A laboratory equipment automatic control method and system based on internet of things

Abstract

The application discloses a kind of laboratory equipment automation control method and system based on Internet of Things, it is related to the field based on Internet of Things laboratory equipment automation control, for solving the problem of heterogeneous equipment collaborative control and fault diagnosis, including: defining experimental procedure event, in the process of event execution of heterogeneous equipment, the physical parameters of heterogeneous equipment are continuously collected;According to the deviation amplitude of physical parameters and the expected behavior of heterogeneous equipment, the deviation amplitude of physical parameters is identified;After event execution is completed, it is judged whether postcondition is achieved within the preset time window;If postcondition is not achieved within the preset time window, or the deviation amplitude of physical parameters is greater than or equal to amplitude threshold, then according to the completion time of postcondition and the deviation amplitude of physical parameters, target diagnostic information is provided, and according to target diagnostic information, the execution parameter or timing of subsequent experimental procedure is adjusted.

Description

Technical Field

[0001] This invention relates to the field of automated control of laboratory equipment based on the Internet of Things (IoT), and more particularly to an automated control method and system for laboratory equipment based on the Internet of Things. Background Technology

[0002] Laboratories today commonly face the challenge of coordinating multiple devices, especially when core equipment such as protein analyzers, rotary evaporators, and ultrapure water systems come from different manufacturers. Their independent communication methods and data formats make unified management and precise control extremely difficult. This heterogeneity in protocols and data formats creates information silos at the logical level, making direct interoperability difficult.

[0003] As research projects become increasingly complex, laboratories often need to perform a series of multi-step experiments. These experiments require protein analyzers, rotary evaporators, and ultrapure water systems to work together under strict time sequences and specific parameters. Due to the independent instruction formats and communication protocols of these devices, technicians have to invest a great deal of effort in tedious instruction conversion and parameter adaptation when writing pre-set sequence scripts to coordinate the actions of these devices in automated experiments.

[0004] Furthermore, long-term operation of equipment and the natural aging of components often lead to a slow, imperceptible decline in physical properties. For example, in a rotary evaporator used under high intensity for extended periods, the vacuum seal, a critical consumable, gradually hardens and loses elasticity due to the natural aging of the material. Under specific experimental conditions, the aged seal may fail to create a perfectly sealed environment, resulting in a slow and imperceptible vacuum leak. Existing single-device remote control systems typically have rudimentary mechanisms for identifying the current operating status of the equipment and for confirming and acknowledging key operations. They generally lack the ability to continuously and frequently acquire pressure value changes during experiments. Therefore, when the aforementioned slow vacuum leak occurs, the phenomenon of pressure gradually deviating from the preset value during the process cannot be detected by existing systems. This "blind spot" in information acquisition prevents the system from perceiving the subtle but continuous deviation between the actual physical state and the expected state.

[0005] The combination of this decline in physical properties and insufficient logical monitoring capabilities can have serious consequences. For example, during a sample concentration experiment requiring precise vacuum levels, slow leaks caused by aging seals and insufficient monitoring capabilities of the control system can result in the actual vacuum level inside the rotary evaporator consistently falling below the preset value. This means the boiling point of the solvent will rise accordingly, leading to an abnormally slow concentration process. For some heat-sensitive samples, prolonged exposure to temperatures above their stability can cause loss of activity, structural degradation, and ultimately sample failure. Even more challenging is that because the system log only records the successful transmission of commands and the pressure values ​​at the start and end points, without showing abnormal fluctuations in the actual vacuum level during the process, technicians cannot find the root cause of the problem in the data during post-incident troubleshooting. This not only results in the loss of valuable samples but also severely delays research progress. Summary of the Invention

[0006] This invention provides an IoT-based automated control method for laboratory equipment, which addresses the challenges of collaborative control and fault diagnosis of heterogeneous equipment.

[0007] In a first aspect, this application discloses an automated control method for laboratory equipment based on the Internet of Things, comprising:

[0008] Define an experimental step event, which includes preconditions, actions to be performed, and postconditions.

[0009] During the execution of this event by the heterogeneous device, the physical parameters of the heterogeneous device are continuously collected;

[0010] Based on the physical parameter and the expected behavior of the heterogeneous device, identify the deviation of the physical parameter;

[0011] After the event is completed, determine whether the postcondition has been met within the preset time window;

[0012] If the postcondition is not met within the preset time window, or if the deviation of the physical parameter is greater than or equal to the magnitude threshold, target diagnostic information is provided based on the completion time of the postcondition and the deviation of the physical parameter, and the execution parameters or timing of subsequent experimental steps are adjusted based on the target diagnostic information.

[0013] This technical solution effectively addresses the challenge of collaborative control of heterogeneous devices. Through event-driven and real-time parameter monitoring, it promptly detects and diagnoses anomalies during the experimental process and makes adaptive adjustments, thereby significantly improving the automation level, reliability, and success rate of the experiment and avoiding "silent failures" caused by the slow decline in equipment performance.

[0014] Furthermore, this precondition is used to indicate the device state and physical parameters that must be met for the event to be initiated.

[0015] This technical solution ensures that experimental events are only initiated when necessary conditions are met, avoiding experimental errors or equipment damage caused by unmet preconditions, thus improving the safety and reliability of experiments.

[0016] Based on this, the postcondition is used to indicate the expected state and physical parameters that the device should reach after the event is completed, as well as the preset time window for completing these conditions.

[0017] This technical solution clarifies the expected results and completion time of experimental events, providing a clear basis for subsequent anomaly judgment and diagnosis, thereby enabling refined management and control of the experimental process.

[0018] In some preferred embodiments, the target diagnostic information is provided based on the completion time of the postcondition and the deviation of the physical parameter, including:

[0019] Diagnostic information that has a mapping relationship with the completion time of the postcondition and the deviation of the physical parameter is determined from the first mapping table, and the diagnostic information is used as the target diagnostic information; the first mapping table includes the mapping relationship between different completion times, different deviations and different diagnostic information.

[0020] This technical solution enables the rapid and accurate association of experimental anomalies (failure to meet postconditions, deviation of physical parameters) with specific diagnostic information based on a preset mapping relationship, thereby improving the efficiency and accuracy of fault diagnosis.

[0021] Furthermore, the heterogeneous device is a rotary evaporator, and the physical parameter is vacuum level data; the method also includes:

[0022] When diagnostic information is insufficient, the target parameters of the rotary evaporator are fine-tuned.

[0023] During the fine-tuning process, the vacuum level of the rotary evaporator was monitored.

[0024] The response mode of the vacuum level to the fine adjustment was analyzed and compared with the preset fault mode; the response mode is a change in pulse fluctuation characteristics.

[0025] Based on the comparison results, the target diagnostic information is determined.

[0026] This technical solution enables the detection and capture of potential fault characteristics by actively fine-tuning equipment parameters and monitoring their response when existing diagnostic information is insufficient to determine the cause of the fault. This allows for a deeper diagnosis of equipment problems, and is particularly suitable for detecting progressive and hidden faults, thus improving the depth and accuracy of the diagnosis.

[0027] As a technical improvement, the target parameters of the rotary evaporator should be fine-tuned, including:

[0028] Assess the composite sensitivity of the current sample, which includes the sample's tolerance to temperature changes, vacuum changes, shear force, and specific solvents.

[0029] Based on this composite sensitivity, the target parameter with the lowest risk to the sample and most likely to induce the target failure feature is identified from the preset sample sensitivity-parameter adjustment risk matrix;

[0030] Based on the sensitivity level of the sample, the upper limit of the fine-tuning amplitude and the duration of the target parameter are dynamically determined; different sensitivity levels correspond to different upper limits of the fine-tuning amplitude and different durations.

[0031] The target parameter is fine-tuned based on the upper limit of the fine-tuning range and the duration of the fine-tuning.

[0032] This technical solution fully considers the sensitivity of the sample when fine-tuning parameters, avoiding damage to the sample caused by blind fine-tuning. At the same time, through risk matrix and dynamic adjustment strategy, it ensures that the fine-tuning process is both safe and efficient, can more accurately induce fault characteristics, and improve the safety and effectiveness of diagnosis.

[0033] In one implementation, the method further includes:

[0034] If the postcondition is not met within the preset time window, or if the deviation of the physical parameter is greater than or equal to the magnitude threshold, and the first mapping table cannot provide target diagnostic information with a mapping relationship, it is determined that the diagnostic information is insufficient.

[0035] This technical solution clarifies the criteria for judging "insufficient diagnostic information," providing clear triggering conditions for initiating deeper fault diagnosis (such as parameter fine-tuning) and ensuring the logic and completeness of the diagnostic process.

[0036] In another implementation, the target parameter is rotational speed, and the target diagnostic information is determined based on the comparison results, including:

[0037] Based on the comparison results, the amplitude, frequency, decay time, and wave pattern of the pulse wave characteristics are analyzed.

[0038] When the amplitude of the pulse wave characteristic increases significantly with increasing rotational speed, and there is a harmonic relationship between the frequency and rotational speed, and the pulse wave characteristic has a long decay time and the wave pattern shows a gradually smoothing trend, the target diagnostic information is loss of elasticity of the sealing ring material.

[0039] Based on the comparison results, the morphology, phase, and local vibration signal of the pulse wave characteristics were analyzed.

[0040] When the pulse fluctuation characteristic appears within a specific speed range, and the pulse shape exhibits a sharp characteristic of rapid decline followed by immediate rebound, accompanied by an abnormal increase in the local vibration signal of the equipment, and the pulse phase remains relatively fixed at different speeds, the target diagnostic information is mechanical stress concentration caused by improper installation of the sealing ring.

[0041] This technical solution enables a detailed analysis of the vacuum pulse fluctuation characteristics of the rotary evaporator under fine-tuning of rotation speed, accurately distinguishing two common fault modes: loss of elasticity of the sealing ring material and improper installation of the sealing ring. This provides more detailed and targeted diagnostic results, offering clear guidance for subsequent repair and maintenance.

[0042] To enhance functionality, the method also includes:

[0043] When the sealing ring material is diagnosed as having lost its elasticity, analyze the type of solvent used in the current experimental procedure and its compatibility data with the sealing ring material.

[0044] Based on the solvent type and compatibility data, assess the likelihood of the sealing ring material swelling or hardening;

[0045] If the assessment results show a high probability of swelling or hardening, analyze the frequency change trend of the pulse fluctuation characteristics. When the frequency change trend is consistent with the material modulus change law caused by swelling or hardening, it is diagnosed as solvent-induced material swelling or hardening.

[0046] If the assessment results indicate a low probability of swelling or hardening, or if the frequency change trend is inconsistent with the material modulus change pattern caused by swelling or hardening, then the diagnosis remains that the loss of elasticity is due to aging of the material itself.

[0047] This technical solution, based on the diagnosis of loss of elasticity in the sealing ring material, further introduces solvent compatibility analysis and frequency change trend comparison, which can distinguish whether the material problem is caused by solvent corrosion or material aging itself, thus providing a deeper analysis of the cause of the failure and providing a key basis for taking the correct maintenance measures (such as replacing the corrosion-resistant sealing ring or adjusting the solvent used).

[0048] Secondly, this application also discloses an IoT-based automated control system for laboratory equipment, the system comprising:

[0049] The event definition module is used to define experimental step events, which include preconditions, execution actions, and postconditions.

[0050] The data acquisition module is used to continuously collect the physical parameters of the heterogeneous device during the execution of the event on the heterogeneous device.

[0051] The trend recognition module is used to identify the deviation of the physical parameter from the expected behavior of the heterogeneous device based on the physical parameter.

[0052] The condition judgment module is used to determine whether the postcondition is met within a preset time window after the event is executed.

[0053] The diagnosis and adjustment module is used to provide target diagnostic information based on the completion time of the postcondition and the deviation of the physical parameter if the postcondition is not met within the preset time window, or if the deviation of the physical parameter is greater than or equal to the magnitude threshold, and to adjust the execution parameters or timing of subsequent experimental steps based on the target diagnostic information.

[0054] This system enables automated and intelligent control of heterogeneous laboratory equipment. Through modular design, it clearly defines functions such as event definition, data acquisition, trend recognition, condition judgment, and diagnostic adjustment, thereby constructing an efficient and reliable automated control platform that effectively solves the problems of collaborative control and fault diagnosis of heterogeneous equipment.

[0055] Beneficial Effects: This application discloses an IoT-based automated control method for laboratory equipment. By defining experimental step events that include preconditions, execution actions, and postconditions, it achieves refined management of the collaborative work of heterogeneous devices. During event execution, the system continuously collects the physical parameters of the equipment and monitors them in real time based on the deviation of the parameters from the expected behavior. After the event is completed, it determines whether the postconditions have been met. If the postconditions are not met or the physical parameters deviate too much, the system can provide target diagnostic information based on the completion time and deviation magnitude, and adjust the execution parameters or timing of subsequent experimental steps accordingly.

[0056] This method effectively solves problems in existing technologies, such as the difficulty in unified management of heterogeneous equipment, the complexity and error-prone nature of automated experimental script writing, and the difficulty in detecting slow performance degradation of equipment. Through an event-driven control model, it overcomes the heterogeneity of communication protocols and data formats among different devices, achieving seamless collaboration among multiple devices. Real-time, high-frequency physical parameter acquisition and deviation amplitude identification compensate for the "blind spots" in process monitoring of existing systems, enabling timely detection and diagnosis of progressive faults such as slow leaks caused by aging vacuum seals, avoiding "silent failures." Furthermore, adaptive adjustments to subsequent experimental steps based on diagnostic information significantly improve the automation level, reliability, and success rate of experiments, reducing sample loss and research delays, providing modern scientific research laboratories with an efficient, precise, and intelligent automated control solution. Attached Figure Description

[0057] Figure 1This is a schematic diagram of a laboratory equipment automation control method based on the Internet of Things provided in an embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of another IoT-based automated control method for laboratory equipment provided in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of an IoT-based automated control system for laboratory equipment provided in an embodiment of the present invention. Detailed Implementation

[0060] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0061] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0062] In modern scientific research laboratories, automated control systems are becoming increasingly important to improve experimental efficiency and data accuracy. However, current laboratories generally face the challenge of multi-device collaboration, especially when core equipment such as protein analyzers, rotary evaporators, and ultrapure water systems come from different manufacturers. Their independent communication methods and data formats make unified management and precise control extremely difficult. This heterogeneity in protocols and data formats creates information silos at the logical level, making direct interoperability difficult. Furthermore, long-term operation of equipment and the natural aging of components often lead to a slow, imperceptible decline in physical performance, and existing automated systems often lack sufficiently precise monitoring capabilities to detect these subtle anomalies in a timely manner. This decline in physical performance combined with insufficient logical monitoring capabilities can have serious consequences. For example, in a sample concentration experiment requiring precise vacuum levels, slow leakage due to aging seals and insufficient monitoring capabilities of the control system can cause the actual vacuum level inside the rotary evaporator to remain below the preset value, resulting in an abnormally slow concentration process or even sample failure.

[0063] In response, this application proposes an automated control method for laboratory equipment based on the Internet of Things, including:

[0064] Define experimental step events, which include preconditions, actions to be performed, and postconditions.

[0065] During the execution of events on heterogeneous devices, physical parameters of the heterogeneous devices are continuously collected;

[0066] Identify the deviation of physical parameters from the expected behavior of heterogeneous devices based on physical parameters;

[0067] After the event is executed, determine whether the postconditions are met within the preset time window;

[0068] If the postcondition is not met within the preset time window, or the deviation of the physical parameters is greater than or equal to the magnitude threshold, target diagnostic information is provided based on the completion time of the postcondition and the deviation of the physical parameters, and the execution parameters or timing of subsequent experimental steps are adjusted based on the target diagnostic information.

[0069] This application aims to provide an IoT-based automated control method for laboratory equipment, addressing the challenges of collaborative control of heterogeneous devices and the difficulty in detecting and diagnosing experimental anomalies in existing technologies. This method achieves refined management of complex experimental processes by defining experimental step events that include preconditions, execution actions, and postconditions. During event execution, physical parameters of heterogeneous devices are continuously collected, and deviations from the expected behavior of these parameters are identified. After event execution, it is determined whether the postconditions are met within a preset time window. If the postconditions are not met or the physical parameter deviations are too large, target diagnostic information is provided, and the execution parameters or timing of subsequent experimental steps are adjusted accordingly. This method effectively improves laboratory automation, reduces manual intervention, and enhances the accuracy and reliability of experiments.

[0070] To better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0071] An "experimental step event" refers to an independent, definable, and executable unit of operation in an automated experimental process. Each event contains three core components: "preconditions," "actions," and "postconditions."

[0072] "Prerequisites" refer to a series of equipment states and physical parameters that must be met before an event can be initiated. For example, before starting a rotary evaporator for concentration, it may be necessary to ensure that the vacuum pump is turned on, the cooling water is circulating, and the heating bath temperature has reached the preset value.

[0073] "Execution actions" refer to the specific sequence of operations that heterogeneous devices need to perform after an event is triggered. For example, for a rotary evaporator, execution actions may include starting rotation, adjusting the vacuum level, and setting the heating temperature.

[0074] "Postconditions" refer to the expected state and physical parameters that the equipment should reach after the event is completed, as well as the preset time window for completing these conditions. For example, after a concentration event is completed, postconditions may require the vacuum level to reach a certain stable value, the sample volume to be reduced to a preset range, the heating bath to be turned off, etc., and these conditions need to be completed within a specific time.

[0075] "Heterogeneous equipment" refers to equipment in a laboratory that comes from different manufacturers and uses different communication protocols and data formats, such as protein analyzers, rotary evaporators, and ultrapure water systems.

[0076] "Physical parameters" refer to physical quantities that can be measured and monitored during equipment operation, such as temperature, pressure, vacuum, rotational speed, and flow rate.

[0077] "Expected behavior" refers to the preset change pattern or stable range that the physical parameters of heterogeneous equipment should follow when performing specific actions under normal operating conditions.

[0078] "Deviation range" refers to the degree of difference between the actual collected physical parameters and the expected behavior.

[0079] "Amplitude threshold" refers to a preset limit used to determine whether the deviation of physical parameters has reached an abnormal level.

[0080] A "preset time window" refers to a specific time range within which a subsequent condition must be met.

[0081] "Target diagnostic information" refers to the analysis results generated by the system regarding the causes of experimental anomalies based on the completion status of post-conditions and the deviation of physical parameters.

[0082] "Execution parameters or timing" refers to the specific numerical settings (such as temperature, pressure, and rotation speed) or the order and time interval of the steps in subsequent experimental procedures.

[0083] Reference Figure 1 This invention provides an automated control method for laboratory equipment based on the Internet of Things, comprising the following steps:

[0084] S1 defines the experimental step event.

[0085] An event includes preconditions, actions to be performed, and postconditions.

[0086] Preconditions are used to indicate the equipment status and physical parameters that must be met for an event to be initiated. For example, for an experimental step event involving a heating reaction, the preconditions might include equipment status such as the reactor temperature having dropped to room temperature, the stirrer having stopped rotating, the feed valve having closed, and all safety guards being in place, as well as physical parameters such as ambient humidity and air pressure being within preset ranges.

[0087] Postconditions indicate the expected state and physical parameters that the equipment should reach after the event is completed, as well as the preset time window for achieving these conditions. Examples include the equipment being in a stopped state, the heater being off, and the valves being closed. They also include physical parameters closely related to the experimental process, such as the temperature reaching a set value, the pressure stabilizing within a certain range, and the pH value of the solution being within the target range. These expected states and physical parameters together constitute the ideal result of the event's successful completion.

[0088] Furthermore, a preset time window refers to the allowed time range for achieving these expected states and physical parameters. The setting of this time window takes into account factors such as equipment response speed, the kinetic characteristics of the experimental process, and safety margins. For example, if the solution temperature should reach and stabilize at the target temperature within 5 minutes after a heating step is completed, then 5 minutes is the preset time window for this post-condition. By introducing a preset time window, the efficiency and timeliness of event completion can be evaluated, avoiding delays in achieving conditions that could affect subsequent experimental steps or lead to potential problems.

[0089] One approach is to manually construct the event flow by dragging and dropping predefined modules in a graphical user interface (GUI), and manually input the preconditions, actions, and postconditions for each module. For example, a user could manually input "vacuum pump is turned on" as a precondition, "start the rotary evaporator and set the vacuum level to 100 mbar" as an action, and "vacuum level stabilizes at 100 ± 5 mbar for 5 minutes" as a postcondition.

[0090] S2. During the execution of events on heterogeneous devices, continuously collect the physical parameters of the heterogeneous devices.

[0091] One possible implementation is to install independent sensors on each heterogeneous device and transmit the sensor data to a central data acquisition unit via wired connections (such as RS-232 or USB). The central data acquisition unit then aggregates the data and uploads it to the control system. For example, a vacuum sensor can be installed on the rotary evaporator and connected to the data acquisition unit via a data cable. The data acquisition unit then sends the vacuum data to the control system.

[0092] Another approach is to leverage Internet of Things (IoT) technology to deploy smart sensor modules that support wireless communication (such as Wi-Fi, Bluetooth, and LoRa) on heterogeneous devices. These modules can autonomously collect physical parameters and transmit the data in real time to a cloud platform or local server via wireless network. For example, a smart vacuum sensor can directly upload vacuum data to a laboratory IoT platform via Wi-Fi.

[0093] S3. Identify the deviation of physical parameters from the expected behavior of heterogeneous devices based on the physical parameters.

[0094] As one possible approach, for the same physical parameter, the difference between the physical parameter and the expected physical parameter corresponding to the expected behavior can be determined, and this difference can be used to identify the deviation of the physical parameter.

[0095] It should be noted that detailed instructions for this step can be found in later sections and will not be repeated here.

[0096] S4. After the event is executed, determine whether the postcondition is met within the preset time window.

[0097] As one possible implementation, the system immediately checks whether all postconditions are met after the event execution. If all conditions are met, the event is considered accomplished; otherwise, it is considered not accomplished. For example, if the postcondition requires "vacuum level to stabilize at 100±5 mbar", the system checks whether the current vacuum level is within this range after the event.

[0098] Another approach is for the system to start a timer after the event execution is complete and continuously monitor the postconditions within a preset time window. The event is considered successful only if all postconditions are met continuously within that time window.

[0099] For example, if the postcondition requires "vacuum level to be stable at 100±5 mbar for 5 minutes", the system will monitor the vacuum level after the event ends and determine that the condition has been met only if it is continuously met for 5 minutes.

[0100] S5. If the postcondition is not met within the preset time window, or the deviation of the physical parameters is greater than or equal to the magnitude threshold, target diagnostic information is provided based on the completion time of the postcondition and the deviation of the physical parameters, and the execution parameters or timing of subsequent experimental steps are adjusted based on the target diagnostic information.

[0101] As one possible implementation, diagnostic information that has a mapping relationship with the completion time of the postcondition and the deviation of the physical parameters can be determined from the first mapping table, and the diagnostic information can be used as the target diagnostic information.

[0102] Furthermore, the execution parameters or timing of subsequent experimental steps are adjusted based on the target diagnostic information and the second mapping table. For example, if the diagnosis is "sealing ring aging," the system may suggest lowering the vacuum setting value of the subsequent concentration step or extending the concentration time to compensate for the impact of leakage.

[0103] It should be noted that the first mapping table includes the mapping relationship between different completion times, different deviation ranges, and different diagnostic information. The second mapping table includes the mapping relationship between different diagnostic information and different execution parameters or timing.

[0104] The IoT-based automated control method for laboratory equipment proposed in this application achieves intelligent control and anomaly diagnosis of laboratory equipment by decomposing the experimental process into manageable events and monitoring the execution of these events in real time and with precision.

[0105] Specifically, before an experiment begins, technicians or the system define a series of experimental steps and events. Each event clearly specifies the preconditions required for its initiation, the actions the equipment should take during execution, and the postconditions and preset time windows to be achieved after the event is completed. For example, a "sample concentration" event might be defined as follows: the preconditions are "the rotary evaporator cooling water is turned on and the heating bath temperature has reached 50°C"; the actions are "start the rotary evaporator, set the rotation speed to 100 rpm, and the vacuum degree to 100 mbar"; the postconditions are "the vacuum degree stabilizes at 100 ± 5 mbar for 10 minutes, and the sample volume is reduced to 5 ml", with a preset time window of 30 minutes.

[0106] When an event is triggered and begins execution, the system continuously collects key physical parameters, such as vacuum level data, from heterogeneous equipment (e.g., a rotary evaporator). This data is transmitted in real-time to the control system for analysis. The control system compares the real-time collected physical parameters with the expected values ​​based on a pre-defined model of the equipment's expected behavior, thereby identifying the deviation of the physical parameters. For example, if the expected vacuum level should be stable at 100 mbar, but the actual collected data shows that the vacuum level fluctuates continuously between 105-110 mbar, the system will identify a deviation of 5-10 mbar.

[0107] After the event is executed, the system will immediately or continuously monitor within a preset time window to determine whether the subsequent conditions have been met. For example, the system will check whether the vacuum level is stable at 100±5 mbar and whether the sample volume has been reduced to 5 ml, and whether these conditions are met within 30 minutes.

[0108] If the system detects that the post-conditions are not met within the preset time window, or that the deviation of physical parameters is greater than or equal to a preset threshold (e.g., vacuum level deviation exceeding 10 mbar), this indicates a potential anomaly during the experiment. In this case, the system will not simply stop or report an error, but will provide targeted diagnostic information based on the specific completion time of the post-conditions (e.g., the post-conditions are met after 40 minutes, exceeding the preset 30-minute time window) and the deviation of physical parameters (e.g., vacuum level consistently deviates by 15 mbar). For example, if the vacuum level continues to deviate and the post-conditions time out, the system might diagnose "vacuum seal aging leading to leakage."

[0109] Once the target diagnostic information is generated, the system intelligently adjusts the execution parameters or timing of subsequent experimental steps based on this information. For example, if the diagnostic information indicates "vacuum seal aging causing leakage," the system may automatically adjust subsequent experimental steps requiring vacuum operation. For instance, it might appropriately lower the vacuum setting to compensate for the leakage, or extend the execution time of subsequent concentration steps to ensure that the sample is sufficiently concentrated. This dynamic adjustment mechanism allows the system to not simply interrupt the experiment after a problem is detected, but rather attempt to correct the problem by optimizing parameters or timing, thereby improving the success rate and efficiency of the experiment.

[0110] Through the aforementioned mechanism, the method of this application can effectively solve the problems of difficulty in coordinating heterogeneous devices and difficulty in detecting and diagnosing anomalies in traditional laboratory automation control. It modularizes complex experimental processes and, through real-time monitoring and intelligent diagnosis, achieves refined management and adaptive adjustment of the experimental process, significantly improving the reliability and efficiency of experiments.

[0111] The IoT-based automated control method for laboratory equipment proposed in this application represents a significant advancement and innovation compared to existing technologies. Traditional automated systems often require technicians to perform tedious instruction conversions and parameter adaptations when handling the collaborative operation of heterogeneous equipment. This not only increases the risk of errors but also limits the widespread adoption of automated experiments. Furthermore, existing systems generally lack the ability to precisely monitor subtle declines in the physical performance of equipment, making it difficult to detect and diagnose gradual anomalies (such as slow leaks caused by aging vacuum seals) in a timely manner, resulting in sample loss and delays in research progress.

[0112] The core innovation of this application lies in introducing the concept of "experimental step events" and combining it with mechanisms for "continuous acquisition of physical parameters," "identification of deviation magnitude," and "intelligent diagnosis and adjustment." By defining events that include preconditions, execution actions, and postconditions, this application modularizes complex experimental procedures, greatly simplifying the collaborative control between heterogeneous devices. For example, in traditional systems, coordinating a rotary evaporator and an ultrapure water system may require writing complex scripts for specific protocols; however, in this application, only the preconditions, execution actions, and postconditions of each event need to be defined, and the system can perform unified scheduling and management based on these definitions, eliminating the need for tedious manual protocol conversion.

[0113] More importantly, this application addresses the problem of insufficient monitoring of subtle anomalies in existing systems by continuously collecting physical parameters of heterogeneous devices during event execution and identifying deviations from expected behavior in real time. For example, regarding the slow leakage problem caused by the aging of the vacuum seal ring in a rotary evaporator, traditional systems may only record the vacuum level at the beginning and end of the experiment, failing to capture the slow decrease during the process. This application, however, can continuously monitor vacuum level data and immediately identify any persistent, even minute, deviation between the actual vacuum level and the expected behavior.

[0114] Furthermore, upon detecting an anomaly, this application does not simply report an error or halt the experiment; instead, it provides target diagnostic information based on the completion time of subsequent conditions and the deviation of physical parameters, and intelligently adjusts the execution parameters or timing of subsequent experimental steps accordingly. This closed-loop mechanism of diagnosis and adjustment is not present in existing systems. For example, when "vacuum seal aging leading to leakage" is diagnosed, the system can automatically adjust the vacuum setting value of subsequent concentration steps or extend the concentration time to compensate for the impact of leakage, thereby avoiding experimental failure and reducing sample loss. This adaptive capability greatly improves the success rate and efficiency of experiments, and reduces the time spent on manual intervention and troubleshooting.

[0115] In one possible design, such as Figure 2 As shown, the heterogeneous equipment is a rotary evaporator. A rotary evaporator typically includes components such as a heating pan, rotating flask, condenser, and vacuum pump. Its operating state is affected by various physical parameters. These physical parameters can be understood as quantitative data characterizing the equipment's operating status. For example, for a rotary evaporator, a specific physical parameter could be vacuum level data. Vacuum level data is a key indicator for measuring the internal vacuum environment of the rotary evaporator; its changes directly reflect the equipment's sealing performance, pumping efficiency, and the stability of the evaporation process. The physical parameter is vacuum level data.

[0116] This application may also include the following steps:

[0117] S101. When diagnostic information is insufficient, fine-tune the target parameters of the rotary evaporator.

[0118] Specifically, when effective diagnostic information cannot be determined through the aforementioned first mapping table, i.e., when diagnostic information is insufficient, a controlled disturbance is introduced to induce or amplify potential fault characteristics. The target parameters can be adjustable parameters such as the rotary evaporator's rotation speed, heating temperature, and vacuum pump power.

[0119] It should be noted that if the postcondition is not met within the preset time window, or the deviation of the physical parameters is greater than or equal to the magnitude threshold, and the first mapping table cannot provide target diagnostic information with a mapping relationship, it is judged as insufficient diagnostic information.

[0120] As one possible approach, the target parameters of the rotary evaporator can be fine-tuned according to a preset step size.

[0121] The preset step size can be set manually.

[0122] S102. During the fine-tuning process, monitor the vacuum level of the rotary evaporator, analyze the response mode of the vacuum level to the fine-tuning, and compare it with the preset fault mode.

[0123] The response mode is a change in the characteristics of pulse fluctuations.

[0124] As a preferred implementation, the response mode can manifest as a change in pulse fluctuation characteristics, such as: a brief decrease or increase in vacuum level during fine-tuning, followed by recovery or a new stable state, or periodic fluctuations.

[0125] S103. Based on the comparison results, determine the target diagnostic information.

[0126] The preset fault modes are established based on historical data, expert experience, or equipment models, describing the vacuum response characteristics that different types of faults may produce under fine-tuning of specific parameters. For example, aging of the sealing ring may cause pulse fluctuations in the vacuum level at a specific frequency when the rotational speed increases, while a vacuum pump failure may cause the vacuum level to decrease slowly or fail to reach the preset value. By comparison, it is possible to identify which known fault mode best matches the current equipment's response mode. Finally, based on the comparison results, target diagnostic information is determined. This target diagnostic information is more accurate and specific than simply relying on a mapping table, and can guide more effective troubleshooting and experimental parameter adjustments.

[0127] In some embodiments, the target parameter is rotational speed.

[0128] As one possible approach, the morphology, phase, and local vibration signals of the equipment can be analyzed based on the comparison results.

[0129] When pulse fluctuations occur within a specific speed range, and the pulse shape exhibits a sharp characteristic of rapid decline followed by immediate rebound, accompanied by an abnormal increase in local vibration signals of the equipment, and the pulse phase remains relatively fixed at different speeds, the target diagnostic information is mechanical stress concentration caused by improper installation of the sealing ring.

[0130] Phase refers to the fixed relationship between pulse fluctuations and the rotation angle of the rotary evaporator, which can help locate the source of the fault. Local vibration signals of the equipment provide additional physical evidence to verify the mechanical root cause of vacuum fluctuations. When pulse fluctuation characteristics appear within a specific speed range, and the pulse pattern exhibits a sharp characteristic of rapid decline followed by immediate rebound, accompanied by an abnormal increase in local vibration signals of the equipment, and the pulse phase remains relatively fixed at different speeds, this is usually diagnosed as mechanical stress concentration caused by improper installation of the sealing ring. Improper installation may cause uneven local pressure on the sealing ring, resulting in periodic instantaneous leakage (sharp pulses) at a specific speed, accompanied by mechanical vibration. Because it is a structural problem, its phase usually remains stable.

[0131] As another possible implementation, the amplitude, frequency, decay time, and wave pattern of the pulse wave characteristics can be analyzed based on the comparison results.

[0132] When the amplitude of the pulse wave characteristics increases significantly with increasing speed, and there is a harmonic relationship between the frequency and the speed, and the pulse wave characteristics have a long decay time and the wave pattern shows a gradually smoothing trend, the target diagnostic information is the loss of elasticity of the sealing ring material.

[0133] A seal that has lost its elasticity is less likely to maintain an effective seal when rotating at high speeds, leading to increased leakage (greater amplitude), and its recovery ability decreases due to material aging (longer decay time and smoother shape).

[0134] This application's solution effectively addresses the problem of traditional methods failing to provide accurate diagnoses when diagnostic information is insufficient by introducing an active detection mechanism. Specifically, when the first mapping table cannot provide target diagnostic information with a mapping relationship, the system no longer passively waits but actively fine-tunes the target parameters of the rotary evaporator. This fine-tuning operation aims to controllably excite or amplify potential fault characteristics, making previously inconspicuous anomalies visible in the vacuum data. By continuously monitoring the vacuum data during fine-tuning and analyzing its response pattern to the fine-tuning, the dynamic behavior of the equipment under specific disturbances can be captured. For example, if there is a micro-crack in the sealing ring, the vacuum may exhibit specific pulse fluctuations during speed fine-tuning. Comparing this unique response pattern with preset fault patterns allows for the identification of fault types from the deep mechanisms of equipment behavior, rather than relying solely on surface parameter deviations. It is precisely this combination of active detection and pattern recognition that enables this application to determine accurate target diagnostic information even in complex situations where diagnostic information is insufficient.

[0135] Through the above technical solutions, this application significantly enhances the ability of automated control systems for laboratory equipment to diagnose complex faults. Compared to basic solutions that rely solely on preset mapping tables for diagnosis, this application, when diagnostic information is insufficient, can delve deeper into the intrinsic mechanisms of equipment faults through proactive parameter fine-tuning and response pattern analysis, thereby obtaining more accurate and specific diagnostic information. This not only improves the accuracy and reliability of diagnosis, avoiding misdiagnosis or missed diagnosis due to insufficient information, but also enables the system to adapt to new and unknown fault modes, enhancing the system's robustness and adaptability. Furthermore, by focusing on specific heterogeneous equipment (such as rotary evaporators) and physical parameters (such as vacuum data), the diagnostic process becomes more targeted, providing more precise guidance for adjusting subsequent experimental steps, effectively ensuring the smooth progress of experiments and the reliability of results.

[0136] In some preferred embodiments, suppose an IoT-based laboratory automation control system is controlling a rotary evaporator to conduct a solvent recovery experiment. During the experiment, the system detects that the vacuum level data continuously deviates from the expected range, and the subsequent condition (e.g., reaching the target vacuum level and maintaining stability within a preset time window) is not met. The system first attempts to determine diagnostic information through a first mapping table, but finds that there is no clear mapping relationship between the current vacuum level deviation and the combination of the subsequent condition completion time in the first mapping table, i.e., the diagnostic information is insufficient.

[0137] At this point, the system will initiate the optimized diagnostic process proposed in this application. Specifically, the system will select the rotational speed of the rotary evaporator as the target parameter for fine-tuning. For example, the rotational speed will be fine-tuned from 100 RPM to 110 RPM and held for 5 seconds, then returned to 100 RPM. During the fine-tuning, the data acquisition module continuously monitors the vacuum data of the rotary evaporator. The trend recognition module then analyzes the response pattern of the vacuum data to the rotational speed fine-tuning. Assuming that when the rotational speed increases, the vacuum data exhibits a brief pulse fluctuation with a frequency harmonic relationship to the rotational speed, and this pulse wave has a relatively long decay time, the diagnostic and adjustment module compares this response pattern with a preset fault mode library. In the preset fault mode library, there exists a fault mode called "loss of elasticity of sealing ring material," characterized by a pulse fluctuation with a significantly increased amplitude, a frequency harmonic relationship to the rotational speed, and a long decay time when the rotational speed increases. Through comparison, the system determines that the current equipment's response pattern highly matches the "loss of elasticity of sealing ring material" fault mode. Therefore, the system can determine the target diagnostic information as "loss of elasticity in the sealing ring material" and adjust subsequent experimental procedures accordingly. For example, it can prompt operators to check or replace the sealing ring, or adjust the vacuum settings of subsequent experiments to suit the current equipment condition. This example demonstrates the ability to successfully diagnose equipment faults through active detection and pattern recognition when traditional mapping tables fail.

[0138] In some preferred embodiments, suppose a rotary evaporator experiences abnormal fluctuations in its vacuum data during an experiment, and the first mapping table cannot directly provide clear diagnostic information, leading to a judgment of insufficient diagnostic information. In this case, the system initiates fine-tuning of the rotary evaporator's target parameter—rotation speed. As the rotation speed gradually increases from 50 RPM to 150 RPM, the data acquisition module continuously monitors the vacuum data. The trend identification module analyzes and discovers periodic pulse fluctuations in the vacuum data. Further, the diagnosis and adjustment module analyzes according to the above scheme: first, it analyzes the amplitude, frequency, decay time, and morphology of the pulse fluctuations. If it is observed that as the rotation speed increases, the pulse amplitude increases from 0.5 kPa to 1.5 kPa, its frequency is always an integer multiple of the rotation speed (e.g., 2 or 3 times), and the decay time of each pulse is approximately 2-3 seconds, with the fluctuation morphology showing a transition from sharp to gradually smoothing, the system determines the diagnostic information as "loss of elasticity in the sealing ring material." As another specific implementation, if the system detects pulse fluctuations occurring only within the 80-100 RPM speed range during fine-tuning of the rotational speed, and the pulse pattern is characterized by a rapid drop in vacuum of 0.8 kPa within a very short time (e.g., 0.1 seconds) followed by an immediate rebound, exhibiting a very sharp characteristic, and simultaneously, an abnormally enhanced local vibration signal is detected by sensors installed at specific locations on the rotary evaporator, with the phase of the pulse fluctuations and the local vibration signal remaining relatively fixed at different rotational speeds, the system determines the diagnostic information as "mechanical stress concentration caused by improper installation of the sealing ring." Through the above specific examples, this application clearly demonstrates how to achieve accurate diagnosis of rotary evaporator sealing system faults through refined parameter analysis.

[0139] In some embodiments described above, this application proposes fine-tuning the target parameters of the rotary evaporator when diagnostic information is insufficient, in order to monitor the response pattern of the vacuum degree to the fine-tuning and determine the target diagnostic information. However, in actual operation, if the specific characteristics of the experimental samples are not fully considered and parameter fine-tuning is performed blindly or inappropriately, it may cause irreversible damage to the precious experimental samples, or even fail to effectively induce fault characteristics, thereby affecting the accuracy and efficiency of diagnosis.

[0140] In this regard, this application further proposes the following steps for fine-tuning the target parameters of the rotary evaporator:

[0141] S201. Assess the composite sensitivity of the current sample.

[0142] Among them, composite sensitivity includes the sample's tolerance level to temperature changes, vacuum changes, shear force, and specific solvents.

[0143] For example, some biological samples may be highly sensitive to temperature and shear force, while some chemical synthesis intermediates may be more sensitive to changes in specific solvents or vacuum levels.

[0144] The composite sensitivity of a sample can be pre-input by an expert.

[0145] S202. Based on the composite sensitivity, identify the target parameter that poses the lowest risk to the sample and is most likely to induce the target fault characteristics from the preset sample sensitivity-parameter adjustment risk matrix.

[0146] The sample sensitivity-parameter adjustment risk matrix can be understood as a multidimensional lookup table or algorithm model. Its input is the composite sensitivity of the sample, and its output is the risk level and the probability of inducing specific fault characteristics when fine-tuning different parameters (such as rotation speed, heating temperature, vacuum pump power, etc.).

[0147] In practical applications, this matrix can be pre-constructed using extensive experimental data, simulations, or domain knowledge. Using this matrix, the system can intelligently select and fine-tune a parameter that effectively exposes potential failure modes while minimizing damage to the sample.

[0148] S203. Based on the sensitivity level of the sample, dynamically determine the upper limit of the fine-tuning range and duration of the target parameter.

[0149] Different sensitivity levels correspond to different upper limits for fine-tuning and different durations. For example, they are divided into high sensitivity, medium sensitivity, and low sensitivity.

[0150] S204. Fine-tune the target parameters according to the upper limit of the fine-tuning range and the duration of the target parameters.

[0151] The proposed solution assesses the combined sensitivity of the current sample before fine-tuning the target parameters of the rotary evaporator, thus comprehensively understanding the sample's tolerance to various operating conditions. Based on this assessment, and combined with a pre-set sample sensitivity-parameter adjustment risk matrix, the system intelligently identifies the target parameters that pose the lowest risk to the sample and are most likely to induce target fault characteristics. For example, if the sample is highly sensitive to temperature, the system may prioritize adjusting the rotational speed rather than the heating temperature. Subsequently, based on the specific sensitivity level of the sample, the upper limit of the fine-tuning amplitude and the duration of the selected target parameter are dynamically determined, avoiding excessive or inappropriate parameter adjustments. Thus, the fine-tuning process is meticulously managed, ensuring that the integrity and validity of the experimental sample are maximized while effectively inducing fault characteristics for diagnosis.

[0152] In some of the above embodiments, a diagnostic method for identifying the loss of elasticity in sealing ring materials based on pulse fluctuation characteristics has been proposed. However, in practical applications, the causes of elasticity loss in sealing ring materials can be varied, such as material aging itself, or chemical reactions (e.g., swelling or hardening) with specific solvents used in the experiment. If these specific causes cannot be further distinguished, subsequent troubleshooting or preventative measures may lack specificity, leading to low maintenance efficiency or recurring problems.

[0153] This application also includes:

[0154] This application further proposes, when diagnosing loss of elasticity in the sealing ring material, to analyze the type of solvent used in the current experimental procedure and its compatibility data with the sealing ring material; based on the solvent type and compatibility data, assess the likelihood of swelling or hardening of the sealing ring material; if the assessment results show a high likelihood of swelling or hardening, analyze the frequency change trend of the pulse fluctuation characteristics; when the frequency change trend is consistent with the material modulus change law caused by swelling or hardening, diagnose it as solvent-induced material swelling or hardening; if the assessment results show a low likelihood of swelling or hardening, or if the frequency change trend is inconsistent with the material modulus change law caused by swelling or hardening, maintain the diagnosis of loss of elasticity due to aging of the material itself.

[0155] By employing the aforementioned technical solutions, the risk of sample degradation or damage due to improper operation can be significantly reduced during diagnostic fine-tuning of the rotary evaporator, thereby protecting experimental results and resources. Simultaneously, by intelligently selecting the lowest-risk and most effective parameters for fine-tuning, the efficiency and accuracy of fault diagnosis are improved, ineffective or repeated attempts are avoided, and the automated control and troubleshooting processes of laboratory equipment are optimized.

[0156] S301. When the diagnosis is loss of elasticity of the sealing ring material, analyze the type of solvent used in the current experimental procedure and its compatibility data with the sealing ring material.

[0157] Specifically, when the system initially diagnoses a loss of elasticity in the sealing ring material, in order to achieve more accurate fault location, it will first be configured to analyze the type of solvent used in the current experimental procedure and its compatibility data with the sealing ring material.

[0158] Solvent type refers to the chemical substance that comes into direct contact with the sealing ring material during the experiment, such as common organic or inorganic solvents. Compatibility data refers to the changes in the physicochemical properties (such as swelling rate, hardness change, corrosion resistance, etc.) of the sealing ring material when it comes into contact with a specific solvent. This data can usually be obtained from technical manuals, Material Safety Data Sheets (MSDS) provided by the material supplier, or from a pre-established material performance database.

[0159] S302. Based on solvent type and compatibility data, assess the likelihood of swelling or hardening of the sealing ring material.

[0160] The likelihood of swelling or hardening includes both high and low probability.

[0161] Swelling refers to the expansion of a material's volume after absorbing a solvent, resulting in a decrease in its mechanical and sealing properties; hardening refers to the cross-linking, degradation, or loss of plasticizers in a material under the action of a solvent, leading to a decrease in its elasticity or even embrittlement.

[0162] For example, if compatibility data shows that the swelling rate or hardness change rate of the material exceeds a preset threshold under the current solvent, it is considered to be highly likely to swell or harden; otherwise, it is considered to be low-probability.

[0163] S303. If the evaluation results show a high probability of swelling or hardening, analyze the frequency change trend of the pulse fluctuation characteristics. When the frequency change trend is consistent with the material modulus change law caused by swelling or hardening, it is diagnosed as material swelling or hardening caused by solvent.

[0164] The frequency variation trend of the pulse wave characteristics can reflect the change in the elastic modulus of the sealing ring material. When the material swells or hardens, its elastic modulus changes accordingly, thus affecting its natural frequency under stress and vibration. For example, swelling usually leads to a decrease in modulus, thereby reducing the vibration frequency; hardening, on the other hand, may lead to an increase in modulus, resulting in an increase in vibration frequency. When this frequency variation trend is consistent with the material modulus change caused by swelling or hardening, the diagnosis of solvent-induced material swelling or hardening can be more accurate.

[0165] S304. If the assessment results show that the possibility of swelling or hardening is low, or if the frequency change trend is inconsistent with the material modulus change law caused by swelling or hardening, then the diagnosis remains that the loss of elasticity is caused by the aging of the material itself.

[0166] Material aging is usually caused by non-chemical factors such as prolonged use, temperature cycling, and ultraviolet radiation, and is unrelated to the effect of solvents.

[0167] Through the above technical solution, this application can more accurately diagnose the root cause of the loss of elasticity in sealing ring materials, distinguishing between material aging and swelling or hardening caused by solvent effects. This refined diagnosis avoids potential misjudgments that may occur with traditional methods, making subsequent maintenance, replacement, or adjustment of experimental parameters more targeted. For example, if the problem is diagnosed as a material issue caused by solvents, a sealing ring material with better tolerance can be replaced or the solvent used in the experiment can be adjusted; if the problem is diagnosed as material aging, preventative replacement can be carried out according to plan. This not only improves the efficiency and accuracy of laboratory equipment maintenance and extends the service life of equipment, but also reduces experimental interruptions and economic losses caused by inaccurate fault diagnosis, significantly enhancing the intelligence level and reliability of laboratory automation control.

[0168] In some preferred embodiments, assuming a rotary evaporator is operating and its vacuum data monitoring shows a significant increase in the amplitude of pulse fluctuations as the rotational speed increases, with a harmonic relationship between the frequency and the rotational speed, and a long decay time and gradually smoothing of the fluctuation pattern, the system initially diagnoses this as a loss of elasticity in the sealing ring material. At this point, the system will further initiate a diagnostic process. First, it will automatically query the solvent used in the current experimental procedure as "toluene" and retrieve the compatibility data between the sealing ring used in the rotary evaporator (e.g., nitrile rubber) and toluene from a preset material compatibility database. This data shows that nitrile rubber has a high probability of swelling in toluene. Based on this high probability assessment, the system will further analyze the frequency change trend of the vacuum pulse fluctuation characteristics. If the pulse fluctuation frequency is observed to decrease over time, and this decreasing trend is consistent with the modulus decrease caused by the swelling of nitrile rubber in toluene, the system will ultimately diagnose it as "swelling of the sealing ring material caused by the solvent (toluene)." Based on this accurate diagnosis, the system can recommend that the operator replace the sealing ring material with one that has better tolerance to toluene (e.g., fluororubber), or adjust the experimental procedure to avoid prolonged contact with toluene, thereby fundamentally solving the problem. If compatibility data shows a low likelihood of swelling between nitrile rubber and toluene, or if the pulse fluctuation frequency trend is inconsistent with the swelling or hardening pattern, the system will maintain the diagnosis of "loss of elasticity due to material aging" and recommend routine seal replacement. This layered diagnostic mechanism ensures accurate identification of the cause of the fault, providing a solid basis for subsequent decision-making.

[0169] like Figure 3 As shown, this embodiment of the invention also provides an IoT-based automated control system for laboratory equipment. The system includes:

[0170] The event definition module is used to define experimental step events. Each event includes preconditions, execution actions, and postconditions.

[0171] The data acquisition module is used to continuously collect the physical parameters of heterogeneous devices during the execution of events on the heterogeneous devices.

[0172] The trend recognition module is used to identify the deviation of physical parameters from the expected behavior of heterogeneous devices based on the physical parameters.

[0173] The condition judgment module is used to determine whether the postcondition has been met within a preset time window after the event is executed;

[0174] The diagnosis and adjustment module is used to provide target diagnostic information based on the completion time of the postcondition and the deviation of the physical parameters if the postcondition is not met within the preset time window or the deviation of the physical parameters is greater than or equal to the magnitude threshold. Based on the target diagnostic information, the module adjusts the execution parameters or timing of subsequent experimental steps.

[0175] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by a computer program instructing related hardware. This program can be stored in the computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be an internal storage unit of the task execution device (including a data sending end and / or a data receiving end) of any of the foregoing embodiments, such as the hard disk or memory of the task execution device. The computer-readable storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device. Further, the computer-readable storage medium can include both the internal storage unit of the task execution device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the task execution device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0176] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0177] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0178] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. An automated control method for laboratory equipment based on the Internet of Things, characterized in that, include: Define experimental step events, which include preconditions, actions to be performed, and postconditions. During the execution of the event by the heterogeneous device, the physical parameters of the heterogeneous device are continuously collected; Based on the physical parameters and the expected behavior of the heterogeneous device, identify the deviation of the physical parameters; After the event is completed, determine whether the postcondition is met within a preset time window; If the postcondition is not met within the preset time window, or if the deviation of the physical parameter is greater than or equal to the magnitude threshold, then target diagnostic information is provided based on the completion time of the postcondition and the deviation of the physical parameter, and the execution parameters or timing of subsequent experimental steps are adjusted based on the target diagnostic information. The provision of target diagnostic information based on the completion time of the postcondition and the deviation of the physical parameters includes: Diagnostic information that has a mapping relationship with the completion time of the postcondition and the deviation of the physical parameter is determined from the first mapping table, and the diagnostic information is used as the target diagnostic information; the first mapping table includes the mapping relationship between different completion times, different deviations and different diagnostic information; The heterogeneous device is a rotary evaporator, and the physical parameters are vacuum level data; the method further includes: In cases where diagnostic information is insufficient, the target parameters of the rotary evaporator are fine-tuned. During fine-tuning, the vacuum level of the rotary evaporator is monitored, and the response pattern of the vacuum level to the fine-tuning is analyzed and compared with a preset fault mode; the response pattern is a change in pulse fluctuation characteristics. Based on the comparison results, the target diagnostic information is determined; The fine-tuning of the target parameters of the rotary evaporator includes: Assess the composite sensitivity of the current sample, which includes the sample's tolerance to temperature changes, vacuum changes, shear force, and specific solvents. Based on the composite sensitivity, the target parameter with the lowest risk to the sample and most likely to induce the target failure feature is identified from the preset sample sensitivity-parameter adjustment risk matrix; Based on the sensitivity level of the sample, the upper limit of the fine-tuning amplitude and the duration of the target parameter are dynamically determined; different sensitivity levels correspond to different upper limits of the fine-tuning amplitude and different durations. The target parameter is fine-tuned based on the upper limit of the fine-tuning range and the duration of the target parameter.

2. The method for automated control of laboratory equipment based on the Internet of Things according to claim 1, characterized in that, The prerequisites are used to indicate the device state and physical parameters that must be met for an event to be initiated.

3. The method for automated control of laboratory equipment based on the Internet of Things according to claim 1, characterized in that, The postconditions are used to indicate the expected state and physical parameters that the device should reach after the event is completed, as well as the preset time window for completing these conditions.

4. The method for automated control of laboratory equipment based on the Internet of Things according to claim 1, characterized in that, The method further includes: If the postcondition is not met within the preset time window, or the deviation of the physical parameter is greater than or equal to the magnitude threshold, and the first mapping table cannot provide target diagnostic information with a mapping relationship, it is determined that the diagnostic information is insufficient.

5. The method for automated control of laboratory equipment based on the Internet of Things according to claim 1, characterized in that, The target parameter is rotational speed. Determining the target diagnostic information based on the comparison results includes: analyzing the amplitude, frequency, decay time, and fluctuation pattern of the pulse fluctuation characteristics based on the comparison results. When the amplitude of the pulse wave feature increases significantly with increasing rotational speed, and there is a harmonic relationship between the frequency and rotational speed, and the pulse wave feature has a long decay time and the wave pattern shows a gradually smoothing trend, the target diagnostic information is loss of elasticity of the sealing ring material. Based on the comparison results, the morphology, phase, and local vibration signals of the pulse wave characteristics are analyzed. When the pulse fluctuation characteristics appear within a specific speed range, and the pulse shape exhibits a sharp characteristic of rapid decline followed by immediate rebound, accompanied by an abnormal increase in the local vibration signal of the equipment, and the pulse phase remains relatively fixed at different speeds, the target diagnostic information is mechanical stress concentration caused by improper installation of the sealing ring.

6. The method for automated control of laboratory equipment based on the Internet of Things according to claim 5, characterized in that, The method further includes: When the loss of elasticity of the sealing ring material is diagnosed, analyze the type of solvent used in the current experimental procedure and its compatibility data with the sealing ring material. Based on the solvent type and the compatibility data, assess the likelihood of the sealing ring material swelling or hardening; If the evaluation results show a high probability of swelling or hardening, the frequency change trend of the pulse fluctuation characteristics is analyzed. When the frequency change trend is consistent with the material modulus change law caused by swelling or hardening, it is diagnosed as solvent-induced material swelling or hardening. If the assessment results indicate a low probability of swelling or hardening, or if the frequency change trend is inconsistent with the material modulus change pattern caused by swelling or hardening, then the diagnosis remains that the loss of elasticity is due to aging of the material itself.

7. An IoT-based automated control system for laboratory equipment, characterized in that, The system includes: The event definition module is used to define experimental step events, which include preconditions, execution actions, and postconditions. The data acquisition module is used to continuously acquire the physical parameters of the heterogeneous device during the execution of the event by the heterogeneous device; A trend recognition module is used to identify the deviation of the physical parameters from the expected behavior of the heterogeneous device based on the physical parameters. The condition judgment module is used to determine whether the postcondition is met within a preset time window after the event is executed; The diagnosis and adjustment module is used to provide target diagnostic information based on the completion time of the postcondition and the deviation of the physical parameter if the postcondition is not met within the preset time window, or if the deviation of the physical parameter is greater than or equal to the magnitude threshold, and to adjust the execution parameters or timing of subsequent experimental steps based on the target diagnostic information. The provision of target diagnostic information based on the completion time of the postcondition and the deviation of the physical parameters includes: Diagnostic information that has a mapping relationship with the completion time of the postcondition and the deviation of the physical parameter is determined from the first mapping table, and the diagnostic information is used as the target diagnostic information; the first mapping table includes the mapping relationship between different completion times, different deviations and different diagnostic information; The heterogeneous device is a rotary evaporator, and the physical parameters are vacuum level data; Also includes: In cases where diagnostic information is insufficient, the target parameters of the rotary evaporator are fine-tuned. During fine-tuning, the vacuum level of the rotary evaporator is monitored, and the response pattern of the vacuum level to the fine-tuning is analyzed and compared with a preset fault mode; the response pattern is a change in pulse fluctuation characteristics. Based on the comparison results, the target diagnostic information is determined; The fine-tuning of the target parameters of the rotary evaporator includes: Assess the composite sensitivity of the current sample, which includes the sample's tolerance to temperature changes, vacuum changes, shear force, and specific solvents. Based on the composite sensitivity, the target parameter with the lowest risk to the sample and most likely to induce the target failure feature is identified from the preset sample sensitivity-parameter adjustment risk matrix; Based on the sensitivity level of the sample, the upper limit of the fine-tuning amplitude and the duration of the target parameter are dynamically determined; different sensitivity levels correspond to different upper limits of the fine-tuning amplitude and different durations. The target parameter is fine-tuned based on the upper limit of the fine-tuning range and the duration of the target parameter.

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