A method for calibrating and monitoring water bath heating digestion parameters

Abstract

The present invention discloses a water bath heating digestion parameter calibration and monitoring method, which belongs to the field of industrial process intelligent control technology. The method includes obtaining a set of environmental state parameters of a water bath digestion system and constructing a three-dimensional thermal field dynamic mirror system, performing a matching operation with a preset historical digestion case library, generating initial heating control parameters and performing digestion, and when the actual thermal field distribution data collected during the digestion process exceeds a preset threshold value from the predicted deviation value of the three-dimensional thermal field dynamic mirror system, generating and responding to a parameter calibration instruction, performing a directional thermal compensation operation and updating the three-dimensional thermal field dynamic mirror system, and performing an association analysis with the historical digestion case library to generate a calibration rule set, and correcting the digestion process based on the calibration rule set. The present invention uses the matching operation of the thermal field dynamic mirror construction and the historical case library to generate initial parameters, which can achieve adaptive closed-loop optimization of the digestion process.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial process intelligent control, and in particular to a method for calibrating and monitoring water bath heating digestion parameters. Background Art

[0002] Currently, water-bath digestion is a critical step in sample pretreatment in laboratories and industry. The accuracy of its parameter calibration directly impacts the reliability of chemical analysis results, such as heavy metal detection and organic matter decomposition. This process generally relies on automated systems consisting of temperature sensor networks and programmable heating devices, and lies at the intersection of industrial process control and electronic digital data processing technologies.

[0003] Existing technologies primarily employ fixed-parameter proportional-integral-derivative control or static calibration schemes based on rule bases. The former maintains the target temperature through preset proportional-integral-derivative parameters, while the latter invokes pre-stored heating curves based on sample type. Some improved solutions incorporate multi-point temperature monitoring, using average temperature values ​​for overall power regulation.

[0004] Existing technical solutions suffer from three major drawbacks. Static parameter settings cannot respond to real-time variables such as changes in water quality and differences in container heat capacity. Separate temperature monitoring and heating control lead to lags in thermal field uniformity control. Handling abnormal operating conditions requires interrupting the experiment for manual intervention, disrupting digestion continuity and increasing operational complexity. Summary of the Invention

[0005] To address the above issues, the present invention provides a method for calibrating and monitoring water bath heating digestion parameters. This method uses dynamic thermal field mirroring and matching operations with a historical case library to generate initial parameters. This method can trigger directional thermal compensation and update calibration rules in combination with real-time deviation analysis, thereby achieving adaptive closed-loop optimization of the digestion process.

[0006] The above objectives can be achieved through the following solutions:

[0007] A water bath heating digestion parameter calibration and monitoring method comprises obtaining an environmental state parameter set of a water bath digestion system; constructing a three-dimensional thermal field dynamic mirroring system with simulated temperature data of a preset water bath according to the environmental state parameter set; performing a matching operation on the three-dimensional thermal field dynamic mirroring system and a preset historical digestion case library to generate initial heating control parameters; performing digestion based on the initial heating control parameters and collecting actual thermal field distribution data during the digestion process; calculating the deviation between the actual thermal field distribution data and the simulated temperature data of the three-dimensional thermal field dynamic mirroring system to obtain a predicted deviation value; triggering a parameter calibration instruction when the predicted deviation value exceeds a preset threshold; in response to the parameter calibration instruction, executing a preset directional thermal compensation algorithm and updating the three-dimensional thermal field dynamic mirroring system; performing an association analysis on the updated three-dimensional thermal field dynamic mirroring system and the historical digestion case library to generate a calibration rule set; and correcting the digestion process based on the calibration rule set.

[0008] Optionally, the generating a three-dimensional thermal field dynamic mirror system includes: dividing a preset water bath physical space into a preset number of partitioned thermal units; obtaining real-time temperature data of each partitioned thermal unit and a power signal of an adjacent heater; correlating and mapping the real-time temperature data with the power signal of the adjacent heater to construct a dynamic heat conduction matrix; and generating a three-dimensional thermal field dynamic mirror system based on the dynamic heat conduction matrix.

[0009] Optionally, generating the initial heating control parameters includes: extracting historical elimination fingerprint data from the historical elimination case library; combining the current environmental state parameter set with each of the historical elimination fingerprint data for calculation to generate a similarity index; selecting the historical elimination fingerprint data with the highest similarity index as a reference template; performing spatial superposition analysis on the three-dimensional thermal field dynamic mirror system and the reference template to generate the initial heating control parameters.

[0010] Optionally, the method further includes: extracting key process parameters from each digestion process to obtain newly added digestion fingerprint data; establishing a parameter association matrix with a coefficient of variation based on the newly added digestion fingerprint data and the historical digestion fingerprint data; when the coefficient of variation in the parameter association matrix exceeds a preset tolerance, reconstructing the historical digestion case library.

[0011] Optionally, the generating parameter calibration instruction includes: based on the digestion operation, collecting the actual temperature data of the partitioned thermal unit in real time; comparing the actual temperature data with the simulated temperature data of the three-dimensional thermal field dynamic mirroring system point by point to generate a local temperature difference vector; performing weighted normalization processing on the local temperature difference vector to obtain a comprehensive predicted deviation value; when the predicted deviation value exceeds the threshold, generating a parameter calibration instruction.

[0012] Optionally, executing a preset directional thermal compensation algorithm and updating the three-dimensional thermal field dynamic mirroring system includes: responding to the parameter calibration instruction to obtain thermal field redistribution data after executing directional thermal compensation; extracting heat conduction path change characteristics based on the thermal field redistribution data; correcting the weight coefficient of the dynamic heat conduction matrix based on the heat conduction path change characteristics to obtain a corrected weight coefficient; and optimizing the three-dimensional thermal field dynamic mirroring system based on the corrected weight coefficient.

[0013] Optionally, generating a calibration rule set includes: comparing the heat conduction path change characteristics with a preset historical abnormal pattern library; when the comparison result meets a preset association condition, activating a cross-cycle learning module; dynamically comparing the partitioned thermal unit with the three-dimensional thermal field dynamic mirror system to generate real-time deviation data; converting the real-time deviation data into calibration rule entries based on the cross-cycle learning module; establishing an index relationship between the calibration rule entries and the historical solution case library to generate a calibration rule set.

[0014] Optionally, the method further comprises: decomposing an environmental interference component in the real-time deviation data;

[0015] Based on a preset component compensation function and the historical fingerprint data, a feedback link is constructed; in combination with the feedback link, when the same environmental interference component is detected to appear repeatedly, a preventive calibration strategy is automatically generated; and the preventive calibration strategy is written into the calibration rule set.

[0016] Optionally, the correction of the resolution process based on the calibration rule set includes: monitoring the version iteration status of the calibration rule set; based on the version iteration status, when a version update event is detected, extracting a new calibration rule entry; parsing the control logic constraints in the new calibration rule entry; parsing the control logic constraints in the new calibration rule entry and correcting the resolution process.

[0017] Based on the same inventive concept, the present invention also provides a water bath heating digestion parameter calibration and monitoring system, which includes: an environmental parameter acquisition module for obtaining a set of environmental state parameters of a water bath digestion system; a thermal field modeling module for constructing a three-dimensional thermal field dynamic mirror system with simulated temperature data of a preset water bath according to the set of environmental state parameters; a case matching module for matching the three-dimensional thermal field dynamic mirror system with a preset historical digestion case library to generate initial heating control parameters; a heating control module for driving a preset adaptive heating controller to perform a digestion operation based on the initial heating control parameters; and a deviation calculation module. A block is used to calculate the predicted deviation value of the three-dimensional thermal field dynamic mirroring system based on the digestion operation; a calibration trigger module is used to trigger a parameter calibration instruction when the predicted deviation value exceeds a preset threshold value; a thermal compensation execution module is used to respond to the parameter calibration instruction, execute a preset directional thermal compensation algorithm and update the three-dimensional thermal field dynamic mirroring system; a rule generation module is used to associate the updated three-dimensional thermal field dynamic mirroring system with the historical digestion case library and generate a calibration rule set; a parameter optimization module is used to jointly analyze the calibration rule set with the adaptive heating controller to generate dynamic correction operating parameters.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. The present invention achieves dynamic and precise mapping and closed-loop control of the global temperature field during the digestion process. By constructing a three-dimensional thermal field dynamic mirror system and synergizing with a real-time sensing network, it overcomes the local overheating or underheating defects caused by traditional single-point temperature control and significantly improves the digestion uniformity of samples with complex physical properties.

[0020] 2. The present invention establishes a deep interaction mechanism between the historical solution case library and the real-time physical field model, automatically matches the optimal historical operation mode in the initial parameter setting stage, and synchronously updates the control strategy and experience knowledge base when an abnormal operation occurs, forming an intelligent decision-making system with both immediate response and long-term evolution capabilities.

[0021] 3. The present invention implements spatially precise energy compensation for detected local abnormal areas through the coordinated execution of a directional thermal compensation algorithm and non-uniform power regulation, avoiding the energy waste caused by traditional overall power adjustment, and reducing system operating energy consumption while ensuring digestion quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 The present invention is a flow chart of a method for calibrating and monitoring parameters of a water bath heating digestion.

[0024] Figure 2 It is a three-dimensional thermal field mirror image of an embodiment of the present invention.

[0025] Figure 3 This is a comparison chart of the current case and historical cases in an embodiment of the present invention.

[0026] Figure 4 It is a comparison curve diagram of simulated temperature data and real-time temperature data according to an embodiment of the present invention.

[0027] Figure 5 3 is a comparative diagram of the heater power before and after calibration compensation according to an embodiment of the present invention.

[0028] Figure 6 The figure is a schematic structural diagram of a water bath heating digestion parameter calibration and monitoring system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0030] Reference Figure 1 One embodiment of the present invention proposes a water bath heating digestion parameter calibration and monitoring method, which uses dynamic thermal field mirroring and matching operations of a historical case library to generate initial parameters. It can trigger directional thermal compensation and update calibration rules in combination with real-time deviation analysis to achieve adaptive closed-loop optimization of the digestion process.

[0031] The method of this embodiment specifically includes:

[0032] Obtain the environmental state parameter set of the water bath digestion system;

[0033] Constructing a three-dimensional thermal field dynamic mirror system with simulated temperature data of a preset water bath according to the environmental state parameter set;

[0034] Perform matching calculations on the three-dimensional thermal field dynamic mirror system and a preset historical solution case library to generate initial heating control parameters;

[0035] Performing digestion based on the initial heating control parameters and collecting actual thermal field distribution data during the digestion process;

[0036] Calculating the deviation between the actual thermal field distribution data and the simulated temperature data of the three-dimensional thermal field dynamic mirror system to obtain a predicted deviation value;

[0037] When the predicted deviation value exceeds a preset threshold, generating a parameter calibration instruction;

[0038] In response to the parameter calibration instruction, performing a directional thermal compensation operation and updating the three-dimensional thermal field dynamic mirror system;

[0039] Performing correlation analysis on the updated three-dimensional thermal field dynamic mirror system and the historical solution case library to generate a calibration rule set;

[0040] The digestion process is modified based on the calibration rule set.

[0041] Specifically, a three-dimensional dynamic thermal field mirroring system for a water bath is constructed based on multi-dimensional real-time sensor data. Environmental parameters such as the water bath's temperature gradient and thermal convection intensity are collected in real time through a distributed sensor array. A three-dimensional dynamic thermal field mirroring system with simulated temperature data is constructed, and matching operations are performed in conjunction with a historical digestion case library to generate initial heating control parameters. During the digestion process, the actual thermal field distribution is continuously compared with the predicted deviation of the mirroring system. When the deviation exceeds the threshold, a directional thermal compensation algorithm is triggered and the thermal field model is updated. Finally, dynamic closed-loop optimization of the control parameters is achieved through correlation analysis between the calibration rule set and the historical case library. This achieves precise mapping and adaptive regulation of the global thermal field during the water bath digestion process, effectively overcoming the drawback of traditional static calibration's delayed response to environmental variables and significantly improving the digestion uniformity and process stability of complex physical samples. At the same time, operational experience is continuously accumulated through a self-learning mechanism, forming an intelligent control system with evolutionary capabilities.

[0042] Optionally, the system for generating a three-dimensional thermal field dynamic mirror image includes:

[0043] Dividing a preset physical space of the water bath into a preset number of zoned thermal units;

[0044] Acquiring real-time temperature data of each of the zoned thermal units and power signals of adjacent heaters;

[0045] Correlating and mapping the real-time temperature data with the power signal of the adjacent heater to construct a dynamic heat conduction matrix;

[0046] A three-dimensional thermal field dynamic mirror system is generated based on the dynamic heat conduction matrix.

[0047] Specifically, to construct a three-dimensional thermal field dynamic mirror system for a water bath, the physical space of the water bath is first divided into several adjacent and non-overlapping partitioned thermal units according to preset rules. Each partitioned thermal unit represents an independent thermodynamic calculation unit in the water bath. Then, the real-time temperature data of each partitioned thermal unit is obtained through the temperature sensors deployed in each partitioned thermal unit, and the real-time power signals of the adjacent heaters associated with the physical position of the water bath are collected at the same time. Based on the principle of heat conduction, a mapping relationship between temperature distribution and heating power is established, and the temperature change of each partitioned thermal unit is calculated by a formula. For the first The heater is Temperature contribution of each zone thermal unit ,have:

[0048] ,

[0049] in, For the The real-time power of each heater, For the Heater to The physical distance between the geometric centers of the thermal units in each partition is obtained by three-dimensional coordinate measurement. is the heat transfer coefficient, obtained through experimental calibration. The temperature contribution values ​​of each heater to the same partitioned heat unit are superimposed and combined with the initial temperature data to construct a dynamic heat transfer matrix. Finally, the discrete partitioned heat unit temperature data is converted into a continuous three-dimensional thermal field distribution model through spatial interpolation algorithm, such as Figure 2 As shown, the coordinate axes of the model represent length, width and temperature respectively. "A7 low temperature zone": located at the coordinate Nearby, the temperature is about 55℃, "E5 high temperature zone": located at coordinates Nearby, the temperature is about 65℃.

[0050] Realize dynamic mirror mapping of the thermal status of the water bath.

[0051] For example, in the heavy metal digestion scenario of industrial wastewater, the operator divides a 120 cm long and 80 cm wide water bath into 192 zoned thermal units with a side length of 5 cm. The system obtains the real-time temperature of each unit through 48 distributed temperature sensors and simultaneously collects the power data of 16 heaters. When processing high viscosity samples, the system detects that the number The temperature of the zoned heat unit is continuously 5 degrees Celsius below the set value. The dynamic heat transfer matrix calculation shows that the unit is closest to the heater. The physical distance is 35 cm, and The current power is only 60% of the rated value. The system automatically increases Power to 85% and activate adjacent heaters Working together, the new heat radiation is calculated according to the formula Compensation effect of the unit. After 5 minutes, As the unit temperature returns to the target range, the three-dimensional thermal field distribution model displays the restoration of global temperature uniformity in real time. Through the synergistic effect of physical space discretization and heat conduction modeling, the formation mechanism of localized low-temperature regions is precisely located. Targeted thermal compensation is achieved by utilizing non-uniform power regulation of adjacent heaters. This overcomes the overall overheating or underheating issues associated with traditional single-point temperature control, significantly improving thermal field stability during the digestion of complex samples.

[0052] Optionally, generating initial heating control parameters includes:

[0053] Extracting historical resolution fingerprint data from the historical resolution case library;

[0054] Calculate the current environmental state parameter set and each of the historical resolved fingerprint data to generate a similarity index;

[0055] Selecting the historical resolved fingerprint data with the highest similarity index as a reference template;

[0056] The three-dimensional thermal field dynamic mirror system and the reference template are spatially superimposed and analyzed to generate initial heating control parameters.

[0057] Specifically, the matching operation first retrieves the stored historical digestion fingerprint data from the historical digestion case library. The data contains three core dimensions. The water quality characteristic parameters are determined by obtaining the sample absorbance value through a spectrometer analyzer. The container heat capacity parameters are obtained by pre-experimentally measuring the specific heat capacity and mass product of containers of different materials. The optimal heating mode records the temperature rise curve of the historical successful digestion and the corresponding heater power combination. Then, the similarity index of the current environmental state parameter set and each historical digestion fingerprint data is calculated using the weighted Euclidean distance formula. The similarity index represents the weighted Euclidean distance between the current environmental state parameter set and the historical digestion fingerprint data. The smaller the value, the higher the similarity. For the similarity index ,have:

[0058] ,

[0059] in, Indicates the difference between the current water temperature and the initial water temperature of the historical sample, Indicates the deviation of the current container heat capacity coefficient from the historical sample, and are weight coefficients, which respectively indicate the importance of water temperature difference and container heat capacity coefficient deviation in similarity calculation. and Determined and satisfied by multiple regression analysis .like Figure 3 As shown, the absorbance mainly reflects the light absorption characteristics of the sample. However, in a water bath heating scenario where heat conduction is the main factor, the influence of absorbance is indeed limited. The heat capacity coefficient is directly related to the container material and volume, but the system greatly reduces its influence through the partitioned thermal unit and real-time compensation mechanism. As for volume, the current case is different from Using 500ml borosilicate glass, Use 400ml polytetrafluoroethylene, D-305 uses 600ml borosilicate glass; The group best matches the current case; Because the 300ml polytetrafluoroethylene container has a low heat capacity and heats up quickly, the 600ml container in the D-305 requires higher power and longer time. The system automatically selects the historical digestion fingerprint data with the highest similarity index as the reference template, aligns the spatial coordinates of the 3D temperature distribution model in this template with the currently constructed 3D thermal field distribution model, and uses a vector superposition algorithm to identify areas of temperature field differences, ultimately generating initial heating control parameters adapted to the current environmental characteristics.

[0060] For example, when an environmental testing center was treating nickel-containing electroplating wastewater, the system detected that the absorbance value of the current water quality characteristic parameter was 0.47, and the container was a 500ml borosilicate glass cup. Three similar records were retrieved from the historical case library, and the similarity calculation found that the number The fingerprint data of the water has the highest similarity, with a water absorbance of 0.46 and the same container material. The system compares the current 3D thermal field model with By spatially superimposing the benchmark templates, it was found that the heat loss on the west side of the water bath had increased due to the newly installed ventilation equipment. Based on this difference analysis, the initial heating parameters were automatically adjusted to reduce the power of the heater in the east area by 10% and increase the power of the heater in the west area by 15%. Through the dual mechanisms of multi-dimensional feature matching and spatial field superposition, the core parameters of the historical optimal solution are inherited, and local thermal field deviations caused by environmental variations are dynamically corrected. This effectively solves the parameter initialization problem when there is a lack of reference data for the digestion of new materials, and avoids the energy waste and sample damage risk caused by traditional trial and error methods.

[0061] Optionally, the method further includes:

[0062] Extract key process parameters from each digestion process to obtain new digestion fingerprint data;

[0063] Establishing a parameter correlation matrix with a coefficient of variation based on the newly added eliminated fingerprint data and the historical eliminated fingerprint data;

[0064] When the coefficient of variation in the parameter association matrix exceeds a preset tolerance, the historical solution case library is reconstructed.

[0065] Specifically, the update operation of the historical digestion case library is started after each digestion task is completed. First, the key process parameters of this digestion process are extracted, including the temperature fluctuation range in the constant temperature stage, the heating rate per unit time, and the quality detection value of the final digestion product. These parameters are combined to form the new digestion fingerprint data. Then, a parameter association matrix of the new digestion fingerprint data and the historical digestion fingerprint data is established. The row vectors of the matrix represent the new data features, and the column vectors represent the historical data features. The matrix element values ​​are obtained by formula calculation. For the element values ​​of the parameter association matrix representing the relative differences between the new digestion fingerprint data and the historical digestion fingerprint data in two parameters, ,have:

[0066] ,

[0067] in, Indicates the newly added fingerprint Item parameter value, Indicates historical fingerprint The item corresponds to the parameter value. Then calculate the coefficient of variation of the parameter correlation matrix. ,have:

[0068] ,

[0069] in is the standard deviation of the matrix elements, is the arithmetic mean of the matrix element values. When the preset tolerance value exceeds 0.25, the case library reconstruction program is automatically started. The program uses the weighted moving average method to calculate the confidence weight of the updated historical fingerprint data. ,have:

[0070] ,

[0071] in, is the original confidence weight of the historical fingerprint data before updating, is the correlation coefficient between the new data and historical data, reflecting the strength of the association between the two. It is the attenuation factor with a fixed value of 0.7, which is used to control the ratio of historical weight to new data weight.

[0072] For example, after a food testing laboratory processed three batches of vegetable oil samples in succession, the system extracted the digestion fingerprint of the third batch, which showed an abnormally accelerated heating rate. After establishing a parameter association matrix with the vegetable oil-related fingerprints in the historical case library, the coefficient of variation was calculated to be 0.31. The system automatically started the reconstruction program, and analysis found that the deviation between the actual power and the set value increased due to the aging of the heater. After reconstruction, the weight of the early cases was reduced, and a power compensation mark was added to the new fingerprint. When similar samples are tested subsequently, the system automatically loads the power compensation coefficient to generate control instructions. By dynamically evaluating the degree of deviation between new and old data and promptly identifying hidden factors such as equipment performance degradation, the case library can continue to maintain the best guidance value, effectively avoiding parameter adaptation failures caused by changes in system status, and significantly improving the adaptability of long-term operation.

[0073] Optionally, the generating a parameter calibration instruction includes:

[0074] Generate a local temperature difference vector based on point-by-point comparison of the real-time temperature data with the simulated temperature data of the three-dimensional thermal field dynamic mirror system;

[0075] Performing weighted normalization processing on the local temperature difference vector to obtain a comprehensive prediction deviation value;

[0076] When the prediction deviation value exceeds the threshold, a parameter calibration instruction is generated.

[0077] Specifically, the real-time temperature data obtained above is compared point by point with the corresponding simulated temperature data in the three-dimensional thermal field dynamic mirror system to generate a local temperature difference vector, which reflects the difference between the actual temperature and the simulated temperature at each point. Then, the local temperature difference vector is weighted and normalized. The specific operation is to multiply the temperature difference of each point by a preset weight coefficient, and then add all the weighted temperature differences to obtain the sum and divide it by the sum of the weight coefficients to obtain the comprehensive prediction deviation value. If the comprehensive prediction deviation value exceeds the preset threshold, the generation of the parameter calibration instruction is triggered. Figure 4 As shown in the figure, the process and effect of temperature calibration are displayed, showing the temperature change curve of the simulated temperature data (i.e., the simulated value) and the real-time temperature data (i.e., the measured value) over time. The solid line represents the simulated value, the dotted line represents the measured value, and the vertical dotted line represents the trigger calibration at 12 minutes. Figure 5 As shown in Figure 2, four heaters are shown. The power change before and after calibration compensation, where It is located on the main heat conduction path of the target area, so the adjustment is larger; For those on secondary paths or with thermal resistance (e.g., container obstruction), adjustments are smaller. This process ensures that the comparison of temperature data and deviation calculations are scientific and operational, while also avoiding direct calculation issues with data of different dimensions through weighted normalization.

[0078] For example, assuming that in a partitioned thermal unit, there are three actual temperature data collection points, the corresponding simulated temperature data are 200°C, 210°C and 190°C respectively, and the actual measured values ​​are 205°C, 208°C and 195°C. The local temperature difference vectors are 5°C, -2°C and 5°C. The preset weight coefficients are 0.3, 0.5 and 0.2 respectively. The weighted normalization processing process is: first multiply each temperature difference by the corresponding weight coefficient to obtain 1.5°C, -1°C and 1°C, then add the three to obtain a total of 1.5°C, and finally divide by the sum of the weight coefficients 1 to obtain a comprehensive predicted deviation value of 1.5°C. If the threshold is 1°C, the parameter calibration instruction is triggered. Through point-by-point comparison and weighted normalization processing, areas with large temperature deviations can be accurately identified and calibration instructions can be generated to optimize the accuracy of thermal field control and ensure the stability of system operation.

[0079] The performing of the directional thermal compensation operation and updating the three-dimensional thermal field dynamic mirroring system comprises:

[0080] Performing parameterized modeling and weighted normalization processing based on the dynamic heat conduction matrix to generate a weight coefficient;

[0081] In response to the parameter calibration instruction, obtaining thermal field redistribution data after performing directional thermal compensation;

[0082] extracting heat conduction path change characteristics based on the thermal field redistribution data;

[0083] Correcting the weight coefficient of the dynamic heat conduction matrix based on the change characteristics of the heat conduction path to obtain a corrected weight coefficient;

[0084] The three-dimensional thermal field dynamic mirror system is optimized based on the corrected weight coefficient.

[0085] Specifically, the method first carries out parametric modeling based on the dynamic heat conduction matrix. During the modeling process, the system calculates the thermal influence factor of each partitioned thermal unit. This factor is equal to the sum of all elements in the matrix row corresponding to the unit, reflecting the comprehensive ability of the unit to transfer heat to other units. The system then performs weighted normalization on all thermal influence factors. Specifically, each thermal influence factor is divided by the sum of all factors to obtain a normalized weight coefficient. When the system detects that the predicted deviation value exceeds the preset threshold, the parameter calibration instruction is triggered to perform a directional thermal compensation operation. The directional thermal compensation execution process first identifies abnormally low temperature areas in the three-dimensional thermal field dynamic mirror system where the temperature value is significantly lower than the preset target temperature threshold. This identification is achieved by traversing the temperature data of all partitioned thermal units and marking areas where the temperature deviation values ​​of more than three consecutive units exceed ±1.5 degrees Celsius. Subsequently, the target partitioned thermal unit set corresponding to the abnormally low temperature area is determined, and its three-dimensional spatial coordinate data is obtained. Then, the thermal radiation influence factor of the adjacent heater on the target partitioned thermal unit is calculated. For calculating the influence factor value of the adjacent heater, the parameter calibration instruction is triggered. ,have:

[0086] ,

[0087] in, is the current power percentage of the heater, Indicates heater To the target zone thermal unit The measured distance of the geometric center, is the heat transfer coefficient obtained through experimental calibration, The medium heat absorption coefficient is obtained by pre-experimentally measuring the thermal permeability of different water qualities. Finally, a non-uniform power adjustment instruction is generated based on the proportional relationship of the influencing factor values ​​of each heater. The instruction contains the power increase or decrease percentage and duration parameters of a specific heater. The operation of updating the three-dimensional thermal field dynamic mirror system first obtains the temperature monitoring value of each partitioned thermal unit in the water bath after the execution of the directional thermal compensation algorithm as the thermal field redistribution data. Then, the change characteristics of the heat conduction path are extracted, and the temperature gradient vector between adjacent thermal units is calculated. ,have:

[0088] ,

[0089] in, for The axial temperature difference is obtained by dividing the temperature difference between two adjacent thermal units by the unit spacing. and The calculation is similar. Then the weight coefficient of the dynamic heat conduction matrix is ​​modified and the weighted moving average formula is used for calculation. ,have:

[0090] ,

[0091] in, is the original weight coefficient, is the deviation between the actual temperature and the predicted temperature, The thermal conductivity sensitivity coefficient is determined by material thermal conductivity experiment. The learning rate is set to a preset value of 0.05. Finally, the heat conduction equation is reconstructed based on the modified weight coefficient to generate an optimized thermal field distribution prediction model.

[0092] For example, when digesting a sample with a high volatility organic solvent, the system detected an abnormally low temperature area with a diameter of 12 cm in the northeast corner of the water bath, involving to Calculate the proximity of the heater The impact factors are 0.38, 0.72, and 0.21 respectively. Power is increased by 25% for 5 minutes. Power is increased by 15% for 3 minutes while maintaining The power remains unchanged. After execution, the temperature in the target area returns to the set range within 4 minutes without causing overheating in other areas. By precisely quantifying the spatial relationship between adjacent heat sources, targeted energy compensation is achieved in localized low-temperature areas, avoiding the energy waste and sample denaturation risks associated with traditional overall heating. This method is particularly suitable for the precise digestion control of heat-sensitive substances.

[0093] When a chemical laboratory was processing a high-viscosity resin sample, the system detected that the directional compensation The actual temperature of the zone is still 2.3 degrees Celsius lower than the predicted value. Analysis of the heat conduction path found that There is an abnormally high gradient value in the axial direction. The system corrects the original weight coefficient of 0.85 to 0.92 and reduces The axial weighting was 0.03. In subsequent digestions of similar samples, the new model accurately predicted the additional compensation time required in the same area, initiating the heater power boost 10 seconds in advance. By dynamically capturing abnormal changes in the heat conduction path and continuously optimizing the local accuracy of the thermodynamic model, it effectively addressed prediction inaccuracies caused by sudden changes in the material's physical properties and significantly improved the feedforward control capabilities of the subsequent digestion process.

[0094] Optionally, generating a calibration rule set includes:

[0095] Comparing the heat conduction path change characteristics with a preset historical abnormal pattern library;

[0096] When the comparison results meet the preset association conditions, the cross-cycle learning module is activated;

[0097] Dynamically comparing the partitioned thermal unit with the three-dimensional thermal field dynamic mirror system to generate real-time deviation data;

[0098] converting the real-time deviation data into calibration rule entries based on the cross-cycle learning module;

[0099] An index relationship between the calibration rule entries and the historical solution case library is established to generate a calibration rule set.

[0100] Specifically, the operation of generating the calibration rule set first compares the obtained heat conduction path change characteristics with the historical anomaly pattern library, which stores the temperature gradient anomaly vectors and their processing solutions recorded in the previous digestion process. The comparison uses the vector space projection method to calculate the similarity. ,have:

[0101] ,

[0102] in, Represents the current heat conduction path change feature vector, Represents the feature vector in the historical abnormal pattern library, Indicates the vector modulus. When the calculation result When the threshold exceeds 0.8, the cross-cycle learning module is activated. This module decomposes the real-time deviation data into axial components along the temperature gradient and normalizes them to generate a standard deviation feature vector. This feature vector is then associated with the resolution phase parameters and converted into a calibration rule entry containing conditional trigger logic and compensation parameters. Finally, an index relationship is established between the rule entry and the historical resolution case library, enabling linked retrieval between the rule and case libraries through a bidirectional pointer.

[0103] For example, when a pharmaceutical factory digested a biological sample, an abnormal heat conduction path occurred and the system detected The axial gradient value reached 8.2°C / cm. Comparison with the historical database revealed that the pattern had a similarity of 0.91 with the cooling water pipe leak incident three months ago. The cross-cycle learning module analyzed that the deviation was mainly due to the newly installed ventilation equipment and generated the rule entry "When the ambient wind speed is greater than 2m / s and When the axial gradient exceeds 7°C / cm, initiate pre-compensation for the west heater. This rule is indexed and associated with all digestion cases using fume hoods. Subsequent digestion tasks using the same scenario are activated 30 seconds in advance. By intelligently linking physical field anomalies with equipment status changes, isolated events are transformed into reusable preventative knowledge, significantly improving the system's ability to predict complex working conditions and preventing the recurrence of similar anomalies.

[0104] Optionally, the method further includes:

[0105] Decomposing an environmental interference component in the real-time deviation data;

[0106] Building a feedback link based on a preset component compensation function and the historical resolved fingerprint data;

[0107] In conjunction with the feedback link, when the same environmental interference component is detected to occur repeatedly, a preventive calibration strategy is automatically generated;

[0108] The preventive calibration strategy is written into the calibration rule set.

[0109] Specifically, the cross-cycle learning module execution process first decomposes the environmental interference component and equipment attenuation component in the real-time deviation data, and uses the wavelet transform algorithm to separate the temperature deviation signal into high-frequency fluctuation part and low-frequency drift part. Among them, the high-frequency fluctuation amplitude exceeding the preset threshold of 0.5℃ / min is identified as the environmental interference component, and the low-frequency drift duration longer than 10 minutes is identified as the equipment attenuation component. The component compensation function is constructed using the formula, and the compensation amount for the environmental interference component is Compensation for equipment attenuation component ,have:

[0110] ,

[0111] in, is the time domain function of the environmental interference component, right Integrate over time and calculate the total interference, which is obtained through high-frequency sampling of the temperature sensor. is the peak value of the equipment attenuation component, and The compensation coefficient is calibrated through historical data analysis. A feedback link is then established between the component compensation function and historical digestion fingerprint data. When the same environmental interference component is detected to recur within three consecutive digestion cycles, a preventive calibration strategy is automatically generated. Ultimately, the preventive calibration strategy, including the compensation function parameters and trigger conditions, is written into the calibration rule set.

[0112] For example, when a semiconductor laboratory continuously processed wafer digestion during the rainy season, the system identified periodic high-frequency fluctuations at 3 pm every day through wavelet transform. Analysis confirmed that the interference was caused by the start and stop of the air conditioning system, and the environmental component compensation coefficient The cross-cycle learning module generates a strategy to automatically increase the power baseline value by 3% at 2:55 every day and write a rule entry After implementation, periodic temperature fluctuations were successfully eliminated, and the equipment attenuation component monitoring value also decreased by 40%. By intelligently identifying and predicting repetitive environmental interference, passive compensation was transformed into an active defense mechanism, significantly reducing performance degradation caused by frequent equipment adjustments while improving process stability under special operating conditions.

[0113] Optionally, the modifying the digestion process based on the calibration rule set includes:

[0114] Monitoring the version iteration status of the calibration rule set;

[0115] Based on the version iteration status, when a version update event is detected, a newly added calibration rule entry is extracted;

[0116] The control logic constraints in the newly added calibration rule entries are analyzed and the resolution process is corrected.

[0117] Specifically, the operation of dynamically correcting the operating parameters first monitors the version iteration status of the calibration rule set by comparing the version feature code of the current rule base with the feature code hash values ​​of the last three updates. When the feature code difference is detected to exceed the preset threshold, it is determined to be a version update event, and the newly added calibration rule entries are automatically extracted. The control logic constraints in the newly added entries are parsed, and the syntax tree decomposition algorithm is used to convert the text rules into triples (conditional attributes, operators, thresholds). Then, the parameter adjustment decision tree of the adaptive heating controller is reconstructed to calculate the weight values ​​of the decision tree nodes. ,have:

[0118] ,

[0119] in, Indicates the triggering frequency of the rule in historical cases. Indicates the success rate evaluation value of the last five executions. and The balancing coefficients are fixed at 0.6 and 0.4, and the leaf nodes store the specific parameter adjustments. Finally, a new decision tree containing hierarchical judgment logic is generated and loaded into the controller.

[0120] For example, a water quality monitoring station upgraded its calibration rule base and added a new entry: "When the injection volume is greater than 500ml and the viscosity is greater than The constant temperature phase is extended by 15%. Upon detecting a version update, the system automatically interprets the conditional attributes as sample volume and viscosity. When processing high-concentration sludge samples, the new decision tree identifies the conditions during the injection phase and proactively adjusts the digestion program time parameters. By intelligently converting text rules into executable control logic, a seamless transition from knowledge base updates to actuator optimization is achieved, effectively addressing the efficiency bottleneck of traditional systems requiring manual reprogramming and significantly enhancing the ability to rapidly respond to new pollutants.

[0121] Based on the same inventive concept, Figure 6 As shown, the present invention also provides a water bath heating digestion parameter calibration and monitoring system, the system comprising:

[0122] Environmental parameter acquisition module, used to obtain the environmental state parameter set of the water bath digestion system;

[0123] A thermal field modeling module is used to construct a three-dimensional thermal field dynamic mirror system with simulated temperature data of a preset water bath according to the set of environmental state parameters;

[0124] A case matching module is used to perform a matching operation on the three-dimensional thermal field dynamic mirror system and a preset historical solution case library to generate initial heating control parameters;

[0125] A heating control module, configured to drive a preset adaptive heating controller to perform a digestion operation based on the initial heating control parameters;

[0126] a deviation calculation module, configured to calculate a predicted deviation value of the three-dimensional thermal field dynamic mirror system based on the resolution operation;

[0127] A calibration trigger module, configured to trigger a parameter calibration instruction when the prediction deviation value exceeds a preset threshold;

[0128] a thermal compensation execution module, configured to respond to the parameter calibration instruction, execute a preset directional thermal compensation algorithm, and update the three-dimensional thermal field dynamic mirror system;

[0129] A rule generation module is used to perform correlation analysis between the updated three-dimensional thermal field dynamic mirror system and the historical solution case library to generate a calibration rule set;

[0130] A parameter optimization module is used to jointly analyze the calibration rule set and the adaptive heating controller to generate dynamically corrected operating parameters.

[0131] It should be noted that the electrical connections between the above-mentioned units do not necessarily mean direct connections of lines. Indirect connections are applicable to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above description is only an exemplary embodiment of the present invention and is not intended to limit the scope of the present invention.

[0132] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. Those skilled in the art will readily conceive of other embodiments of the present invention after considering the disclosure of the specification and practical truths. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary technical means in the art not described herein.

Claims

1. A method for calibrating and monitoring water bath heating digestion parameters, characterized in that: The method comprises: Obtain the environmental state parameter set of the water bath digestion system; Constructing a three-dimensional thermal field dynamic mirror system with simulated temperature data of a preset water bath according to the environmental state parameter set; Perform matching calculations on the three-dimensional thermal field dynamic mirror system and a preset historical solution case library to generate initial heating control parameters; Performing digestion based on the initial heating control parameters and collecting actual thermal field distribution data during the digestion process; Calculating the deviation between the actual thermal field distribution data and the simulated temperature data of the three-dimensional thermal field dynamic mirror system to obtain a predicted deviation value; When the predicted deviation value exceeds a preset threshold, generating a parameter calibration instruction; In response to the parameter calibration instruction, performing a directional thermal compensation operation and updating the three-dimensional thermal field dynamic mirror system; Performing correlation analysis on the updated three-dimensional thermal field dynamic mirror system and the historical solution case library to generate a calibration rule set; The digestion process is modified based on the calibration rule set.

2. A water bath heating digestion parameter calibration and monitoring method according to claim 1, characterized in that, The three-dimensional thermal field dynamic mirroring system includes: Dividing a preset physical space of the water bath into a preset number of zoned thermal units; Acquiring real-time temperature data of each of the zoned thermal units and power signals of adjacent heaters; Correlating and mapping the real-time temperature data with the power signal of the adjacent heater to construct a dynamic heat conduction matrix; A three-dimensional thermal field dynamic mirror system is generated based on the dynamic heat conduction matrix.

3. A water bath heating digestion parameter calibration and monitoring method according to claim 2, characterized in that, Generating the initial heating control parameters includes: Extracting historical resolution fingerprint data from the historical resolution case library; Calculate the current environmental state parameter set and each of the historical resolved fingerprint data to generate a similarity index; Selecting the historical resolved fingerprint data with the highest similarity index as a reference template; The three-dimensional thermal field dynamic mirror system and the reference template are spatially superimposed and analyzed to generate initial heating control parameters.

4. A water bath heating digestion parameter calibration and monitoring method according to claim 3, characterized in that, The method further comprises: Extract key process parameters from each digestion process to obtain new digestion fingerprint data; Establishing a parameter correlation matrix with a coefficient of variation based on the newly added eliminated fingerprint data and the historical eliminated fingerprint data; When the coefficient of variation in the parameter association matrix exceeds a preset tolerance, the historical solution case library is reconstructed.

5. A water bath heating digestion parameter calibration and monitoring method according to claim 3, characterized in that, The generating parameter calibration instruction comprises: Based on the digestion operation, collecting actual temperature data of the zoned thermal unit in real time; Comparing the actual temperature data with the simulated temperature data of the three-dimensional thermal field dynamic mirror system point by point to generate a local temperature difference vector; Performing weighted normalization processing on the local temperature difference vector to obtain a comprehensive prediction deviation value; When the prediction deviation value exceeds the threshold, a parameter calibration instruction is generated.

6. A water bath heating digestion parameter calibration and monitoring method according to claim 5, characterized in that, The performing of the directional thermal compensation operation and updating the three-dimensional thermal field dynamic mirroring system comprises: In response to the parameter calibration instruction, obtaining thermal field redistribution data after performing directional thermal compensation; extracting heat conduction path change characteristics based on the thermal field redistribution data; Correcting the weight coefficient of the dynamic heat conduction matrix based on the change characteristics of the heat conduction path to obtain a corrected weight coefficient; The three-dimensional thermal field dynamic mirror system is optimized based on the corrected weight coefficient.

7. A water bath heating digestion parameter calibration and monitoring method according to claim 6, characterized in that, Generating a calibration rule set includes: Comparing the heat conduction path change characteristics with a preset historical abnormal pattern library; When the comparison results meet the preset association conditions, the cross-cycle learning module is activated; Dynamically comparing the partitioned thermal unit with the three-dimensional thermal field dynamic mirror system to generate real-time deviation data; converting the real-time deviation data into calibration rule entries based on the cross-cycle learning module; An index relationship between the calibration rule entries and the historical solution case library is established to generate a calibration rule set.

8. A water bath heating digestion parameter calibration and monitoring method according to claim 7, characterized in that, The method further comprises: Decomposing an environmental interference component in the real-time deviation data; Building a feedback link based on a preset component compensation function and the historical resolved fingerprint data; In conjunction with the feedback link, when the same environmental interference component is detected to occur repeatedly, a preventive calibration strategy is automatically generated; The preventive calibration strategy is written into the calibration rule set.

9. A water bath heating digestion parameter calibration and monitoring method according to claim 1, characterized in that, The correcting the digestion process based on the calibration rule set includes: Monitoring the version iteration status of the calibration rule set; Based on the version iteration status, when a version update event is detected, a newly added calibration rule entry is extracted; The control logic constraints in the newly added calibration rule entries are analyzed and the resolution process is corrected.

10. A water bath heating digestion parameter calibration and monitoring system, applied to a water bath heating digestion parameter calibration and monitoring method according to any one of claims 1 to 9, characterized in that: The system comprises: Environmental parameter acquisition module, used to obtain the environmental state parameter set of the water bath digestion system; A thermal field modeling module is used to construct a three-dimensional thermal field dynamic mirror system with simulated temperature data of a preset water bath according to the set of environmental state parameters; A case matching module is used to perform a matching operation on the three-dimensional thermal field dynamic mirror system and a preset historical solution case library to generate initial heating control parameters; A heating control module, configured to drive a preset adaptive heating controller to perform a digestion operation based on the initial heating control parameters; a deviation calculation module, configured to calculate a predicted deviation value of the three-dimensional thermal field dynamic mirror system based on the resolution operation; A calibration trigger module, configured to trigger a parameter calibration instruction when the prediction deviation value exceeds a preset threshold; a thermal compensation execution module, configured to respond to the parameter calibration instruction, execute a preset directional thermal compensation algorithm, and update the three-dimensional thermal field dynamic mirror system; A rule generation module is used to perform correlation analysis between the updated three-dimensional thermal field dynamic mirror system and the historical solution case library to generate a calibration rule set; A parameter optimization module is used to jointly analyze the calibration rule set and the adaptive heating controller to generate dynamically corrected operating parameters.

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