An automatic experiment system for special mortar formula research and development

Abstract

The application relates to the technical field of general chemical or physical laboratory equipment, and discloses an automatic experiment system for special mortar formula research and development. The system monitors the operation parameters of a stirring unit through a central controller, compares a real-time process fingerprint with a failure fingerprint mode library after entering a stable stage in a mixing process, and adaptively adjusts a judgment condition according to a baseline value of an initial state of the system. Therefore, whether to terminate the process in advance before the mixing is completed is decided. The application changes an experiment process from a rigid time sequence into a dynamic process based on real-time feedback and resource allocation of process evolution information, so that invalid investment of subsequent time and resources caused by waiting and processing samples that are destined to be unqualified in the initial mixing stage is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to an automatic experiment system for the development of special mortar formula, belonging to the technical field of general chemical or physical laboratory equipment. BACKGROUND

[0002] Currently, it has become a general technical consensus to improve the efficiency of research and development by using automatic experiment systems to perform high-throughput sample preparation and testing. Such systems can accurately perform a series of preset processes such as weighing, feeding and mixing of materials, ensuring the repeatability of experimental operations. Through standardized physical execution, highly consistent samples are provided for subsequent performance evaluation. However, the operation of all such automatic systems must comply with a more fundamental principle constraint determined by the physical and chemical properties of cement-based materials themselves: that is, the internal wetting, dispersion, flocculation and early hydration product generation and a series of microscopic kinetic evolution within the first tens of minutes after mixing with water have largely determined the final macroscopic performance of the materials. This early dynamic process is manifested through the continuous change of the rheological state of the slurry.

[0003] However, the working logic of all automatic experiment systems is built around this fixed end-state evaluation method. The system, like a loyal executor, strictly follows the set procedures to complete the steps of mixing, molding and curing, and then transfers the measured sample as a black box of information to the external test instrument, passively waiting for a judgment result about its final performance. The inherent contradiction of this working method lies in the fact that it is aware of the decisive role of the early process but selectively ignores real-time insight into this process in system design. As a result, the system will spend hours or even days to carefully nurture and wait for samples whose internal structure has been doomed to fail to form qualified products within the first hour after mixing is completed. This approach of investing a large amount of time and space in nurturing and waiting for samples that are doomed to fail is a common default resource mismatch in the current automatic experiment method.

[0004] To address this issue, a seemingly straightforward improvement approach is to integrate a dedicated online rheometer or hydration heat analyzer into the automated system to directly measure early process parameters. However, this approach significantly increases the structural complexity and cost of the originally single-function B01L preparation system due to the introduction of a complex G01N analysis module. It also introduces additional challenges related to sample cross-contamination and cleaning / maintenance, thus failing to become a universally applicable solution. Therefore, existing technologies generally suffer from the following limitations: 1. Process information blind spots: In the core mixing stage, the automated system is completely oblivious to the early kinetic evolution processes occurring within the sample that determine its ultimate fate. 2. Rigid workflow: The entire experimental process is a one-way open loop; once started, it cannot be dynamically adjusted or prematurely terminated based on the real-time performance of the sample, lacking necessary process self-reflection and decision-making capabilities. 3. Misaligned resource allocation: A large amount of system resources and time are indiscriminately invested in all samples, failing to prioritize resources for experimental pathways with greater success potential. Therefore, the technical problem to be solved by this invention is how to enhance the inherent ability of an automated system to perceive the early process dynamics of a sample in situ, non-destructively, and at low cost, without adding extra complex analytical hardware or changing the standard hybrid process, by utilizing the most basic execution unit already existing in the automated system, and based on this perception result to make intelligent real-time decisions and optimize the experimental process, thereby transforming the automated experimental system from a passive end-point state evaluator into an active early process identifyer. Summary of the Invention

[0005] This invention provides an automated experimental system for the research and development of special mortar formulations. Its main purpose is to solve the problem that existing automated experimental systems have information blind spots in the mixing stage and cannot effectively identify the early state of the sample, resulting in a lot of time and resources being wasted on samples that are destined to fail.

[0006] To achieve the above objectives, this invention provides an automated experimental system for the research and development of special mortar formulations. The system includes a mixing unit monitoring module and a central controller. The central controller contains a set of operating rules that define the system's operating mode, specifically:

[0007] Before the mixing process begins, the stirring unit is controlled to run in the reference phase, and the monitoring module is instructed to obtain an operating parameter at this moment as a baseline value.

[0008] During the mixing process, the operating parameters of the stirring unit are continuously collected to form time series data, and a standard deviation within a rolling time window is calculated based on the time series data.

[0009] When the calculated standard deviation first continuously falls below a stability threshold value for characterizing the end of macroscopic mechanical chaos, a decision-making process is triggered, in which the time series data collected after this time point is corrected by the baseline value and constructed into a process fingerprint;

[0010] The morphology of the process fingerprint is compared with a failure fingerprint pattern library, and the similarity determination condition for the comparison is adjusted according to the obtained baseline value through a determined mapping relationship;

[0011] If the similarity of the comparison meets the adjusted similarity determination condition, the central controller executes the instruction to terminate the current mortar sample mixing and subsequent processing process before the end of a planned mixing duration.

[0012] Preferably, the operating parameter of the stirring unit is the power consumption value of the stirring motor; the reference stage is an empty running or a pre-stirring operation on dry powder materials.

[0013] Preferably, the central controller performs baseline value correction, specifically: subtracting the baseline value from each data point in the time series data.

[0014] Preferably, the central controller is further limited to: when the similarity of the comparison between the process fingerprint and the failure fingerprint pattern library meets the condition , an active analysis process is triggered; wherein, and are the lower threshold and upper threshold for defining the fuzzy interval in the similarity determination condition; the active analysis process specifically includes: applying a rotational speed disturbance with a determined waveform and duration to the motor of the stirring unit, and synchronously monitoring the transient response characteristics caused by the rotational speed disturbance on the operating parameter, and the central controller makes the final decision on whether to terminate early based on the analysis results of the morphology of the transient response characteristics.

[0015] Preferably, the determined mapping relationship is a lookup table or function stored in the central controller, which defines the association between different value intervals of the baseline value and a corresponding set of different similarity thresholds.

[0016] Preferably, the failure fingerprint pattern library includes multiple failure fingerprint patterns, and each failure fingerprint pattern is the process fingerprint morphology generated by a mortar sample that is finally verified as unqualified in historical experiments.

[0017] Preferably, for all mortar samples that are not terminated early and complete the entire process, the central controller is further limited to: associating the process fingerprint of the mortar sample with its final measured macroscopic performance data, and storing it in a fingerprint and performance association database.

[0018] Preferably, the central controller is further defined as: when a successful recipe needs to be reproduced on another stirring device, the process fingerprint pattern of the successful recipe is retrieved from the fingerprint-performance correlation database, and a total specific energy value is calculated by integrating the process fingerprint, which together forms a standard process profile; and a closed-loop control method is adopted to dynamically adjust the stirring speed of the other stirring device, so that the real-time process fingerprint pattern generated on the new device matches the fingerprint pattern in the standard process profile, and the termination condition of the stirring process is that the total specific energy value accumulated reaches the total specific energy value recorded in the standard process profile.

[0019] Preferably, the central controller uses dynamic time warping algorithm or cross-correlation algorithm to perform similarity comparison between the process fingerprint pattern and the patterns in the failure fingerprint pattern library.

[0020] Preferably, the central controller is further defined as: automatically controlling the system to prepare a standard reference sample and measure its process fingerprint according to a set time period; when the measured process fingerprint deviates from a stored reference fingerprint beyond the allowed range, automatically performing global correction on the similarity determination condition, or outputting a maintenance warning.

[0021] Compared with the prior art, the present application has the following advantages:

[0022] 1. The central controller monitors and records the time sequence of the operating parameters of the stirring unit during the mixing process of the mixture in real time, and continuously compares it with the failure fingerprint patterns representing the processes of known unqualified samples stored in advance; when the similarity between the real-time acquired process fingerprint and any failure pattern reaches a preset condition, the controller will execute the decision to terminate the mixing of the current sample and subsequent processing at any time before the end of the mixing process, so that the experimental process changes from a preset rigid time sequence to a dynamic process based on real-time feedback and resource allocation of the process evolution information itself, avoiding the subsequent time and resource investment caused by waiting for and processing samples that are destined to be unqualified at the beginning of the mixing process.

[0023] 2. The application also provides a decision trigger mechanism, which, in the initial stage of the mixing process, preferentially continuously calculates the short-term rolling standard deviation of the time series of operating parameters and continuously compares it with a threshold value representing signal stability; the function module in the central controller for performing the comparison between the process fingerprint and the failure mode library is only activated after the above-mentioned rolling standard deviation is first lower than the threshold value. This design, which uses the statistical stability of the signal as a prerequisite for activating the core decision function, enables the system to autonomously distinguish between the macro-mechanical chaotic stage in the initial stage of material mixing and the subsequent chemical dynamics-dominated stage, ensuring that the subsequent morphological-based comparison analysis always occurs within a time window with pure information and highlighted chemical significance, thereby eliminating the possibility of making false termination decisions due to initial physical noise interference.

[0024] 3. When the system identifies that the real-time process fingerprint is simultaneously similar to both the qualified mode and the failure mode within a preset fuzzy interval during the above-mentioned decision window, the central controller will switch its control logic and apply a preset short-term operating parameter perturbation to the stirring unit; instead of relying on external sensors, the system continues to capture and analyze the transient response characteristics caused by the perturbation on the process fingerprint through the original operating parameter monitoring module, and makes the final decision on whether to terminate early based on the morphology of the response characteristics. This operation mode, which repurposes the execution component (stirring unit) as an active detection tool under certain conditions and interprets two different dimensional data (passive observation information and active response information) from a single information source (operating parameters), enables the system to actively interrogate and identify the internal microscopic structure of the sample through macroscopic manifestations when facing unclear process evolution characteristics. BRIEF DESCRIPTION OF DRAWINGS

[0025] Fig. 1 Flowchart of the system's dynamic decision-making and intelligent identification process of the application;

[0026] Fig. 2 Comparison chart of process fingerprint power curves of different performance mortar samples of the application;

[0027] Fig. 3 Hardware architecture and information flow diagram of the system's closed-loop control of the application. DETAILED DESCRIPTION

[0028] To make the technical solutions and advantages of the application clearer, the technical solutions of the application will be described in detail below; obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments; based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the application.

[0029] The application provides a kind of for special mortar formula research and development automatic experiment system, its overall architecture is established on the closed loop control system of stirring unit monitoring module and central controller;Wherein, stirring unit is standard laboratory material mixing equipment, monitoring module is configured to the uninterrupted data acquisition of one or more operating parameters of the stirring unit, central controller is as the decision core of system, for receiving and processing the real-time data stream provided by monitoring module, and according to a group of solidified operating rules, the running of stirring unit and the whole experiment process are dynamically based on the real-time deployment of process information;In a specific experiment process, to eliminate the interference of ambient temperature fluctuation on subsequent measurement, the system is configured to execute a reference stage operation by central controller before mixing process starts, and instruct monitoring module to obtain an operating parameter as baseline value in this stage;The operation of reference stage can be no-load operation or pre-stirring operation of dry powder material, their common feature is that stirring load is constant and low, for example, central controller can instruct stirring unit to run at 60RPM speed for 10 seconds, during which, monitoring module collects power consumption value of stirring motor at 10Hz frequency, and central controller calculates the average of collected power value, to obtain a baseline value reflecting the initial state of current system thermodynamics;For example, the baseline value measured at room temperature 20 may be 10.2W, and at room temperature 30 may be 10.8W, which is recorded by the system as a reference for subsequent data correction and decision logic adjustment.

[0030] After material is put into stirring unit and water is added, mixing process starts, to distinguish the signal fluctuation caused by material physical movement in initial mixing stage from the smooth signal dominated by subsequent chemical hydration reaction, the system adopts a decision-triggered gating mechanism;Its procedure is that, in mixing process, central controller continuously collects operating parameters of stirring unit to form time series data, and calculates a standard deviation in rolling time window based on the time series data, for example, set a rolling time window of 5 seconds in length, and calculate the standard deviation of 50 power data points in the window in real time;In initial mixing stage, tumbling and wall sticking of material will cause power signal to fluctuate dramatically, and its standard deviation can be above 5.0W, as material gradually forms uniform slurry, power curve tends to be smooth, and its standard deviation will decrease;System is preset with a stability threshold for representing the end of physical mixing stage, to provide an objective and reproducible decision trigger basis for system, and the specific value of stability threshold is determined by the following standardization calibration procedure:First, select no less than five representative standard mortar formulations whose final performance verification is qualified, in constant environmental conditions, i.e. temperature 23±2 With the relative humidity 50±5%, repeat the complete mixing process for each formulation five times, and record the entire power consumption time series; secondly, for each experimental power time series data, use 5 seconds as the rolling time window to calculate its standard deviation, generate the standard deviation curve with time; then, for objective identification of each curve transition point from macro mechanical chaos phase to chemical dynamics dominant phase, linear fitting is performed on the initial descending segment of 0 to 30 seconds and the stable segment before the end of mixing, and the projection value of the intersection of the two fitting straight lines on the vertical coordinate is defined as the candidate threshold value of this experiment; finally, all the candidate threshold values obtained from all experiments are statistically sorted, and the 90th percentile point value is taken as the stability threshold value, which is fixed in the central controller and applied globally; when the standard deviation calculated by the central controller is first continuously lower than this stability threshold value, for example, continuously 3 seconds below 0.5W, a decision-making process is triggered, and this mechanism enables subsequent decision analysis to be carried out within a time window where information is stable and chemical significance is highlighted.

[0031] After the decision flow is triggered, the central controller starts to build a process fingerprint for decision; specifically, the system corrects the baseline value of the time series data collected after this trigger point, which is to subtract the baseline value obtained at the beginning of the experiment from each data point in the time series data, for example, if the current collected power value is 35.6W, and the initial baseline value is 10.2W, then the corrected net power value is 25.4W; the operation aims to strip off the systematic deviation caused by environmental temperature and device inherent friction, in order to highlight the resistance change caused by the change of the rheological property of the mortar slurry; the corrected time series data constitutes a process fingerprint representing the early hydration kinetics characteristics of the current sample; then, the system enters the real-time decision link, and compares the shape of the real-time built process fingerprint with a pre-stored failure fingerprint pattern library; the failure fingerprint pattern library includes the process fingerprint shapes generated by the mortar samples verified as unqualified in multiple historical experiments, for example, the false setting mode shows an abnormally steep peak in power within a certain time window after mixing starts, while the segregation bleeding mode shows that the power cannot establish effective growth for a long time; the comparison algorithm can use dynamic time warping algorithm or cross-correlation algorithm to measure the similarity of the two time series curves in shape, without being affected by slight stretching or shifting on the time axis; it should be noted that the similarity determination condition for comparison is not a fixed value, but is adjusted by a certain mapping relationship according to the baseline value obtained in this experiment, which can be a lookup table or a function stored in the central controller, for example, the lookup table can define: when the baseline value is in the interval of 10.0W to 11.0W, the similarity threshold for triggering termination decision is 0.95, and when the baseline value is in the interval of 11.1W to 12.0W, the threshold is adjusted to 0.90; if the comparison similarity of the current real-time process fingerprint with any pattern in the failure fingerprint pattern library meets the dynamically adjusted similarity determination condition, the central controller executes the instruction to terminate the mixing and subsequent processing of the current mortar sample, without waiting for the planned mixing time to end.

[0032] If the comparison similarity S of the real-time process fingerprint with the failure pattern library falls within a preset fuzzy interval, for example, it satisfies the condition wherein, and To define the lower and upper thresholds of the fuzzy range and to overcome this decision ambiguity, the central controller is configured to trigger an active analysis process. Specifically, this process involves applying a speed disturbance with a defined waveform and duration to the motor of the stirring unit. For example, a square wave disturbance with an amplitude of ±5 RPM lasting 0.5 seconds is superimposed on a constant speed of 60 RPM. The system, through its existing monitoring module, synchronously monitors the transient response characteristics of the speed disturbance on the operating parameters. For a slurry with a well-developed internal gel network that has initially formed, the response is characterized by a high-amplitude, rapidly decaying peak. Conversely, for a slurry with a loose internal structure, the response is weaker. The central controller then analyzes these transient response characteristics. The analysis results are used to make the final decision on whether to terminate the process early. In the active analysis process, in order to transform the morphological analysis of transient response characteristics into a deterministic decision instruction, its inherent decision partitioning diagram is established and quantified according to the following procedure: Select a stable qualified sample Sample-G, a non-qualified sample Sample-B that will fail, and a critical sample Sample-M whose performance is at the boundary state. Repeated mixing experiments are conducted on these three types of samples. When the mixing reaches the 15th minute, a square wave speed perturbation with an amplitude of ±5 RPM and a duration of 0.5 seconds is uniformly applied. For the transient response generated by each perturbation, the power data within a 2.0-second window after the perturbation occurs is extracted, and the peak amplitude is calculated from it. With response decay time Two quantitative metrics, where decay time is defined as the time from the peak point until the signal amplitude first decays to 37% of the peak value. The time elapsed after doubling; finally, collect all the experimental data. The numerical pairs and their corresponding final sample performance labels are used as a training dataset and input into a linear support vector machine (SVM) classifier for training. After the classifier is trained, it generates a dataset of the form... The decision function, and its decision rule, namely... Continue the process The execution of the termination command at the appropriate time constitutes the specific implementation of the decision partitioning diagram within the central controller.

[0033] For all mortar samples that are not terminated early and complete the entire process, the central controller also correlates the process fingerprint of the mortar sample with its final measured macroscopic property data and stores the correlation in a fingerprint-to-property correlation database, so that the knowledge base of the system can be continuously improved during use; further, when a successful recipe needs to be reproduced on another mixing device, the central controller retrieves the process fingerprint pattern and the total specific energy value of the successful recipe from the fingerprint-to-property correlation database, the process fingerprint pattern is integrated with respect to time and divided by the sample mass to obtain the total specific energy value, and the process fingerprint pattern and the total specific energy value together constitute a standard process profile; during reproduction, the system uses a closed-loop control method to dynamically adjust the stirring speed of the other mixing device, so that the real-time process fingerprint pattern generated on the new device matches the fingerprint pattern in the standard process profile, and the termination condition of the stirring process is that the total specific energy value accumulated reaches the total specific energy value recorded in the standard process profile; to ensure the long-term stability of the system, the central controller is also configured to automatically control the system to prepare a standard reference sample and measure its process fingerprint at a set time period, for example, once a week; when the difference between the measured process fingerprint and a stored reference fingerprint exceeds the allowed range, it indicates that the mechanical properties of the mixing unit may have drifted, at which time the system can automatically globally correct the similarity determination condition or output a maintenance alert.

[0034] In an experimental project aimed at high-throughput screening for developing a fast-setting high-early-strength specialty mortar, an automated experimental system was deployed for continuously preparing and evaluating samples of a series of formulations; one technical challenge in this scenario is that some formulations can experience false setting or segregation at an early stage of the mixing process due to interactions among their components, and if a fixed-length experimental procedure is followed, the system will invest the machine time and subsequent curing resources into these samples that have already failed at an early stage; when the system started preparing a new formulation sample numbered Sample-073, its operating procedure was as follows: the system first executed a reference stage of operation, i.e., pre-agitation of the dry powders fed into the mixing unit for 30 seconds, and the central controller obtained the average power consumption value for this stage through the monitoring module and recorded it as the baseline value for this experiment, which provided a dynamic reference for the subsequent judgment procedure that was coupled with the current equipment state and ambient temperature; then, the system added the prescribed amount of water according to the formulation and started mixing, and during the first two minutes after the start of mixing, the power consumption values collected by the monitoring module exhibited large-amplitude random fluctuations, and the decision trigger mechanism in the central controller was activated at this stage, continuously calculating the standard deviation of the power signal within a 5-second rolling time window, and only when this standard deviation first became continuously lower than the preset stability threshold for 3 seconds did the mechanism send a trigger signal to the judgment procedure module; this mechanism isolated the subsequent morphological comparison from the physical noise at the initial stage of mixing, so that the input for the morphological comparison was a time series data that mainly reflected the chemical kinetics evolution after filtering out the physical mixing interference.

[0035] After the decision flow is triggered, the central controller starts to build a process fingerprint from the time series data collected in real time and corrected by the baseline value, and compare it with the failure fingerprint pattern library; when the mixing process reaches the 11th minute, the process fingerprint of Sample-073 reaches a high similarity value with a failure fingerprint pattern in the library, which is marked as false setting; at this time, instead of comparing this similarity value with a fixed threshold, the central controller calls the similarity judgment condition based on the initial baseline value adjustment; due to the slightly high laboratory environment temperature at the beginning of the experiment, the initial baseline value is correspondingly high, and the decision condition adjusts the similarity threshold value from the regular 0.90 to 0.85 according to the preset lookup table; since the currently calculated similarity value has exceeded the adjusted threshold value of 0.85, the central controller makes a termination decision; this combination of signal stability quality inspection and environment adaptive threshold adjustment provides a mechanism for synchronously correcting the judgment benchmark when using a changing signal for judgment, so that the working mode of the system changes from passive end-point evaluation to an active process with early identification capability; the central controller immediately executes the instruction to terminate the current mortar sample mixing and subsequent processing process, and the system automatically discharges Sample-073 as waste and immediately starts the preparation process of the next sample Sample-074; the previously occupied subsequent mixing, molding and curing machine time and space resources for Sample-073 are immediately released.

[0036] Example 2: To objectively verify the effectiveness of the automatic experimental system of the present application in the research and development workflow in early identification and process termination of mortar samples with different performance, the following comparative test is designed and performed; the test platform uses the automatic experimental system in the foregoing specific embodiment, the stirring unit is a planetary mortar stirrer, the speed control accuracy is ±1 RPM, the monitoring module is a power sensor connected to the stirring motor, the data acquisition frequency is set to 10 Hz, and the power resolution is 0.1 W; a failure fingerprint pattern library containing false setting and segregation bleeding failure modes is preloaded in the central controller; 10 groups of special mortar formulas with different performances verified in advance are selected as test materials, numbered as P-01 to P-10, among which P-01 to P-04 are formulas with final performance, P-05 to P-07 are formulas that will cause false setting during mixing, and P-08 to P-10 are formulas that will cause segregation bleeding; the test is divided into a control group and a test group; the control group uses a fixed time length working mode, i.e. the early identification and termination function of the system is disabled, and all 10 groups of formula samples perform a complete mixing process with a time length of 30.0 minutes; the test group uses the automatic experimental system of the present application, and the stability threshold value in the decision trigger gating mechanism is set to 0.5 W, and the other parameters are consistent with those in the specific embodiment.

[0037] The experimental results showed that in the control group, all 10 sample groups underwent a complete 30.0-minute mixing process. In the experimental group, for samples P-01 to P-04, whose expected performance was satisfactory, the system did not trigger an early termination command and completed the entire 30.0-minute mixing process, with the judgment results consistent with the expected performance. In contrast, for samples P-05, P-06, and P-07, which were expected to experience false coagulation, the system made an early termination decision at 12.4 minutes, 14.8 minutes, and 13.1 minutes of mixing, respectively. Similarly, for samples P-08, P-09, and P-10, which were expected to experience segregation and bleeding, the mixing process ended at 16.7 minutes, 18.5 minutes, and 18.5 minutes, respectively. The experiment was terminated by the system at 19.2 minutes. The system's judgment results for the experimental group were consistent with the expected performance of all 10 groups of samples. The data from this experiment show that the automated experimental system can effectively identify the performance evolution trend of samples in the early stage of material mixing and automatically terminate the subsequent preparation process of those samples that are judged to be unqualified. The underlying mechanism is that the process fingerprint morphology generated by the unqualified samples in the early stage of mixing reaches the similarity judgment condition of the corresponding pattern in the failure fingerprint pattern library after dynamic adjustment, thereby triggering the termination command. Compared with the fixed duration working mode, the system reduces the invalid machine time allocated to unqualified samples by 35% to 58%, improving the time utilization rate of R&D equipment.

[0038] Example 3: This example combines Figs. 1 to 3 This describes an automated experimental system for the research and development of special mortar formulations, such as... Fig. 1 As shown, the process begins with the start of the experiment / introduction of dry powder materials, followed by a reference phase to obtain baseline values ​​to correct for environmental influences. After the formal mixing starts, the system uses a decision-triggered gating mechanism to compare the calculated rolling standard deviation with a preset stability threshold to determine whether the mixing process has escaped macroscopic mechanical chaos. When the standard deviation is less than the stability threshold, the subsequent judgment process is triggered, namely, entering the process fingerprint construction stage. The data corrected by the baseline value is used to construct a process fingerprint characterizing the early hydration kinetics of the sample. This process fingerprint is compared and judged with the typical fingerprint morphology of unqualified samples in the failure fingerprint pattern library. If the similarity meets the termination condition dynamically adjusted by the baseline value, it is judged as an unqualified sample and the early termination process is executed to release machine time and resources. If the similarity does not meet the termination condition, the sample enters the entire mixing process and becomes a qualified sample. Its process fingerprint and final performance data are stored in the fingerprint and performance association database for knowledge accumulation and process reproduction. When the similarity is in the fuzzy range, the active analysis process is triggered. By applying micro-perturbations of rotation speed and analyzing transient response characteristics, it provides auxiliary basis for the final decision.

[0039] As Fig. 2 shown in FIG. 1, the horizontal axis is time in seconds, and the vertical axis is the corrected power in watts. The figure shows three typical process fingerprint curves, where the solid line marked as qualified sample shows a smooth and steady growth trend in power value after mixing begins, eventually stabilizing at a higher plateau, reflecting the dynamics of the formation of a stable internal structure of the slurry. The coarse dashed line marked as false coagulation sample shows an abnormal sharp increase in power value after mixing proceeds to about 300 seconds, significantly higher than the normal level of the qualified sample, indicating a sharp loss of slurry flowability. The dotted line marked as segregation bleeding sample shows a power value that remains at a low level and grows weakly throughout the mixing process, reflecting the failure of the material to form a uniform and stable gel structure.

[0040] As Fig. 3 shown in FIG. 2, the system architecture takes the central controller as the core, which contains decision modules, data processing and storage units, and operation rule solidification modules. The central controller issues parameter collection instructions to the monitoring module, which monitors the power of the stirring unit through its internal power sensor. The stirring unit includes a feeding port, a stirring container, a stirring motor, and a control panel. The monitoring module returns the collected data to the central controller in real-time data stream at a frequency of 10 Hz. The central controller analyzes and makes decisions based on the received data stream and sends a pattern comparison request to the database system, which contains a failure fingerprint pattern library and a fingerprint performance correlation library. The central controller returns the similarity comparison result to the central controller. Finally, the central controller issues control instructions such as speed control to the stirring motor in the stirring unit based on the operation rules and comparison results, thus forming a closed-loop dynamic decision and control system.

[0041] Example 4: When an automated experimental system is first deployed in a new special mortar research and development project, due to the lack of historical data for the material system, the upper and lower threshold values of the fuzzy interval in the central controller for triggering the active analysis process and the key parameters of the active analysis process are in an undefined state; to solve this problem, the system needs to perform a standardized initial parameter calibration and decision logic quantification procedure before being put into regular use.

[0042] The first step of the procedure is to calibrate the boundary of the fuzzy interval, the operator needs to prepare three types of standard samples in advance, a stable performance qualified sample Sample-G, a failure sample Sample-B, and a performance at the boundary critical sample Sample-M; the system conducts 5 repeated mixing experiments on these three types of samples in the state of disabling the early termination function, and records the complete process fingerprint of each sample; the central controller calculates and counts the similarity distribution data according to this, including the similarity between qualified samples, the similarity between unqualified samples, and the similarity between critical samples and qualified samples and unqualified samples; based on the data distribution, the system can determine the initial threshold, and according to this, the is set as the lower boundary value of the similarity distribution between critical samples and qualified samples 0.80, and the is set as the upper boundary value of the similarity distribution between critical samples and unqualified samples 0.90.

[0043] The second step of the procedure is to determine the rotational speed disturbance parameter applied in the active analysis process, and the determination method aims to make the signal-to-noise ratio of the response signal meet the requirements while avoiding interference with the hydration process of the sample itself; the calibration method is to use the aforementioned critical sample Sample-M for testing, and the central controller applies different amplitude and duration square wave rotational speed disturbances in sequence according to the preset gradient parameter table at the 15th minute of the mixing process, and records the transient response caused by each disturbance on the power consumption parameter; the finally selected parameter combination is amplitude ±5 RPM and duration 0.5 seconds, and the judgment basis is that the signal-to-noise ratio of the peak value of the transient response caused under this parameter is greater than 10 dB, and after the disturbance ends, the overall process fingerprint of the sample can recover to the trend line before the disturbance within 20 seconds.

[0044] The third step of the procedure is to quantify the analysis logic of the transient response characteristics, when the active analysis process is triggered and the aforementioned calibrated rotational speed disturbance is applied, the central controller executes the following algorithm steps: intercept the power consumption data in the 2.0 second time window after the disturbance occurs; subtract the power average of 1.0 second before the disturbance from this data segment to obtain the net response signal; calculate two quantitative indicators, peak amplitude and response decay time from the net response signal, where the decay time is defined as the time elapsed from the peak point to the first decay of the signal amplitude to 37% of the peak value; finally, the calculated , ) the numerical pair is compared with a decision partition map established by calibration samples, if the point falls in the qualified region, the system continues to execute the mixing procedure, if it falls in the unqualified region, the system executes the early termination instruction; through the execution of the above procedure, the automated experimental system completes the parameter calibration under the new material system, and provides a quantitative decision basis for the subsequent routine operation.

[0045] In order to enable the automated experimental system to maintain the accuracy of its decision under the changing laboratory environment temperature, the mapping relationship stored in the system for adjusting the similarity determination condition needs to be calibrated offline; the procedure is executed in a chamber with controllable environment temperature, first the automated experimental system is placed in the chamber, and an initial temperature point is set, after the system temperature and the environment temperature are balanced, the reference stage is executed to record the baseline value corresponding to this temperature point, then the qualified sample Sample-G, the unqualified sample Sample-B and the critical sample Sample-M are mixed for experiment, and the similarity determination condition that can correctly distinguish the critical sample under the baseline value is determined, the above steps are repeated, that is, at multiple different temperature points, the corresponding baseline value and the similarity determination condition numerical pair are sequentially obtained, and these data pairs are filled into a lookup table in the central controller.

[0046] In order to enable the system's failure fingerprint mode library to be expanded and optimized during use, the system is also configured with a mode updating and maintenance procedure; in the routine experimental procedure, for each sample that is finally confirmed as unqualified after going through the complete procedure, the central controller will archive its process fingerprint together with the final performance label, when the number of newly added unqualified samples with the same performance label reaches a preset number, the central controller automatically performs a clustering analysis on the process fingerprints of these samples, if the analysis result shows that there is a fingerprint form cluster with high cohesion, the system calculates a center fingerprint for the newly discovered cluster, and adds the center fingerprint as a new entry into the failure fingerprint mode library.

[0047] In order to apply an established standard process file to a large-scale experiment or repeated production using a new batch of raw materials, a standardized pre-deployment verification and adaptive adjustment procedure is performed to ensure that the consistency of the mixing process is not affected by the differences in physical properties between batches of raw materials. The procedure is as follows: specify the standard process file to be verified in the system, and load the identification information of the new batch of raw materials; the system then enters verification mode and uses closed-loop control to mix samples using the new batch of raw materials, with the control target being to match the real-time generated process fingerprint pattern and total specific energy value with the target values recorded in the standard process file; during the mixing process, the central controller synchronously calculates the cumulative root mean square error between the currently generated process fingerprint and the target process fingerprint, and at the end of the mixing, compares the cumulative root mean square error with a pre-set process fidelity threshold; if the calculated cumulative root mean square error is lower than the process fidelity threshold, it is determined that the current standard process file is applicable to the new batch of raw materials; if the cumulative root mean square error exceeds the threshold, it indicates that the new batch of raw materials has an impact on the mixing process, at which time the system automatically saves the process fingerprint pattern actually generated in the verification mode and the total specific energy value finally reached as a new standard process file associated with the identification information of the new batch of raw materials; through this verification and adaptive adjustment procedure, the system generates a corresponding standardized mixing process for different batches of raw materials, providing traceable consistency assurance for subsequent routine experiments or production tasks.

[0048] After the automated experimental system has been running continuously for a set period of time, in order to verify and maintain the long-term stability of its measurement reference, the central controller automatically triggers a system self-check and calibration procedure during the intervals of experimental tasks; in this procedure, the system first calls an internally stored standard reference sample formula and automatically controls the batching and feeding units to complete the preparation of the standard reference sample, then the system mixes the sample according to the standard procedure and collects its complete real-time process fingerprint, the central controller uses dynamic time warping algorithm to compare the morphological similarity between the newly measured process fingerprint and the reference fingerprint stored at the initial calibration of the system, and calculates a drift value; if the drift value is below the upper limit of the pre-set allowed range, the system self-check passes, if the drift value exceeds the allowed range, the central controller outputs a maintenance alert according to the pre-set rules, or based on the size and direction of the drift value, calculates a set of correction coefficients and makes a global compensation adjustment to the similarity judgment conditions.

[0049] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0050] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. An automated experimental system for the research and development of special mortar formulations, the system comprising a mixing unit monitoring module and a central controller, characterized in that, The central controller has a set of operating rules that define how the system operates, including: Before the mixing process begins, the stirring unit is controlled to run in the reference phase, and the monitoring module is instructed to obtain an operating parameter at this moment as a baseline value. During the mixing process, the operating parameters of the stirring unit are continuously collected to form time series data, and a standard deviation within a rolling time window is calculated based on the time series data. When the calculated standard deviation is first and sustained below a stability threshold used to characterize the end of macroscopic mechanical chaos, a decision process is triggered. In this decision process, time series data collected after this time point are corrected for baseline values ​​and constructed as a process fingerprint. The morphology of the process fingerprint is compared with a failure fingerprint pattern library for similarity. The similarity judgment criteria used for this comparison are adjusted based on the obtained baseline value through a defined mapping relationship. If the similarity of the comparison meets the adjusted similarity judgment criteria, the central controller will execute the instruction to terminate the current mortar sample mixing and subsequent processing process before the end of a planned mixing time. The operating parameters of the stirring unit are the power consumption values ​​of the stirring motor; the reference stage is the operation under no-load conditions or the pre-stirring of dry powder materials. The specific value of the stability threshold is determined through the following standardized calibration procedure: First, select no fewer than five representative standard mortar formulations whose final performance has been verified as qualified, and then test them under constant environmental conditions, i.e., a temperature of 23±2°C. At a relative humidity of 50±5%, the mixing process was repeated five times for each formulation, and the total power consumption time series was recorded. Next, for the power time series data of each experiment, the standard deviation was calculated using a 5-second rolling time window, generating a curve showing the standard deviation over time. Then, to identify the turning point of each curve transitioning from the macroscopic mechanical chaos stage to the chemical kinetics-dominated stage, linear fitting was performed on the initial decreasing segment from 0 to 30 seconds and the stable segment 60 seconds before the end of mixing. The projection value of the intersection of the two fitted lines on the vertical axis was defined as the candidate threshold for that experiment. Finally, all candidate thresholds obtained from all experiments were statistically sorted, and the value at the 90th percentile was stored in the central controller as a globally unified stability threshold.

2. The automated experimental system for developing special mortar formulations according to claim 1, characterized in that, The central controller performs baseline correction, specifically by subtracting the baseline value from each data point in the time series data.

3. The automated experimental system for developing special mortar formulations according to claim 1, characterized in that, The central controller is further defined as: when the process fingerprint is compared with the failure fingerprint pattern library, the similarity is... Meet the conditions When this occurs, an active analysis process is triggered; among which, and The lower and upper thresholds used to define the fuzzy interval in the similarity judgment conditions are: the active analysis process is as follows: a speed disturbance with a defined waveform and duration is applied to the motor of the stirring unit, and the transient response characteristics caused by the speed disturbance on the operating parameters are monitored simultaneously. The central controller makes the final decision on whether to terminate early based on the analysis results of the transient response characteristics.

4. The automated experimental system for developing special mortar formulations according to claim 1, characterized in that, The defined mapping relationship is a lookup table or function stored in the central controller, which defines the association between different numerical ranges of the baseline value and a set of different similarity thresholds.

5. The automated experimental system for developing special mortar formulations according to claim 1, characterized in that, The failure fingerprint pattern library includes multiple failure fingerprint patterns. Each failure fingerprint pattern is a process fingerprint morphology generated by a mortar sample that was ultimately verified as unqualified in historical experiments.

6. The automated experimental system for developing special mortar formulations according to claim 1, characterized in that, For all mortar samples that were not terminated prematurely and completed the entire process, the central controller is also defined to associate the process fingerprint of the mortar sample with its final measured macroscopic performance data and store it in a fingerprint-performance association database.

7. The automated experimental system for developing special mortar formulations according to claim 6, characterized in that, The central controller is further defined as follows: when a successful formula needs to be reproduced on another mixing device, it retrieves the process fingerprint of the successful formula from the fingerprint and performance association database, calculates a total specific energy value by integrating the process fingerprint, and together they form a standard process file; and adopts a closed-loop control method to dynamically adjust the mixing speed of the other mixing device so that the real-time process fingerprint generated on the new device matches the fingerprint in the standard process file, and the termination condition of the mixing process is that the cumulative applied total specific energy value reaches the total specific energy value recorded in the standard process file.

8. The automated experimental system for developing special mortar formulations according to claim 1, characterized in that, The central controller uses a dynamic time warping algorithm or a cross-correlation algorithm to compare the similarity between the execution process fingerprint pattern and the patterns in the failure fingerprint pattern library.

9. The automated experimental system for developing special mortar formulations according to claim 1, characterized in that, The central controller is also defined as follows: according to a set time period, the automatic control system prepares a standard reference sample and measures its process fingerprint; when the difference between the measured process fingerprint and a stored baseline fingerprint exceeds the allowable range, the similarity judgment condition is globally corrected, or a maintenance alarm is output.

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