the utensil to the corresponding cleaning module for cleaning through an automatic transmission mechanism, and using an automatic detection platform to perform spectral detection on the cleaned utensil to ensure that the cleanliness meets the standard. By automatically recording the cleaning process and detection results, intelligent management is realized, and the technical effect of improving the efficiency and accuracy of cleaning and detection of general laboratory instruments is achieved.

[0038] In a possible implementation, the first utensil is subjected to spectral detection by the automatic detection platform, and cleanliness detection is performed based on the first detection spectrum. When the cleanliness detection passes, the first utensil is sent to the storage area. Step S500 further includes step S510, wherein the automatic detection platform includes a spectral detection module and a spectral analysis module, wherein the spectral detection module includes an infrared spectrometer and an ATR accessory, and the first utensil is dried and then transferred to the ATR accessory. According to the preset spectral irradiation conditions, the infrared spectrometer is started to generate the first detection spectrum. Specifically, before the first utensil is transferred to the automatic detection platform, it is ensured that the utensil has been thoroughly dried. The drying process is used to remove residual moisture and detergent to prevent these substances from interfering with the spectral detection. The first utensil after drying is automatically transferred to the ATR (attenuated total reflection) accessory. ATR allows infrared light to pass through a very thin layer of air or vacuum layer on the surface of the first utensil and then reflect back, thereby collecting spectral information on the surface of the first utensil. The ATR accessory includes a crystal (such as diamond, silicon, etc.), and the first utensil is placed on the crystal. The infrared light irradiates the surface of the first utensil through the crystal. According to the preset spectral irradiation conditions (the spectral irradiation conditions are set according to the characteristics of the sample to be tested and the detection requirements, including the wavelength range, light intensity, scanning speed, etc. of the infrared light), the infrared spectrometer is started, and the infrared spectrometer emits infrared light, which is irradiated to the surface of the first device through the ATR accessory, and the reflected spectral signal is collected. After processing, a first detection spectrum is generated. This spectrum contains the chemical information of the surface material of the first device, which is used for cleanliness detection. Step S520, the first detection spectrum is tested for cleanliness through the spectral analysis module. Specifically, the generated first detection spectrum data is loaded into the spectral analysis module. The spectral analysis module selects a cleanliness detection algorithm according to the specific needs of the laboratory and the existing spectral database, such as methods based on spectral feature matching, quantitative analysis of chemical components, etc., and uses the cleanliness detection algorithm to analyze the first detection spectrum, identify characteristic peaks, peak intensity and other information in the spectrum, and compare it with the preset cleanliness standard. According to the result of the cleanliness test, the cleanliness assessment report of the first instrument is output. If the cleanliness test is passed, the report may display "qualified" or a similar conclusion, and instruct the first instrument to be sent to the storage area; if the cleanliness test is not passed, the report may display "unqualified", and further processing or re-cleaning is required. This implementation method uses a combination of ATR accessories and infrared spectrometers to achieve non-contact and non-destructive detection of the instrument surface, avoiding the contamination problems that may exist in traditional detection methods, and achieving the technical effect of improving the reliability of the evaluation of the cleanliness of the instrument surface.

[0039] In a possible implementation, the first detection spectrum is tested for cleanliness by the spectrum analysis module, and step S520 further includes step S521, establishing an absorption spectrum database of the instrument samples that have passed the cleanliness test, wherein the absorption spectrum database includes absorption spectra corresponding to a plurality of instruments and has a spectrum acquisition condition identifier. Specifically, a large number of absorption spectrum data of instrument samples that have passed the cleanliness test are collected, and these data are obtained by spectrum detection equipment such as infrared spectrometers, and the cleanliness of each sample is strictly verified. The collected absorption spectrum data are classified and sorted according to the type of instrument, and a spectrum acquisition condition identifier is added to each absorption spectrum data. These conditions include the setting parameters of the spectrometer (such as wavelength range, resolution, scanning speed, etc.), the sample processing method (such as pretreatment steps, drying temperature, etc.) and environmental conditions (such as temperature, humidity, etc.), and these conditions are used to ensure the comparability and accuracy of the spectrum data. The sorted absorption spectrum data and the corresponding spectrum acquisition condition identifier are stored in the database to form an absorption spectrum database of the instrument samples that have passed the cleanliness test. Step S522, the absorption spectrum database is called by the spectrum analysis module to match the absorption spectrum corresponding to the first instrument as the first preset spectrum, and the corresponding spectrum acquisition condition identifier is obtained to generate the preset spectrum irradiation condition. Specifically, the type of the first instrument is determined by automatic recognition or user input, and the absorption spectrum data matching the first instrument type is queried in the absorption spectrum database as the first preset spectrum. According to the spectrum acquisition condition identifier corresponding to the queried absorption spectrum data, the preset spectrum irradiation conditions for the first detection spectrum acquisition are generated, and these conditions are used to guide the operation of the spectrum detection module.

[0040] Step S523, compare the deviations of the first preset spectrum and the first detection spectrum. If the spectral deviation meets the preset deviation, the cleanliness test is passed. Specifically, the first detection spectrum and the first preset spectrum are subjected to necessary preprocessing, such as baseline correction, noise removal, etc., to improve the accuracy of the comparison. The first detection spectrum is compared with the first preset spectrum, and the deviation between the two is calculated. The deviation calculation includes multiple aspects, such as the position, intensity, shape, etc. of the characteristic peak. The calculated spectral deviation is compared with the preset deviation threshold. If the spectral deviation meets the preset deviation (i.e., less than or equal to the threshold), the cleanliness test of the first utensil is considered to have passed; otherwise, the cleanliness test is considered to have failed. This implementation method ensures the comparability of spectral data collected from different batches and different devices by recording the spectral acquisition condition identifier and generating preset spectral irradiation conditions, reduces the errors caused by differences in experimental conditions, and achieves the technical effect of improving the accuracy of cleanliness detection.

[0041] In a possible implementation, step S520 further includes step S524, when the cleanliness test fails, extracting the absorption peak structure of the functional group based on the first detection spectrum. Specifically, when the cleanliness test fails, use spectrum analysis software to view the waveform, intensity distribution and other characteristics of the spectrum, analyze the first detection spectrum in detail, and identify the absorption peaks corresponding to different functional groups in the spectrum. The functional group refers to an atom or atom group in a molecule that can determine the chemical properties of an organic compound, and has a specific absorption peak in the infrared spectrum. For each identified absorption peak, extract its detailed structural information, including the position (wavelength), intensity, shape (such as width, symmetry), etc. of the peak. Step S525, identify the cleaning residue based on the absorption peak structure and the first reagent residue information, and generate the second reagent residue information. Specifically, compare the extracted absorption peak structure with a known residue spectrum database, which contains characteristic absorption peaks of various reagents, pollutants, etc. under the infrared spectrum. According to the comparison result, identify the corresponding residue in the first detection spectrum, record the identified residue information, and form the second reagent residue information. Step S526, based on the second reagent residue information, cleaning agent matching, cleaning parameter analysis and equipment cleaning are performed again until the cleanliness test passes. Specifically, according to the second reagent residue information, steps S300-S500 are repeated to clean the equipment until the cleanliness test passes. This implementation method identifies the type and nature of the residue by analyzing the absorption peak structure in the spectrum, avoiding the blind use of multiple cleaning agents or repeated ineffective cleaning operations, and achieving the technical effect of saving time and resources and improving cleaning efficiency.

[0042] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for automatic cleaning and detection of common laboratory instruments, characterized in that: include: Acquire a mixed cleaning mechanism of a target laboratory, wherein the mixed cleaning mechanism includes a plurality of cleaning modules corresponding to a plurality of cleaning agents, and each cleaning agent has a cleaning property database; Parsing the reagent application tag of the first utensil to be cleaned to generate first reagent residual information; Calling the cleaning attribute database, performing cleaning agent matching and cleaning parameter analysis based on the first reagent residue information, and generating a multi-stage cleaning plan; Establishing a cleaning and conveying path through the multi-stage cleaning scheme, conveying the first utensil to a corresponding cleaning module in the multi-stage cleaning scheme for cleaning through an automatic conveying mechanism according to the cleaning and conveying path, and conveying the first utensil to an automatic detection platform after the multi-stage cleaning scheme is completed; Performing spectrum detection on the first tool by the automatic detection platform, performing cleanliness detection based on the first detection spectrum, and sending the first tool to a storage area when the cleanliness detection passes; The cleaning attribute database is called, cleaning agent matching and cleaning parameter analysis are performed based on the first reagent residue information, and a multi-stage cleaning scheme is generated, including: Based on the first reagent residual information, perform residual reagent attribute identification to generate multiple attribute information of multiple residual reagents; Inputting the plurality of attribute information into the cleaning attribute database for traversal matching, and generating a plurality of mapping results about residual reagents and cleaning agents; Configure the cleaning solution concentration and the cleaning reaction time based on the multiple mapping results to generate the multi-stage cleaning scheme; The configuration of the cleaning solution concentration and the cleaning reaction time based on the multiple mapping results to generate the multi-stage cleaning scheme includes: extracting a first cleaning agent and a plurality of matching residual reagents in the first mapping result, wherein any one of the plurality of mapping results is extracted as the first mapping result; Performing a wall analysis on the first device based on the multiple matching residual reagents to generate multiple predicted residual amounts; Recalling historical cleaning record data of the first cleaning agent, configuring the cleaning solution concentration and the cleaning reaction time based on the multiple predicted residual amounts, and generating a first cleaning solution; Performing sequence impact analysis on the detergents corresponding to the plurality of mapping results to generate a detergent sequence; combining the cleaning agent sequence and the first cleaning scheme to generate the multi-stage cleaning scheme; Wherein, the first device is subjected to wall analysis based on the plurality of matching residual reagents to generate a plurality of predicted residual amounts, including: Performing viscosity identification on the plurality of matching residual reagents to generate a reagent viscosity index; Performing size recognition on the first utensil, inputting the size recognition result and the reagent viscosity index into a wall hanging prediction network for prediction analysis, and generating the plurality of predicted residual amounts, wherein the wall hanging prediction network is trained by calling a prediction sample library; The prediction sample library includes multiple groups of prediction samples, and any prediction sample includes a reagent viscosity index sample, a utensil size sample and corresponding identification information including the amount of wall hanging; The method includes: performing spectrum detection on the first tool through the automatic detection platform, performing cleanliness detection based on the first detection spectrum, and sending the first tool to the storage area when the cleanliness detection passes, including: The automatic detection platform includes a spectrum detection module and a spectrum analysis module, wherein the spectrum detection module includes an infrared spectrometer and an ATR accessory; The first device is dried and then transferred to the ATR accessory, and the infrared spectrometer is started according to the preset spectral irradiation conditions to generate the first detection spectrum; Performing cleanliness detection on the first detection spectrum by using the spectrum analysis module; Wherein, performing cleanliness detection on the first detection spectrum by the spectrum analysis module includes: Establishing an absorption spectrum database of instrument samples that have passed the cleanliness test, wherein the absorption spectrum database includes absorption spectra corresponding to a variety of instruments and has spectrum acquisition condition identifiers; The absorption spectrum database is called by the spectrum analysis module to match the absorption spectrum corresponding to the first device as a first preset spectrum, and a corresponding spectrum acquisition condition identifier is obtained to generate the preset spectrum irradiation condition; Comparing the deviation between the first preset spectrum and the first detected spectrum, if the spectrum deviation meets the preset deviation, the cleanliness detection passes; The method for automatically cleaning and detecting common laboratory instruments also includes: When the cleanliness test fails, extracting the absorption peak structure of the functional group based on the first detection spectrum; Identify cleaning residues based on the absorption peak structure and the first reagent residue information to generate second reagent residue information; Based on the second reagent residue information, cleaning agent matching, cleaning parameter analysis and utensil cleaning are performed again until the cleanliness detection passes.

2. The method for automatic cleaning and detection of general laboratory equipment according to claim 1, characterized in that: The cleaning delivery path is established by the multi-stage cleaning scheme, including: Positioning a plurality of target cleaning modules corresponding to the multi-stage cleaning scheme in the hybrid cleaning mechanism; Acquire a transmission path connection network of the hybrid cleaning mechanism, perform segmented path positioning on the multiple target cleaning modules according to the stage sequence of the multi-stage cleaning scheme, and generate the cleaning transmission path; The transmission path connection network includes transmission paths between the plurality of cleaning modules, and the end points of the segmented paths in the cleaning transmission paths are cleaning tanks of the plurality of target cleaning modules.

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