Automatic pathological section adjusting method and system

Abstract

The invention relates to the technical field of automatic control, in particular to a pathological section automatic adjusting method and system, and the method comprises the following steps: based on the real-time operation state of a microscope stage, analyzing the load and the moving track of the pathological section stage, adjusting the motion parameters of the microscope stage, and responding to the deviation of the section stage. And adjusting position parameters of the slice by combining slice thickness measurement, and generating a platform deck movement adjustment scheme. According to the invention, through real-time analysis of the operation state of the carrying platform, dynamic adjustment of the load and the track, improvement of the motion accuracy and stability, reduction of the positioning error, combination of the slice thickness, optimization of position parameters, enhancement of the processing consistency and efficiency, reduction of dislocation and invalid motion through track optimization monitoring, adjustment of the batch sequence and deformation correction optimization, and equalization of sample distribution. High-throughput operation efficiency is improved, slice offset is dynamically corrected, accurate positioning under different deformation conditions is ensured, automatic adaptability is improved, and errors and time cost are remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to a method and system for automatically adjusting pathological sections. Background Art

[0002] The field of automated control technology involves the use of a variety of control devices and information technologies to automatically operate and manage equipment and processes, with no or little human intervention required. It relies on the collaborative work of sensors, actuators, control units, and software. In an automated control environment, sensors are responsible for collecting data on the environment or equipment status, and control units (such as microprocessors or digital computers) process this data based on preset algorithms, and then guide actuators to adjust equipment operations according to established instructions. This technology is widely used in manufacturing, mechanical engineering, aerospace, transportation, and energy management. Its main purpose is to improve efficiency, safety, and economy, while reducing human errors and operating costs.

[0003] Among them, the automatic adjustment method of pathology sections refers to the use of automated equipment to adjust the position and orientation of pathology sections to ensure that they are correctly placed for further microscopic examination or image analysis. This method includes automatically loading slices, adjusting slices to the appropriate focal plane, and accurately positioning slices to capture key image areas. Its main purpose is to improve the accuracy and repeatability of sample processing in a laboratory environment, while reducing the time and labor intensity of sample preparation. By automatically adjusting slices, it can ensure that each slice can be properly analyzed, supporting high-throughput and high-precision pathology examinations, thereby providing reliable data for pathology research and related fields.

[0004] The existing technology lacks precise real-time monitoring and adjustment means for the motion trajectory of the microscope stage during the slice processing process, which leads to sample misalignment or repeated adjustment problems when the stage deviates and the trajectory is not optimized, increasing operation time and the need for human intervention. The existing technology is insufficient in the precise processing of slice thickness or deformation correction, and lacks the ability to dynamically adjust the logical allocation of batch processing, resulting in poor repeatability and consistency of analysis results between different samples. In traditional methods, the correction of slice position mostly relies on fixed preset algorithms, which cannot flexibly deal with sample differences. Especially in high-throughput processing scenarios, inefficient operation modes can easily lead to system bottlenecks. The lack of integration between the motion parameter adjustment of the microscope stage and the sample characteristics makes it difficult for the existing technology to effectively deal with complex or deformed samples, directly affecting the accuracy and reliability of pathological slice analysis, limiting the actual application capabilities of current technology in large-scale automated processing, and unable to fully meet the needs of efficient and high-precision pathological sample analysis. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for automatically adjusting pathological sections.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for automatically adjusting pathological sections, comprising the following steps:

[0007] S1: Based on the real-time operation status of the microscope stage, analyze the load and movement trajectory of the pathology slide stage, adjust the motion parameters of the microscope stage, address the deviation of the slide stage, adjust the position parameters of the slide in combination with the slice thickness measurement, and generate a stage motion adjustment plan;

[0008] S2: Based on the stage motion adjustment scheme, the deviation value of the motion trajectory during the slicing operation is recorded, and the stage motion path is optimized in combination with the microscope stage motion record. The optimized motion trajectory data is obtained, and the motion trajectory is monitored in real time. The motion trajectory path nodes are adjusted to match the real-time requirements, and a trajectory optimization execution plan is generated.

[0009] S3: Based on the trajectory optimization execution plan, adjust the slice batch allocation, record the numerical deviation of the slice deformation before and after correction, analyze the optimization degree of the motion path in the slice deformation correction, optimize the batch processing order, obtain the slice batch adjustment data, adjust the slice allocation order within the batch, analyze the characteristics of the differentiated batch slice data and optimize the arrangement logic, and generate a correction adjustment execution plan;

[0010] S4: Based on the correction and adjustment execution plan, correct the slice position offset, dynamically adjust the position offset in combination with the pathology slice stage motion trajectory, record the real-time adjustment parameters of the microscope equipment, and generate a pathology slice deformation correction and stabilization control plan in combination with the slice stage motion control data.

[0011] As a further solution of the present invention, the steps for obtaining the stage motion adjustment plan are specifically as follows:

[0012] S111: Based on the real-time operating status of the microscope stage, analyze the real-time load and movement trajectory of the pathology section stage, collect data from the load sensor and position encoder, analyze the motion deviation of the stage under different operating conditions, and obtain motion deviation data;

[0013] S112: Using the motion deviation data and the slice thickness data, combined with the stage speed and acceleration information, a multivariate linear regression model is used, using the formula:

[0014] y=b0+b1x1+b2x2+b3x3

[0015] Calculate the optimal position of the carrier and obtain the carrier position adjustment data;

[0016] Where y represents the optimized position of the stage, x1 represents the motion deviation, x2 represents the slice thickness, x3 represents the stage speed, and b0, b1, b2, and b3 are model coefficients;

[0017] S113: According to the stage position adjustment data, the motion control parameters of the microscope stage are adjusted to optimize the motion trajectory of the stage and obtain a stage motion adjustment plan.

[0018] As a further solution of the present invention, the step of obtaining the optimized motion trajectory data is specifically as follows:

[0019] S211: Based on the stage motion adjustment scheme, the displacement data of the stage during the slicing operation is recorded, the deviation value of each position point is collected, the degree of deviation at each moment is identified by comparison with the preset trajectory, and the data is calibrated in combination with the timestamp to generate motion trajectory deviation data;

[0020] S212: Using the motion trajectory deviation data, comparing the deviation values ​​of the differentiated positions, and combining the motion speed and acceleration of each point, adjusting the corresponding control parameters, and obtaining optimized control parameter data by correcting the acceleration and speed balance motion trajectory;

[0021] S213: Replanning the path of the platform according to the optimized control parameter data in combination with the current speed, acceleration and corrected position, performing path analysis according to the adjusted path points, speed and acceleration, and obtaining optimized motion trajectory data.

[0022] As a further solution of the present invention, the steps of obtaining the trajectory optimization execution plan are specifically as follows:

[0023] S221: extracting key path nodes in the trajectory based on the optimized motion trajectory data, including trajectory curvature change points, speed change points, and direction change points, classifying and arranging the node data, and generating a path node set;

[0024] S222: Based on the path node set and the real-time monitored motion trajectory requirement parameters, the matching degree between the trajectory path and the real-time requirement is analyzed using the formula:

[0025]

[0026] Calculate the path matching degree, adjust the positions of mismatched nodes and optimize the overall path to generate an optimized path node set;

[0027] Among them, C represents the path matching degree, E i is the ideal value of the path node, y i is the current node value, w i is the weight coefficient, n is the number of path nodes;

[0028] S223: According to the optimized path node set and in combination with the trajectory changes monitored in real time, the trajectory path is dynamically adjusted, the updated path is set as the execution plan, the effect of the trajectory adjustment is verified through a real-time feedback mechanism, and a trajectory optimization execution plan is generated.

[0029] As a further solution of the present invention, the step of obtaining the slice batch adjustment data is specifically as follows:

[0030] S311: Analyze the numerical deviation of each slice before and after deformation according to the trajectory optimization execution plan, analyze the deformation trend of each batch of slices, correct the deformation of the slices based on the data before and after deformation, evaluate the deformation correction effect of each batch of slices, and obtain slice deformation optimization data;

[0031] S312: Based on the slice deformation optimization data, analyzing the processing efficiency of each slice under the differentiated processing order, evaluating the optimization of the batch processing order in combination with the deformation correction effect, identifying the adjustment optimization degree of the batch in combination with the weight value of each batch processing, and obtaining batch processing order optimization data;

[0032] S313: According to the batch processing sequence optimization data, the slice batch sequence is adjusted, and the batch adjustment is performed in combination with the target optimization data, using the formula:

[0033]

[0034] Get slice batch adjustment data;

[0035] Among them, T adjust Indicates the adjustment value of the slice batch, d k represents the deformation deviation of slice k, S k represents the deformation degree of slice k, w k represents the processing order weight of slice k, D k represents the correction accuracy of slice k, F k represents the processing feedback value of slice k, α, β and γ are adjustment coefficients, and m represents the total number of slices.

[0036] As a further solution of the present invention, the step of obtaining the correction and adjustment execution plan is specifically as follows:

[0037] S321: Based on the slice batch adjustment data, the slice data is rearranged, the slices are allocated to differentiated batches in a preset order, and the data is refined into multiple sub-batches according to the slice attribute characteristics. The slice attribute homogeneity within each batch is used for secondary allocation to generate a batch slice adjustment result;

[0038] S322: Based on the batch slice adjustment result, compare the numerical characteristics of the slices in each sub-batch, identify the degree of difference of each slice in the same batch, perform sorting and adjustment, arrange the slice order according to the degree of difference, and adjust the slice allocation ratio in each sub-batch to generate a differentiated slice order optimization result;

[0039] S323: Based on the differentiated slicing sequence optimization results and in combination with the slicing characteristics of each sub-batch, the overall batch sorting optimization is performed, adjustments are made according to the characteristics of the batch, and a correction adjustment execution plan is generated.

[0040] As a further solution of the present invention, the steps for obtaining the pathological section deformation correction and stabilization control solution are specifically as follows:

[0041] S411: Based on the correction and adjustment execution plan, extract the associated data of the motion trajectory of the slice stage, smooth the motion trajectory, combine the real-time position parameters of the microscope device, perform preliminary offset calculation of the slice position, and generate a slice displacement correction value based on the difference between the real-time position and the target position;

[0042] S412: Call the slice displacement correction value, combine the real-time parameters of the microscope device and the dynamic adjustment coefficient, and set the correction adjustment coefficient using the formula:

[0043] ΔP=G·|ΔT-H|+M·(ΔT-λ) 1 / 2

[0044] Get the slice position correction offset;

[0045] Where ΔP represents the slice position correction offset, ΔT represents the real-time offset of the slice, G is the position correction weight coefficient, H is the slice dynamic offset threshold, M is the correction weight coefficient, and λ is the dynamic adjustment coefficient;

[0046] S413: Perform deformation correction analysis based on the slice position correction offset, combined with the motion trajectory data of the slice stage and the real-time adjustment parameters of the microscope device, to generate a pathology slice deformation correction and stabilization control solution.

[0047] A pathology section automatic adjustment system, the pathology section automatic adjustment system is used to perform the pathology section automatic adjustment method described above, the system comprising:

[0048] The stage operation monitoring module records the movement trajectory and load status data of the microscope stage based on its real-time operation status. It analyzes the relationship between offset and load based on the trajectory and load data, identifies key offset nodes, analyzes the nodes, and obtains offset-load interaction data.

[0049] The trajectory optimization module selects key trajectory nodes based on the offset load interaction data, adjusts node parameters through node deviation data, matches real-time load changes, re-arranges the node sequence to optimize the trajectory, and generates an optimized trajectory node sequence;

[0050] The batch adjustment module monitors the deformation deviation before and after the slice batch based on the optimized trajectory node sequence, analyzes the deformation and deviation data, adjusts the order of the slices in the batch, and obtains the adjusted batch sequence by rearranging the slices in the batch;

[0051] The deformation correction module measures the position offset of the slices based on the adjusted batch sequence, compares the slice position offset data, adjusts the position of each slice according to the data, and obtains slice position accuracy data;

[0052] The dynamic control module updates the microscope equipment control parameters based on the slice position accuracy data, synchronizes the dynamic data of the slice stage, adjusts the motion trajectory according to the current slice position, and establishes a pathological slice deformation correction and stabilization control solution.

[0053] Compared with the prior art, the advantages and positive effects of the present invention are:

[0054] In the present invention, by analyzing the real-time operating status of the pathology slice carrier, combining the dynamic adjustment of the carrier load and the moving trajectory, the accuracy and stability of the microscope carrier motion are improved, the sample positioning error caused by the carrier deviation is effectively reduced, the slice position parameters are dynamically adjusted and combined with the slice thickness measurement, so that the slice position adjustment is more accurate, and the consistency and efficiency of the sample processing are significantly improved. The trajectory deviation during the operation is recorded and the motion path is optimized, and real-time monitoring and path optimization of the motion trajectory are achieved, effectively reducing the slice dislocation and unnecessary movement, and improving the accuracy of sample analysis. Based on the adjustment of the batch processing order and the analysis of the slice deformation data, efficient optimization of the slice deformation correction is achieved, the uneven distribution of samples within the batch is reduced, and the operating efficiency in the high-throughput environment is improved. The slice offset is dynamically corrected and combined with the adjustment parameters of the microscope equipment to ensure that pathology slices can obtain stable and accurate positioning under different deformation conditions. By recording the carrier control data in real time and adjusting the operation logic, the adaptability of the overall method is enhanced, and reliable support is provided for the automated processing of large-scale pathology samples. While improving the accuracy and consistency of slice processing, the error and time cost caused by manual operation are significantly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0056] Figure 2 This is a flow chart of the stage motion adjustment scheme of the present invention;

[0057] Figure 3 This is a flow chart of the optimized motion trajectory data in the present invention;

[0058] Figure 4 Flowchart of the trajectory optimization execution plan in the present invention;

[0059] Figure 5 This is a flow chart of slicing batch adjustment data in the present invention;

[0060] Figure 6 A flowchart of the correction and adjustment execution plan in the present invention;

[0061] Figure 7 This is a flow chart of the pathological section deformation correction and stabilization control solution in the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0063] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0064] Example 1

[0065] See also Figure 1 The present invention provides a technical solution: a method for automatically adjusting pathological sections, comprising the following steps:

[0066] S1: Based on the real-time operation status of the microscope stage, analyze the load and movement trajectory of the pathology slide stage, adjust the motion parameters of the microscope stage, address the deviation of the slide stage, adjust the position parameters of the slide in combination with the slice thickness measurement, and generate a stage motion adjustment plan;

[0067] S2: Based on the stage motion adjustment plan, the deviation value of the motion trajectory during the slicing operation is recorded, and the stage motion path is optimized in combination with the microscope stage motion record. The optimized motion trajectory data is obtained, and the motion trajectory is monitored in real time. The motion trajectory path nodes are adjusted to match the real-time requirements, and a trajectory optimization execution plan is generated.

[0068] S3: Optimize the execution plan based on the trajectory, adjust the slice batch allocation, record the numerical deviation before and after the slice deformation correction, analyze the optimization degree of the motion path in the slice deformation correction, optimize the batch processing order, obtain the slice batch adjustment data, adjust the slice allocation order within the batch, analyze the characteristics of the differentiated batch slice data and optimize the arrangement logic, and generate the correction adjustment execution plan;

[0069] S4: Based on the correction and adjustment execution plan, the slice position offset is corrected, the position offset is dynamically adjusted in combination with the pathology slice stage motion trajectory, the real-time adjustment parameters of the microscope equipment are recorded, and the pathology slice deformation correction and stabilization control scheme is generated in combination with the slice stage motion control data.

[0070] The carrier motion adjustment plan includes parameter optimization, deviation response, and slice positioning. The optimized motion trajectory data includes path deviation value, path adjustment plan, and monitoring feedback. The trajectory optimization execution plan includes path node optimization, real-time monitoring, and deviation data collection. The slice batch adjustment data includes batch sorting logic, deformation correction effect, and batch performance analysis. The correction adjustment execution plan includes batch allocation optimization, numerical deviation recording, and data characteristic analysis. The pathological slice deformation correction and stabilization control plan includes position offset correction, trajectory dynamic adjustment, and control parameter recording.

[0071] See also Figure 2 , the specific steps for obtaining the stage motion adjustment plan are:

[0072] S111: Based on the real-time operating status of the microscope stage, analyze the real-time load and movement trajectory of the pathology section stage, collect data from the load sensor and position encoder, analyze the motion deviation of the stage under different operating conditions, and obtain motion deviation data;

[0073] By real-time monitoring of the load and movement trajectory of the pathology section carrier, collecting data from load sensors and position encoders, and analyzing the motion deviation of the carrier under different operating conditions through real-time data processing, data acquisition, data filtering and preliminary analysis are implemented in this process to remove noise and outliers. The data is integrated to estimate the motion abnormalities of the carrier, and the load transfer parameters are adjusted according to the abnormalities, thereby optimizing the motion performance of the carrier and ensuring the accuracy and reliability of the data. The motion deviation data can be accurately obtained. The data directly affects the calibration of the slice carrier and the final operating accuracy. By performing time series analysis on the data output by the load sensor and position encoder, problems that may arise in the future operation of the carrier can be predicted and adjusted in advance, including the use of advanced statistical methods and machine learning techniques to parse the data, extract useful information, and then apply the information to optimize the control strategy of the carrier movement.

[0074] S112: Using the motion deviation data and the slice thickness data, combined with the stage speed and acceleration information, a multivariate linear regression model is used, using the formula:

[0075] y=b0+b1x1+b2x2+b3x3

[0076] Calculate the optimal position of the carrier and obtain the carrier position adjustment data;

[0077] Where y represents the optimized position of the stage, x1 represents the motion deviation, x2 represents the slice thickness, x3 represents the stage speed, and b0, b1, b2, and b3 are model coefficients;

[0078] The formula is beneficial in that it takes into account multiple dynamic variables (motion deviation, slice thickness, stage speed), thus enabling more accurate prediction and adjustment of the stage position;

[0079] Set the current position of the stage to a deviation of x1 = 0.05 mm, the thickness of the slice to x2 = 0.5 mm, the speed of the stage to x3 = 2 mm / s, and the regression coefficients to b0 = 0, b1 = 1, b2 = 0.8, and b3 = 0.1;

[0080] According to the formula, y = 0 + 1 × 0.05 + 0.8 × 0.5 + 0.1 × 2 = 0.85 mm;

[0081] The results show that based on the current dynamic deviation, slice thickness and stage speed, the stage should be adjusted to a new position, namely 0.85mm, to maintain the best operating effect and slice quality. The integration of real-time monitoring data and historical calibration data makes the stage adjustment more precise, reduces errors during operation, and thus improves overall operating efficiency and slice quality.

[0082] S113: adjusting the motion control parameters of the microscope stage according to the stage position adjustment data, optimizing the motion trajectory of the stage, and obtaining a stage motion adjustment plan;

[0083] Adjust the motion control parameters of the microscope stage, including motor speed, step accuracy and stop point adjustment. The control parameters are optimized according to the predicted optimal stage position. Each control point is fine-tuned through the control algorithm to ensure that the stage moves smoothly and accurately to the preset position. Real-time feedback correction is also required to deal with deviations and external disturbances. In this way, the optimized stage motion trajectory ensures the precise alignment of the slice and the microscope light source, thereby improving the accuracy of observation and analysis. Finally, the stage motion adjustment scheme is obtained. By continuously collecting operating data, the parameters can be learned and automatically adjusted to adapt to different operating conditions and slice types. The process involves complex data analysis and real-time calculation to ensure that the adjustment scheme is highly personalized and adaptable.

[0084] See also Figure 3 ,The specific steps for obtaining the optimized motion trajectory data are:

[0085] S211: Based on the stage motion adjustment plan, the displacement data of the stage during the slicing operation is recorded, and the deviation value of each position point is collected. By comparing with the preset trajectory, the degree of deviation at each moment is identified, and the data is calibrated in combination with the timestamp to generate motion trajectory deviation data;

[0086] First, the displacement data of the platform is obtained, and the actual deviation of each position point is recorded based on the timestamp. For each position point, the deviation value at each moment is obtained by calculating the difference between its current coordinates and the corresponding position in the preset trajectory. If the deviation value exceeds the preset allowable error range, further calibration is required. In the calibration step, the timestamp is used to map the actual deviation value of each position point to ensure that each data point is aligned on the time axis to achieve the purpose of accurate calibration. The calibration process ensures the accuracy of the subsequent trajectory data and eliminates errors caused by factors such as mechanical deviation and changes in the external environment. The obtained motion trajectory deviation data can fully reflect the error distribution between the actual motion trajectory and the preset trajectory, providing an important basis for the subsequent adjustment of control parameters.

[0087] S212: Using the motion trajectory deviation data, comparing the deviation values ​​of the differentiated positions, and combining the motion speed and acceleration of each point, adjusting the corresponding control parameters, and obtaining optimized control parameter data by correcting the acceleration and speed balance motion trajectory;

[0088] First, the deviation of each position point is differentially analyzed from the deviation of the adjacent position points to identify areas with larger deviations. For each area, the influence of the deviation on the motion state is calculated in combination with the motion speed and acceleration at the moment, and then the control parameters are adjusted. By comparing the acceleration and speed changes between different position points, it is determined whether the acceleration needs to be appropriately increased or decreased in the position segment with larger deviation to ensure the smoothness and accuracy of the trajectory. For those intervals with too large or too small acceleration, the parameters of the control system are appropriately adjusted to achieve a balance between speed and acceleration, ensuring that the trajectory of the platform at each position point can be as close to the preset trajectory as possible, and the motion process remains smooth, reducing vibration and error accumulation, and obtaining optimized control parameter data, which can accurately guide the movement of the platform and ensure the accuracy and stability of the trajectory.

[0089] S213: Replanning the path of the platform based on the optimized control parameter data, combined with the current speed, acceleration, and corrected position, performing path analysis based on the adjusted path points, speed, and acceleration to obtain optimized motion trajectory data;

[0090] First, the path planning of the carrier is recalculated in combination with the optimized control parameter data. The speed, acceleration and corrected position of each path point are used as key inputs for path replanning. By comparing the new control parameters and the corrected trajectory points, the motion path between each path point is determined. The speed and acceleration requirements of each path point are further analyzed and optimized. The adjusted path analysis must not only ensure the continuity of the path, but also take into account the smoothness of the transition between different path segments to avoid motion shocks caused by drastic changes. By comparing the relationship between the actual speed, acceleration and the optimized parameters, the optimal path of the carrier is redrawn, and the path planning algorithm is used to ensure that the gap between the motion trajectory and the preset trajectory is minimized. Through the path analysis process, the optimized motion trajectory data is obtained. This data can provide more precise and stable motion control for the carrier, ensuring that it can accurately perform slicing operations.

[0091] See also Figure 4 ,The specific steps for obtaining the trajectory optimization execution plan are:

[0092] S221: Extract key path nodes in the trajectory based on the optimized motion trajectory data, including trajectory curvature change points, speed change points, and direction change points, classify and organize the node data, and generate a path node set;

[0093] Based on the motion trajectory data obtained by the platform sensor, the key path nodes in the trajectory are first identified, including the trajectory curvature change points, speed change points and direction change points. The change points are extracted by mathematical analysis of the trajectory point data. The curvature change points are calculated by the second-order derivative of the trajectory, the speed change points are judged based on the rate change of the trajectory, and the direction change points are obtained by calculating the angular change of the trajectory. Then, the key nodes are classified and sorted according to the actual measured motion data. Using cluster analysis or classification technology, the path nodes are classified into different groups according to their characteristics to form a path node set, which lays the foundation for subsequent trajectory optimization and will serve as the basic data for subsequent trajectory adjustment and optimization, and ultimately provides actionable data information for adjusting the path nodes.

[0094] S222: Based on the path node set and the real-time monitored motion trajectory requirement parameters, the matching degree between the trajectory path and the real-time requirement is analyzed using the formula:

[0095]

[0096] Calculate the path matching degree, adjust the positions of mismatched nodes and optimize the overall path to generate an optimized path node set;

[0097] Among them, C represents the path matching degree, E i is the ideal value of the path node, y i is the current node value, w iis the weight coefficient, n is the number of path nodes;

[0098] The benefit of the formula is that by introducing the weight coefficient w i , the optimization requirements of different path nodes can be processed differently, thereby improving the accuracy of path matching and making the adjustment of the optimized path more in line with actual motion requirements;

[0099] This formula is used to calculate the path matching degree C, where E i represents the ideal position of the optimized path node, y i Indicates the coordinates of the current actual path node, weight coefficient w i Used to emphasize the relative importance of different nodes and calculate the difference in the position of each path node |E i -y i |, then multiply the difference value by the corresponding weight coefficient w i , and sum up to get the total difference value, calculate the length of the optimized path and the current path in space, and finally calculate the path matching degree through this formula;

[0100] When performing specific calculations, if the total number of path nodes is set to 5, the differences of all path nodes need to be calculated according to the above formula;

[0101] Set the position of the path node to:

[0102] Optimized path E = [2, 3, 4, 5, 6];

[0103] Actual path y = [2.1, 3.1, 4.2, 4.8, 6.2];

[0104] And the weight coefficient w = [1, 1, 1, 1, 1];

[0105] The calculation process of matching degree C is:

[0106] First calculate

[0107]

[0108] calculate

[0109]

[0110] calculate

[0111]

[0112] Therefore, the matching degree C is:

[0113]

[0114] The result shows that the path matching degree is low, which means that there is a large deviation between the current path and the optimized path, and further optimization is needed.

[0115] S223: Dynamically adjust the trajectory path based on the optimized path node set and the trajectory changes monitored in real time, set the updated path as the execution plan, verify the effect of the trajectory adjustment through a real-time feedback mechanism, and generate a trajectory optimization execution plan;

[0116] Based on the set of path nodes and the real-time monitored motion trajectory demand parameters, the path node set is analyzed, and the deviation between each node and the real-time demand path is calculated. The above-mentioned matching degree calculation method is used to determine the adjustment requirements of the path nodes. According to the analysis results, the trajectory path is adjusted to make the path more in line with the real-time demand. The specific adjustment process is carried out dynamically according to the real-time data. By combining the real-time feedback information of the path nodes, the adjusted trajectory path will form a new optimized path. As part of the execution plan, the real-time feedback mechanism will verify the performance of each updated path node to ensure that the path adjustment achieves the expected effect. Finally, the trajectory optimization execution plan is output, and through further monitoring and adjustment, the effectiveness of the path optimization plan in long-term operation is ensured.

[0117] See also Figure 5 , the steps for obtaining slice batch adjustment data are as follows:

[0118] S311: Analyze the numerical deviation of each slice before and after deformation according to the trajectory optimization execution plan, analyze the deformation trend of each batch of slices, correct the deformation of the slices based on the data before and after deformation, evaluate the deformation correction effect of each batch of slices, and obtain slice deformation optimization data;

[0119] First, the initial allocation parameters of each slice are extracted, including slice number, size, initial deformation data, relative deviation, and deviation value. The data can be collected in real time during the allocation process by high-precision sensors or monitoring systems. The numerical deviation of each slice before and after deformation is calculated by comparing the initial shape of the slice with the numerical data after deformation (such as the values ​​measured by strain gauges). Using the collected data, according to the set deformation rules, the deformation data of each slice is gradually collected, and the collection frequency is adjusted according to the data fluctuation. Based on the obtained data, the trend of the variation of each slice's deviation value is further analyzed to determine the correction direction of each slice. The correction coefficient of each slice is obtained and quantified by calculation. A specific mathematical model (such as the least squares method) is used for parameter fitting to generate a deformation adjustment model for each slice. By calling the deformation characteristics of the slice under different parameters and combining it with the existing data for classification calculation, the deformation trend of each slice is finally modeled. By comparing the deviation parameters in the model before and after correction, it is confirmed whether it meets the predetermined error range. If the model error does not meet the standard, it is necessary to further optimize the model until the deviation value remains within the acceptable range, and finally obtain the optimized slice deformation data.

[0120] S312: Based on the slice deformation optimization data, the processing efficiency of each slice under the differentiated processing order is analyzed, the optimization of the batch processing order is evaluated in combination with the deformation correction effect, and the adjustment optimization degree of the batch is identified in combination with the weight value of each batch processing, and the batch processing order optimization data is obtained;

[0121] Each slicing batch is refined and the deformation correction effect value of each slicing batch is extracted. The effect value can be obtained by comparing the slicing data before and after correction. For example, the correction effect of each batch can be evaluated by comparing the deformation coefficient and the deviation value. The initial data and the corrected data of each batch will generate a processing data set for comparative analysis. The batch data is reordered by combining the initial allocation data of the slicing batch and the weight value of the batch processing order after correction. The weight value is set according to the priority of different batch processing, such as the adjustment of the processing order and the importance of deformation correction. It can be set based on the actual application environment or the priority of the production process. In order to optimize the batch processing efficiency, the slicing efficiency calculation model under different processing orders is called multiple times to estimate the processing efficiency under different sorting methods. By comparing the data before and after adjustment, the influence of various processing orders on the final optimization effect is analyzed, and the sorting is further adjusted. According to the comprehensive adjustment optimization degree and efficiency results, the optimized data of the batch processing order is obtained, providing a more accurate reference and basis for subsequent production processes.

[0122] S313: Optimize the data according to the batch processing order, adjust the slice batch order, and adjust the batch according to the target optimization data. The formula is:

[0123]

[0124] Get slice batch adjustment data;

[0125] Among them, T adjust Indicates the adjustment value of the slice batch, d k represents the deformation deviation of slice k, S k represents the deformation degree of slice k, w k represents the processing order weight of slice k, D k represents the correction accuracy of slice k, F k represents the processing feedback value of slice k, α, β and γ are adjustment coefficients, and m represents the total number of slices;

[0126] The formula is beneficial in that it optimizes the slice batch processing order and deformation correction through a combination of multiple parameters. It uses nonlinear operations to adapt to complex deformation characteristics and batch processing dynamics, making the data balance in the processing process more refined, helping to improve the optimization effect of the overall system and avoid excessive bias in the final result due to a single weight.

[0127] α, β, and γ are adjustment coefficients, which perform weighted adjustments on deformation, correction, and feedback, respectively;

[0128] Set d k =0.05, S k =1.2, w k =0.8, D k =1.5, F k =0.6, α=0.3, β=0.5, γ=0.4, and put them into the formula for calculation item by item:

[0129] The first part of the calculation:

[0130]

[0131] The second part of the calculation:

[0132]

[0133] The third part of the calculation:

[0134]

[0135] Comprehensive calculation:

[0136] T adjust ≈0.0418·0.376·0.516≈0.0081

[0137] This result shows that the T calculated by the formula adjustThe value is 0.0081, which represents the final adjustment value of the slice batch. The adjustment value can help optimize the order of subsequent batches and affect the correction effect of each slice, ensuring the optimal combination of batch processing order and deformation correction.

[0138] See also Figure 6 The specific steps for obtaining the correction and adjustment execution plan are as follows:

[0139] S321: Based on the slice batch adjustment data, the slice data is rearranged, and the slices are allocated to differentiated batches in a preset order. The data is further divided into multiple sub-batches according to the slice attribute characteristics. The slice attribute homogeneity within each batch is used for secondary allocation to generate a batch slice adjustment result.

[0140] The slice data that has been preliminarily divided are arranged in a predetermined order. The data structure and attributes of each slice are carefully recorded and pre-processed. Based on the physical properties, functional characteristics or quality grade of the slice, a preset sorting rule is set, and the slices are classified according to the characteristics and the predetermined attribute threshold. For example, in the quality allocation process, if the quality grades of the slices are divided into three categories: A, B, and C, each type of slice will be further divided into multiple sub-batches according to its specific attribute characteristics. For example, Class A slices are divided secondary according to thickness, surface flatness, hardness, etc., and Class B slices are allocated according to indicators. The slices in each sub-batch will be rearranged according to their homogeneity to ensure that the attributes of the slices in each sub-batch are highly consistent, which helps to optimize the consistency between batches in the subsequent processing process. Through the above operations, the slice data in each sub-batch is obtained, and the batch slice adjustment results are formed to ensure that the slices in each batch are more homogeneous, preparing for subsequent differentiated processing.

[0141] S322: Based on the batch slice adjustment result, compare the numerical characteristics of the slices in each sub-batch, identify the degree of difference of each slice in the same batch, perform sorting and adjustment, arrange the slice order according to the degree of difference, and adjust the slice allocation ratio in each sub-batch to generate a differentiated slice order optimization result;

[0142] For slices within a certain batch, compare indicators such as surface finish, dimensional deviation, and color difference. Based on the indicators, first calculate the degree of difference of each slice and identify the relative difference between each slice. Assuming that in a certain sub-batch, the thickness of several slices varies greatly, or the appearance deviation of some slices is significantly higher than that of other slices, they can be sorted according to the degree of difference. For example, the slices with the smallest thickness deviation are placed in the front, and the slices with larger thickness deviation are placed in the back. After sorting, adjust the distribution ratio of the slices according to the attribute differences of each slice to ensure that in subsequent processing, slices with large differences will not affect the overall quality or processing results. You can also use the numerical characteristics of the slices, such as hardness, color difference, density, etc., to perform weighted comparison. Through quantitative comparison of the characteristics and weight adjustment, finally generate differentiated slice order optimization results to ensure that the order and distribution ratio of the slices in the entire batch can minimize the impact of the differences.

[0143] S323: Based on the differentiated slicing sequence optimization results and in combination with the slicing characteristics of each sub-batch, the overall batch sorting is optimized, adjustments are made based on the batch characteristics, and a correction and adjustment execution plan is generated;

[0144] Based on the optimized differentiated slicing sequence, the entire batch is sorted and optimized by integrating the slicing characteristics of each sub-batch. The slicing characteristics of each sub-batch are integrated. For example, the slice thickness, density, surface finish and other attributes of each sub-batch are summarized and the total difference is calculated. If the characteristics between some batches are very different, the order of the batches is adjusted to ensure that it will not cause too much impact on the overall production process. Based on the difference information, the optimal arrangement of the slices within the batch is performed. For example, the slices in some batches with similar properties can be arranged together to reduce the adjustment time and quality fluctuations during the processing. For batches with large differences in characteristics, they can be optimized and allocated to avoid being processed at the same time. A correction adjustment execution plan is generated to clearly indicate the sorting and allocation ratio of slices within each batch to ensure high efficiency and product consistency in the production process. The execution plan will combine the characteristics of each batch and specify how to adjust the allocation ratio, sequence and subsequent processing steps of the slices within the batch to optimize overall production efficiency.

[0145] See also Figure 7 ,The specific steps for obtaining the pathological section deformation correction and stabilization control scheme are:

[0146] S411: Based on the correction and adjustment execution plan, extract the associated data of the motion trajectory of the slice stage, smooth the motion trajectory, combine the real-time position parameters of the microscope device, perform preliminary offset calculation of the slice position, and generate a slice displacement correction value based on the difference between the real-time position and the target position;

[0147] Collect the motion trajectory data of the slice stage, obtain the displacement of each motion cycle, and combine it with the real-time position parameters of the microscope equipment to perform preliminary slice position offset calculation to obtain the difference between the target position and the actual position. It is necessary to collect the motion trajectory data of the slice stage. The data can be obtained through the displacement parameters recorded by the microscope motion system, including the displacement of each cycle. By smoothing the displacement of each motion cycle, accurate displacement data can be obtained. The real-time position parameters provided by the microscope equipment can be used to determine the actual position of the slice. The position parameters include the position of the microscope lens and the real-time movement parameters of the stage. By comparing the difference between the actual position and the target position, the preliminary position offset of the slice is calculated. By combining the motion trajectory and real-time position data, the slice displacement correction value is obtained. This correction value provides basic data for subsequent adjustments.

[0148] S412: Call the slice displacement correction value, combine the real-time parameters of the microscope device and the dynamic adjustment coefficient, and set the correction adjustment coefficient using the formula:

[0149] ΔP=G·|ΔT-H|+M·(ΔT-λ) 1 / 2

[0150] Get the slice position correction offset;

[0151] Where ΔP represents the slice position correction offset, ΔT represents the real-time offset of the slice, G is the position correction weight coefficient, H is the slice dynamic offset threshold, M is the correction weight coefficient, and λ is the dynamic adjustment coefficient;

[0152] The benefit of this formula is that it effectively corrects the slice position by introducing a position offset and a dynamic adjustment coefficient, while controlling the relationship between correction accuracy and dynamic offset. The formula uses the correction weight coefficient and the position offset threshold to accurately calculate the final correction offset, avoiding overcorrection or unnecessary adjustment.

[0153] ΔP represents the slice position correction offset, ΔT represents the real-time offset of the slice, G is the position correction weight coefficient, H is the slice dynamic offset threshold, M is the correction weight coefficient, and λ is the dynamic adjustment coefficient;

[0154] Calculate the real-time offset ΔT of the slice, which is based on the difference between the slice position data obtained in the previous step and the target position, and set the dynamic offset threshold H to determine the correction range of the offset;

[0155] The correction offset ΔP is calculated using the formula. For example, if the correction weight coefficient G is set to 0.8, the dynamic adjustment coefficient λ is set to 0.5, the correction weight coefficient M is set to 1.2, the real-time offset ΔT is set to 2.5, and the offset threshold H is set to 1.8, the calculation process is as follows:

[0156] ΔP=0.8·|2.5-1.8|+1.2·(2.5-0.5) 1 / 2

[0157] ΔP=0.8·0.7+1.2·1.414=0.56+1.697=2.257

[0158] This result shows that the final corrected offset of the slice is 2.257 units, which means that the slice position of the microscope device needs to be adjusted according to this correction value to ensure that the target position is reached.

[0159] S413: Perform deformation correction analysis based on the slice position correction offset, combined with the motion trajectory data of the slice stage and the real-time adjustment parameters of the microscope device, to generate a pathology slice deformation correction and stabilization control solution;

[0160] First, slice position correction and stabilization control values ​​need to be called. This data can be obtained from the slice displacement correction values ​​and stabilization control information obtained in the previous stage. Based on these parameters and combined with the real-time position adjustment of the microscope equipment, a deformation correction scheme is derived. This process involves real-time adjustment parameters of the microscope equipment, such as the relative position of the microscope lens and the stage, the target correction position, etc. The deformation correction scheme is formulated not only to take into account the correction of slice position, but also to be carried out within the dynamic adjustment range of the equipment. Finally, combined with the previous correction information, a deformation correction and stabilization control scheme for pathological slices is obtained.

[0161] The automatic pathology section adjustment system is used to execute the above-mentioned automatic pathology section adjustment method, and the system includes:

[0162] The stage operation monitoring module records the movement trajectory and load status data of the microscope stage based on its real-time operation status. It analyzes the relationship between offset and load based on the trajectory and load data, identifies key offset nodes, analyzes the nodes, and obtains offset-load interaction data.

[0163] The trajectory optimization module selects key trajectory nodes based on the offset load interaction data, adjusts node parameters through the node deviation data, matches the real-time load changes, re-arranges the node sequence to optimize the trajectory, and generates an optimized trajectory node sequence;

[0164] The batch adjustment module monitors the deformation deviation of the slice batch based on the optimized trajectory node sequence, analyzes the deformation and deviation data, adjusts the order of the slices in the batch, and obtains the adjusted batch sequence by rearranging the slices in the batch;

[0165] The deformation correction module measures the position offset of the slices based on the adjusted batch sequence, compares the slice position offset data, adjusts the position of each slice according to the data, and obtains the slice position accuracy data;

[0166] The dynamic control module updates the microscope equipment control parameters based on the slice position accuracy data, synchronizes the dynamic data of the slice stage, updates and adjusts the motion trajectory according to the current slice position, and establishes a pathological slice deformation correction and stabilization control solution.

[0167] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for automatically adjusting pathological sections, characterized in that: The following steps are involved: Based on the real-time operation status of the microscope stage, the load and movement trajectory of the pathology slide stage are analyzed, the motion parameters of the microscope stage are adjusted, the deviation of the slide stage is addressed, the position parameters of the slide are adjusted in combination with the slice thickness measurement, and a stage motion adjustment plan is generated; Based on the stage motion adjustment scheme, the deviation value of the motion trajectory during the slicing operation is recorded, the stage motion path is optimized in combination with the microscope stage motion record, the optimized motion trajectory data is obtained, the motion trajectory is monitored in real time, the motion trajectory path nodes are adjusted to match the real-time requirements, and a trajectory optimization execution plan is generated; Based on the trajectory optimization execution plan, slice batch allocation is adjusted, numerical deviations before and after slice deformation correction are recorded, the optimization degree of the motion path in slice deformation correction is analyzed, the batch processing sequence is optimized, slice batch adjustment data is obtained, the slice allocation sequence within the batch is adjusted, differentiated batch slice data characteristics are analyzed and arrangement logic is optimized, and a correction adjustment execution plan is generated; Based on the correction and adjustment execution plan, the slice position offset is corrected, the position offset is dynamically adjusted in combination with the motion trajectory of the pathology slice stage, the real-time adjustment parameters of the microscope equipment are recorded, and the pathology slice deformation correction and stabilization control plan is generated in combination with the slice stage motion control data.

2. The method for automatically adjusting pathological sections according to claim 1, wherein: The steps for obtaining the stage motion adjustment plan are specifically as follows: Based on the real-time operating status of the microscope stage, the real-time load and movement trajectory of the pathology section stage are analyzed. Data is collected from the load sensor and position encoder to analyze the motion deviation of the stage under different operating conditions and obtain motion deviation data. The motion deviation data and slice thickness data are combined with the speed and acceleration information of the stage, and a multiple linear regression model is used to adopt the formula: y=b0+b1x1+b2x2+b3x3 Calculate the optimal position of the carrier and obtain the carrier position adjustment data; Where y represents the optimized position of the stage, x1 represents the motion deviation, x2 represents the slice thickness, x3 represents the stage speed, and b0, b1, b2, and b3 are model coefficients; According to the stage position adjustment data, the motion control parameters of the microscope stage are adjusted, the motion trajectory of the stage is optimized, and a stage motion adjustment plan is obtained.

3. The method for automatically adjusting pathological sections according to claim 2, characterized in that: The steps for obtaining the optimized motion trajectory data are specifically as follows: Based on the stage motion adjustment scheme, the displacement data of the stage during the slicing operation is recorded, the deviation value of each position point is collected, and the degree of deviation at each moment is identified by comparing with the preset trajectory. The data is calibrated in combination with the timestamp to generate motion trajectory deviation data; Using the motion trajectory deviation data, comparing the deviation values ​​of the differentiated positions, combining the motion speed and acceleration of each point, adjusting the corresponding control parameters, and obtaining optimized control parameter data by correcting the acceleration and speed balance motion trajectory; According to the optimized control parameter data, the path of the platform is replanned in combination with the current speed, acceleration and corrected position, and the path analysis is performed according to the adjusted path points, speed and acceleration to obtain the optimized motion trajectory data.

4. The method for automatically adjusting pathological sections according to claim 3, characterized in that: The steps for obtaining the trajectory optimization execution plan are specifically as follows: Extracting key path nodes in the trajectory based on the optimized motion trajectory data, including trajectory curvature change points, speed change points, and direction change points, classifying and arranging the node data, and generating a path node set; According to the path node set, combined with the real-time monitoring of the motion trajectory requirement parameters, the matching degree between the trajectory path and the real-time demand is analyzed using the formula: Calculate the path matching degree, adjust the positions of mismatched nodes and optimize the overall path to generate an optimized path node set; Among them, C represents the path matching degree, E i is the ideal value of the path node, y i is the current node value, w i is the weight coefficient, n is the number of path nodes; According to the optimized path node set and in combination with the trajectory changes monitored in real time, the trajectory path is dynamically adjusted, the updated path is set as the execution plan, the effect of the trajectory adjustment is verified through a real-time feedback mechanism, and a trajectory optimization execution plan is generated.

5. The method for automatically adjusting pathological sections according to claim 4, characterized in that: The steps for obtaining the slice batch adjustment data are specifically as follows: According to the trajectory optimization execution plan, the numerical deviation of each slice before and after deformation is analyzed, the deformation trend of each batch of slices is analyzed, the deformation of the slices is corrected based on the data before and after deformation, the deformation correction effect of each batch of slices is evaluated, and the slice deformation optimization data is obtained; Based on the slice deformation optimization data, the processing efficiency of each slice under the differentiated processing sequence is analyzed, the optimization of the batch processing sequence is evaluated in combination with the deformation correction effect, and the adjustment optimization degree of the batch is identified in combination with the weight value of each batch processing, thereby obtaining the batch processing sequence optimization data; According to the batch processing order optimization data, adjust the slice batch order, combine the target optimization data, and perform batch adjustment using the formula: Get slice batch adjustment data; Among them, T adjust Indicates the adjustment value of the slice batch, d k represents the deformation deviation of slice k, S k represents the deformation degree of slice k, w k represents the processing order weight of slice k, D k represents the correction accuracy of slice k, F k represents the processing feedback value of slice k, α, β and γ are adjustment coefficients, and m represents the total number of slices.

6. The method for automatically adjusting pathological sections according to claim 5, characterized in that: The steps for obtaining the correction and adjustment execution plan are specifically as follows: Based on the slice batch adjustment data, the slice data is rearranged, the slices are allocated to differentiated batches in a preset order, and the data is refined into multiple sub-batches according to the slice attribute characteristics. The slice attribute homogeneity within each batch is used for secondary allocation to generate a batch slice adjustment result; Based on the batch slice adjustment result, the numerical characteristics of the slices in each sub-batch are compared, the degree of difference of each slice in the same batch is identified, the order of the slices is adjusted, the slice order is arranged according to the degree of difference, and the slice allocation ratio in each sub-batch is adjusted to generate a differentiated slice order optimization result; Based on the differentiated slicing sequence optimization results and combined with the slicing characteristics between each sub-batch, the overall batch sorting optimization is performed, adjustments are made according to the characteristics of the batch, and a correction adjustment execution plan is generated.

7. The method for automatically adjusting pathological sections according to claim 6, characterized in that: The steps for obtaining the pathological section deformation correction and stabilization control scheme are specifically as follows: Based on the correction and adjustment execution plan, the associated data of the motion trajectory of the slice stage is extracted, the motion trajectory is smoothed, and a preliminary offset calculation of the slice position is performed in combination with the real-time position parameters of the microscope device, and a slice displacement correction value is generated according to the difference between the real-time position and the target position; The slice displacement correction value is called, and the correction adjustment coefficient is set in combination with the real-time parameters of the microscope device and the dynamic adjustment coefficient. The formula is: ΔP=G·|ΔT-H|+M·(ΔT-λ) 1 / 2 Get the slice position correction offset; Where ΔP represents the slice position correction offset, ΔT represents the real-time offset of the slice, G is the position correction weight coefficient, H is the slice dynamic offset threshold, M is the correction weight coefficient, and λ is the dynamic adjustment coefficient; According to the slice position correction offset, combined with the motion trajectory data of the slice stage and the real-time adjustment parameters of the microscope equipment, deformation correction analysis is performed to generate a pathological slice deformation correction and stabilization control solution.

8. A pathology section automatic adjustment system, characterized in that: The method for automatically adjusting pathological sections according to any one of claims 1 to 7, wherein the system comprises: The stage operation monitoring module records the movement trajectory and load status data of the microscope stage based on its real-time operation status. It analyzes the relationship between offset and load based on the trajectory and load data, identifies key offset nodes, analyzes the nodes, and obtains offset-load interaction data. The trajectory optimization module selects key trajectory nodes based on the offset load interaction data, adjusts node parameters through node deviation data, matches real-time load changes, re-arranges the node sequence to optimize the trajectory, and generates an optimized trajectory node sequence; The batch adjustment module monitors the deformation deviation before and after the slice batch based on the optimized trajectory node sequence, analyzes the deformation and deviation data, adjusts the order of the slices in the batch, and obtains the adjusted batch sequence by rearranging the slices in the batch; The deformation correction module measures the position offset of the slices based on the adjusted batch sequence, compares the slice position offset data, adjusts the position of each slice according to the data, and obtains slice position accuracy data; The dynamic control module updates the microscope equipment control parameters based on the slice position accuracy data, synchronizes the dynamic data of the slice stage, adjusts the motion trajectory according to the current slice position, and establishes a pathological slice deformation correction and stabilization control solution.

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