Intelligent sampling device for microbial inspection and control method thereof

Abstract

The invention provides an intelligent sampling device for microbiological examination and a control method thereof, and the method comprises the steps: determining cooperative association features among all dimension information in full-dimension information of a microbiological examination region in a sampling process, and generating a global sampling path of the sampling device in the microbiological examination region according to the cooperative association features; judging the moving trend of a dynamic interference source in the microbiological inspection area, and determining a local bypass path of the sampling device among the sampling points according to the moving trend and the sampling precision rule of each sampling point; determining a motion constraint condition of the sampling device in the microbiological examination area, and determining a smooth path when the sampling device performs stable sampling on each sampling point according to the motion constraint condition and all the local bypass paths; and controlling the sampling device to sample each sampling point based on the smooth path. By adopting the scheme of the invention, the sampling path of the sampling device can be locally avoided based on the moving trend of the interference source in the microbiological inspection area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent sampling, and more particularly to a microbial testing intelligent sampling device and a control method thereof. BACKGROUND

[0002] Intelligent sampling is a process of autonomously, accurately and efficiently collecting microorganisms or other analysis targets by using advanced robot technology, multi-sensor fusion and intelligent algorithms. It perceives complex environments, plans global paths and avoids obstacles in real time, and finally automatically completes a series of sample collection at preset points while ensuring smooth and reliable sampling operations.

[0003] In the field of microbial testing, the sampling environment is often highly complex: the testing area often contains narrow sample channels, densely distributed multiple static sample points, and may have dynamic interference sources. Traditional microbial sampling relies on manual operation or simple automatic control, which has significant limitations: on the one hand, the perception dimension of environmental information is single, and it is difficult to integrate static spatial structure and dynamic interference, resulting in a lack of global adaptability of path planning; on the other hand, in narrow channels, path congestion is easy to occur due to movement constraints, and it is difficult to adjust the avoidance strategy in real time when facing dynamic interference sources, and the influence of sample point density on sampling efficiency is not considered, ultimately causing low sampling accuracy, excessive time consumption or high risk of equipment collision, etc., which is difficult to meet the high-precision and high-efficiency microbial testing requirements. Therefore, how to locally avoid the sampling path of the sampling device based on the movement trend of the interference source in the microbial testing area has become a problem faced by the industry. SUMMARY

[0004] The present application provides a microbial testing intelligent sampling device and a control method thereof, which can locally avoid the sampling path of the sampling device based on the movement trend of the interference source in the microbial testing area.

[0005] In a first aspect, the present application provides a control method of a microbial testing intelligent sampling device, wherein the sampling device samples a microbial testing area, and the microbial testing area includes multiple sampling points. The method includes the following steps: Obtaining static environmental information, dynamic environmental information and sampling task information of the microbial testing area during sampling to generate full-dimensional information of the microbial testing area; Determining the cooperative correlation characteristics between each dimension information in the full-dimensional information, and performing adaptive sampling point path planning of spatial feature matching of the sampling path of the sampling device in the microbial testing area based on the spatial distribution density of the sampling points in the microbial testing area and the cooperative correlation characteristics, to generate a global sampling path of the sampling device in the microbial testing area; determine a moving trend of the dynamic interference source in the microorganism inspection area, and determine a local avoidance path of the sampling device between each sampling point according to the moving trend and a sampling accuracy rule of each sampling point; determine a motion constraint condition of the sampling device in the microorganism inspection area, and adjust the global sampling path by smoothing the motion constraint condition and all the local avoidance paths to obtain a smooth path of the sampling device for smooth sampling of each sampling point; control the sampling device to perform sampling operation on each sampling point based on the smooth path.

[0006] In some embodiments, determining the cooperative correlation feature between each dimension information in the full-dimension information specifically includes: preprocessing the full-dimension information to obtain preprocessed full-dimension information; determining the cooperative correlation feature between each dimension information in the full-dimension information according to the preprocessed full-dimension information.

[0007] In some embodiments, the adaptive sampling point path planning of the sampling device in the microorganism inspection area based on the spatial distribution density of the sampling points in the microorganism inspection area and the cooperative correlation feature for spatial feature matching of the sampling path of the sampling device in the microorganism inspection area specifically includes: obtaining the spatial distribution density of the sampling points in the microorganism inspection area; dividing the microorganism inspection area into a high-density area and a low-density area based on the spatial distribution density; performing adaptive planning on the sampling point path of the high-density area and the low-density area to obtain adaptive sampling point paths of the high-density area and the low-density area; determining a spatial feature matching index between the microorganism inspection area and the sampling device; verifying the adaptive sampling point paths of the high-density area and the low-density area according to the spatial feature matching index to obtain verified sampling point paths of the high-density area and the low-density area; smoothly connecting the verified sampling point paths of the high-density area and the low-density area according to the cooperative correlation feature to obtain the global sampling path of the sampling device in the microorganism inspection area.

[0008] In some embodiments, determining the moving trend of the dynamic interference source in the microorganism inspection area specifically includes: obtaining motion data of the dynamic interference source in the microorganism inspection area and a type of the dynamic interference source; performing denoising processing on the motion data to obtain denoised motion data; The movement trend of the dynamic interference source in the microbial testing area is determined based on the denoised motion data and the type of the dynamic interference source.

[0009] In some embodiments, determining the local bypass path of the sampling device between sampling points based on the movement trend and the sampling accuracy rules of each sampling point specifically includes: Obtain the sampling accuracy rules for each sampling point; Determine conflict sampling paths between each sampling point based on the described movement trend; The local bypass path of the sampling device between each sampling point is determined based on all conflict sampling paths and all sampling accuracy rules.

[0010] In some embodiments, determining the motion constraints of the sampling device in the microbial testing area specifically includes: Obtain the spatial dimensions of the microbial testing area and the movement dimensions of the sampling device; The motion constraints of the sampling device in the microbial testing area are determined based on the spatial dimensions and the motion dimensions.

[0011] In some embodiments, the smooth path obtained by smoothly adjusting the global sampling path using the motion constraints and all local avoidance paths to achieve stable sampling of each sampling point by the sampling device specifically includes: Extract the key parameters of each local bypass path; The global sampling path is adjusted for conflicts based on all key parameters to obtain the sampling adjustment path. The sampling adjustment path is smoothed according to the motion constraints to obtain a smooth path for the sampling device to perform smooth sampling at each sampling point.

[0012] Secondly, this application provides an intelligent sampling device for microbial testing, wherein the sampling device samples a microbial testing area, the microbial testing area including multiple sampling points, and the device includes: The acquisition module is used to acquire static environmental information, dynamic environmental information, and sampling task information of the microbial testing area during the sampling process, so as to generate full-dimensional information of the microbial testing area. The processing module is used to determine the collaborative correlation features between the various dimensions of information in the full-dimensional information, and to perform adaptive sampling point path planning for the sampling path of the sampling device in the microbial testing area based on the spatial distribution density of sampling points in the microbial testing area and the collaborative correlation features, thereby generating the global sampling path of the sampling device in the microbial testing area. The processing module is also used to determine the movement trend of dynamic interference sources in the microbial testing area, and to determine the local bypass path of the sampling device between each sampling point based on the movement trend and the sampling accuracy rules of each sampling point. The processing module is also used to determine the motion constraints of the sampling device in the microbial testing area, and to smoothly adjust the global sampling path through the motion constraints and all local bypass paths to obtain a smooth path for the sampling device to perform stable sampling at each sampling point. The execution module is used to control the sampling device to perform sampling operations at each sampling point based on the smooth path.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the control method of the above-described intelligent sampling device for microbial testing.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the control method of the above-described intelligent sampling device for microbial testing.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The intelligent sampling device and control method for microbial testing provided in this application first acquires static environmental information, dynamic environmental information, and sampling task information of the microbial testing area during the sampling process to generate full-dimensional information of the microbial testing area; determines the collaborative correlation features between the various dimensions of the full-dimensional information; and performs adaptive sampling point path planning based on spatial feature matching of the sampling path of the sampling device in the microbial testing area based on the spatial distribution density of sampling points in the microbial testing area and the collaborative correlation features to generate a global sampling path of the sampling device in the microbial testing area; determines the movement trend of dynamic interference sources in the microbial testing area; determines local bypass paths of the sampling device between sampling points based on the movement trend and the sampling accuracy rules of each sampling point; determines the motion constraints of the sampling device in the microbial testing area; and smoothly adjusts the global sampling path through the motion constraints and all local bypass paths to obtain a smooth path for the sampling device to perform stable sampling at each sampling point; and controls the sampling device to perform sampling operations at each sampling point based on the smooth path.

[0016] Therefore, this application, in the control process of the intelligent sampling device for microbial testing, constructs multi-dimensional information of the microbial testing area through comprehensive collection of static environment, dynamic environment, and sampling task information. Adaptive path planning based on the spatial distribution density of sampling points and the synergistic correlation characteristics of multi-dimensional information generates a global sampling path that ensures the integrity of sampling coverage while reserving optimization space for subsequent local avoidance, thus improving the overall efficiency of path planning. By real-time judgment of the movement trend of dynamic interference sources, and customizing local avoidance paths based on the accuracy rules of each sampling point, precise avoidance of the interference source's affected area is achieved, avoiding the sampling data distortion caused by interference source obstruction or interference in traditional fixed paths. Finally, smooth adjustment of the global path through motion constraints ensures the operational stability of the sampling device when avoiding interference sources, guaranteeing the effectiveness of local avoidance and improving the continuity and reliability of the overall sampling process. Using this application's scheme, the sampling path of the sampling device can be locally avoided based on the movement trend of interference sources in the microbial testing area. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of a control method for an intelligent sampling device for microbial testing according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the determination of the movement trend of a dynamic interference source according to some embodiments of this application; Figure 3 This is an exemplary flowchart illustrating the determination of a smooth path according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of an intelligent sampling device for microbial testing according to some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a computer device for implementing a control method for an intelligent sampling device for microbial testing, according to some embodiments of this application. Detailed Implementation

[0018] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a control method for an intelligent sampling device for microbial testing according to some embodiments of this application. The control method for the intelligent sampling device for microbial testing mainly includes the following steps: In step 101, static environmental information, dynamic environmental information, and sampling task information of the microbial testing area during the sampling process are obtained to generate full-dimensional information of the microbial testing area.

[0020] It should be noted that the static environmental information in this application includes temperature, humidity, and air pressure data of the microbial testing area, as well as the outline of the microbial testing area and the three-dimensional coordinates of multiple static sample points. This information is recorded every 30 seconds to form static environmental information, reflecting the stable and unchanging basic environmental conditions and spatial geometric layout of the microbial testing area. The static environmental information represents the static constraints and basic environmental background that the sampling device deployment and path planning must follow. The dynamic environmental information includes the location, speed, and direction of movement of dynamic interference sources in the microbial testing area. Dynamic interference sources include airflow disturbances, mechanical vibrations, and personnel movement. Dynamic sampling is performed 10 times per second, reflecting the real-time changing interference state and movement patterns within the testing area. The dynamic environmental information represents dynamic risk factors that may affect sampling accuracy and need to be avoided in real time. The sampling task information includes sampling point codes, sample volume requirements for each sampling point, sampling depth, priority ranking, and corresponding testing item requirements. This reflects the core needs, objectives, and execution standards of microbial testing work. The sampling task information represents the specific operational content, execution sequence, and quality requirements that the sampling device must complete.

[0021] In specific implementation, static environmental information is collected by temperature sensors, humidity sensors, air pressure sensors, and channel contour laser scanners, recorded every 30 seconds to form a static data matrix; dynamic environmental information is captured in real time by high-definition cameras, infrared motion detectors, and vibration sensors, and the position, speed, and direction of motion of dynamic interference sources are dynamically sampled 10 times per second; sampling task information is received by the system's host computer, including sampling point codes, sample volume requirements for each sampling point, sampling depth, and priority ranking; finally, the three types of information are denoised and correlated to generate full-dimensional information data of the microbial testing area, including environmental parameters, interference status, and task requirements. Other methods can also be used in other embodiments, which will not be elaborated here.

[0022] In step 102, the collaborative correlation features between the various dimensions of information in the full-dimensional information are determined. Based on the spatial distribution density of sampling points in the microbial testing area and the collaborative correlation features, the sampling path of the sampling device in the microbial testing area is planned by spatial feature matching to generate the global sampling path of the sampling device in the microbial testing area.

[0023] In some embodiments, determining the collaborative correlation features between the various dimensions of information in the full-dimensional information can be achieved by the following steps: The full-dimensional information is preprocessed to obtain preprocessed full-dimensional information; Based on the preprocessed full-dimensional information, determine the collaborative correlation features between the various dimensions of the full-dimensional information.

[0024] In specific implementation, the full-dimensional information is preprocessed to obtain the preprocessed full-dimensional information, which can be achieved in the following way: First, data cleaning is performed. For sensor data such as temperature and humidity in static environmental information, outliers are removed using the Grubbs criterion. The mean and standard deviation of the data in this dimension are calculated, and the Grubbs statistic for each data point is calculated using the formula. Data with a statistic greater than the critical value at the corresponding significance level are identified as outliers and deleted. For real-time data such as the location and speed of interference sources in dynamic environmental information, the sliding window averaging method is used to smooth noise. The mean of adjacent sampling points is used to replace the data at the middle point in the window to reduce fluctuations. For discrete data in the sampling task information, numerical encoding is performed, and high, medium, and low priorities are respectively assigned to 3 1. Different inspection items are coded with unique numerical values ​​according to industry standards. Next, data standardization is implemented, using the minimum-maximum standardization method to process data of different dimensions. All dimensions of data, such as temperature of static environmental information, speed of interference sources of dynamic environmental information, and sampling amount of sampling task information, are mapped to the [0,1] interval. The calculation formula is: standardized data = (original data - minimum value of this dimension) / (maximum value of this dimension - minimum value of this dimension). Finally, data alignment is performed. Based on the sampling timestamp of dynamic environmental information, the missing time point data of static environmental information are supplemented to ensure that the three types of information correspond one-to-one in the time dimension, and the preprocessed full-dimensional information is obtained. Other methods can be used in other embodiments, which are not limited here.

[0025] In specific implementation, the collaborative association features between various dimensions of information in the preprocessed full-dimensional information can be determined in the following way: First, based on industry experience, a minimum support and a minimum confidence level are set. The preprocessed full-dimensional information is scanned, and the frequency of occurrence of data items in each dimension is counted. Items with a frequency ≥ the minimum support are selected as frequent 1-itemsets. Then, frequent 1-itemsets are combined in pairs to generate candidate 2-itemsets. The data is scanned again to calculate the support of the candidate 2-itemsets. Items with a support ≥ the minimum support are retained as frequent 2-itemsets. This process is repeated to generate higher-order frequent itemsets. Finally, association rules are generated based on the frequent itemsets. The confidence level of the rules is calculated. For example, the confidence level of a sample size ≥ 1 mL at a temperature ≥ 37℃ is calculated as the data volume that satisfies both conditions / the data volume that satisfies a temperature ≥ 37℃. Rules with a confidence level ≥ the minimum confidence level are selected as collaborative association features. The rationality of the rules is verified by combining knowledge from the field of microbiological testing. Finally, the collaborative association features between various dimensions of information are determined. Other methods can also be used in other embodiments, which are not limited here.

[0026] It should be noted that the collaborative correlation feature in this application represents the inherent logic of mutual influence and adaptation between basic environmental conditions, real-time interference status and sampling operation requirements in the microbial testing scenario. It reflects the constraints of environmental factors on the execution standards of sampling tasks. For example, in a high-humidity environment, the sealing of the sampling needle needs to be improved simultaneously to avoid sample contamination by moisture. It also reflects the guidance of sampling requirements on environmental interference response strategies.

[0027] In some embodiments, the following steps can be used to generate a global sampling path for the sampling device in the microbial testing area by performing adaptive sampling point path planning based on spatial feature matching of the sampling points in the microbial testing area and the cooperative association features: Obtain the spatial distribution density of sampling points in the microbial testing area; Based on the spatial distribution density, the microbial testing area is divided into a high-density area and a low-density area. Adaptive planning is performed on the sampling point paths of the high-density region and the low-density region to obtain the adaptive sampling point paths of the high-density region and the low-density region. Determine the spatial characteristic matching index between the microbial testing area and the sampling device; The adaptive sampling point paths of the high-density region and the low-density region are verified based on the spatial feature matching index to obtain the verification sampling point paths of the high-density region and the low-density region. Based on the collaborative association features, the paths of the verification sampling points in the high-density area and the low-density area are smoothly connected to obtain the global sampling path of the sampling device in the microbial testing area.

[0028] It should be noted that the spatial distribution density of sampling points in this application includes the actual number of sampling points per unit space and the density difference between different sub-regions, the clustering pattern of high-density regions and the spacing distance of low-density regions. Among them, different sub-regions are straight sections and curved sections of narrow passages, areas affected by interference sources and areas not affected by interference sources, and the clustering pattern is point clustering and linear clustering. The spatial distribution density reflects the spatial density distribution of sampling points within the microbial testing area.

[0029] In specific implementation, the microbial testing area can be divided into high-density and low-density areas based on the spatial distribution density in the following way: areas with a density ≥ 3 microorganisms / cubic centimeter are designated as high-density areas, which usually correspond to microbial enrichment or key monitoring areas, while areas with a density < 3 microorganisms / cubic centimeter are designated as low-density areas. At the same time, the boundary coordinates of the two types of areas and the codes of the sampling points they contain are marked. Referring to the enrichment distribution characteristics of common target microorganisms in typical testing scenarios such as food and medical, their colony distribution density in contaminated or key monitoring areas usually corresponds to a sampling point coverage requirement of 1-5 microorganisms / cubic centimeter. 3 microorganisms / cubic centimeter can ensure that the sampling coverage of potential enrichment areas is not missed, while avoiding redundant operations caused by excessive density.

[0030] In addition, in specific implementation, adaptive planning of the sampling point paths in the high-density area and the low-density area can be achieved in the following way: For the high-density area, a greedy algorithm is used. Taking any sampling point in the area as the starting point, the straight-line distance between that point and all other sampling points is calculated using the Euclidean distance formula. The sampling point with the closest distance is selected as the next access node. This process is repeated until all sampling points are traversed to form a preliminary path to reduce the number of backtracking, thus obtaining the adaptive sampling point path for the high-density area. For the low-density area, the A* algorithm is used. Taking the initial position of the device at the area entrance as the starting point and the area exit as the ending point, the cost function is calculated using the actual travel distance from the starting point to the current node plus the straight-line distance from the current node to the ending point. The path trajectory with the minimum cost is calculated to ensure the shortest path and avoid unnecessary detours, thus obtaining the adaptive sampling point path for the low-density area.

[0031] In addition, in specific implementation, the spatial characteristic matching index between the microbial testing area and the sampling device can be determined in the following way: based on the geometric constraints of the narrow channel, the motion performance of the sampling device and the testing accuracy requirements, an index system is determined. The spatial characteristic matching index includes the parallelism deviation between the path and the channel axis ≤ 5°, the maximum turning angle of a single path segment ≤ 30° (matching the mechanical turning limit of the sampling device), the minimum safe gap between the path and the inner wall of the channel ≥ 2cm (to prevent the sampling device from scratching), and the path curvature change rate within the interval between adjacent sampling points ≤ 0.5rad / cm (to ensure smooth movement).

[0032] In addition, in specific implementation, the adaptive sampling point paths of the high-density and low-density regions are verified according to the spatial feature matching index. The verification sampling point paths of the high-density and low-density regions can be obtained in the following way: extract the coordinate sequence of the adaptive sampling point paths of the high-density and low-density regions segment by segment, verify whether each segment of the path meets the requirements of the spatial feature matching index, parallelism is achieved by calculating the angle between the path segment and the channel axis, turning angle is obtained by calculating the angle between adjacent path segments, and safety gap is determined by the distance difference between the path node and the inner wall coordinate of the channel; path segments that do not meet the spatial feature matching index are corrected. For example, for path segments with excessive parallelism, the node coordinates are adjusted to make them move closer to the channel axis, and for path segments with excessive turning angle, 2-3 arc transition nodes are inserted. For road segments with abrupt curvature changes, linear interpolation is used to correct the node spacing to obtain the verification sampling point paths of the high-density and low-density regions.

[0033] In addition, in specific implementation, the sampling path of the high-density area and the low-density area is smoothly connected according to the cooperative association feature to obtain the global sampling path of the sampling device in the microbial testing area. This can be achieved in the following way: the path connection order is determined based on the cooperative association feature. The path of the area with higher priority in the cooperative association feature is connected first. For example, the path of the high-density area with a temperature ≥37℃ takes precedence over the path of the low-density normal temperature area. The start and end nodes of the adjacent area paths are interpolated to generate a smooth transition path segment. At the same time, the total length of the global path after connection and the movement speed parameters of each node are calculated. For example, the path speed of the high-density area is ≤5cm / s and the path speed of the low-density area is ≤10cm / s to ensure that there is no sudden change in speed at the connection point. Finally, the global sampling path of the sampling device in the microbial testing area containing the complete coordinate sequence, movement parameters and area connection marks is output.

[0034] It should be noted that, in this application, high-density areas and low-density areas refer to microbial testing sub-regions divided according to the spatial distribution density of sampling points, reflecting the differences in the potential enrichment level of microorganisms and testing needs within the testing area. High density corresponds to enrichment or key monitoring areas, while low density corresponds to routine monitoring areas. Adaptive sampling point paths refer to local paths planned using different algorithms for the characteristics of the two types of areas, reflecting the adaptability of path planning to regional density differences. Spatial feature matching indexes refer to path verification standards formulated in combination with the geometric constraints of narrow channels, the motion performance of sampling devices, and testing accuracy, reflecting the environmental adaptability, mechanical feasibility, and accuracy assurance requirements that the path must meet. Verified sampling point paths refer to regional paths that have been verified and corrected segment by segment by spatial feature matching indexes, reflecting the compliance of the path with constraints and the stability of movement. Global sampling paths refer to sampling paths that are planned in a coordinated manner for the sampling tasks of the entire testing area, taking into account the sampling efficiency, environmental interference avoidance, and testing accuracy requirements of different areas.

[0035] In step 103, the movement trend of dynamic interference sources in the microbial testing area is determined, and the local bypass path of the sampling device between each sampling point is determined according to the movement trend and the sampling accuracy rules of each sampling point.

[0036] In some embodiments, reference Figure 2 As shown, this figure is an exemplary flowchart for determining the movement trend of dynamic interference sources in some embodiments of this application. In this embodiment, determining the movement trend of dynamic interference sources in the microbial testing area can be achieved by the following steps: First, in step 1031, the motion data of the dynamic interference sources and the types of the dynamic interference sources in the microbial testing area are obtained; Secondly, in step 1032, the motion data is denoised to obtain denoised motion data; Finally, in step 1033, the movement trend of the dynamic interference source in the microbial testing area is determined based on the denoised motion data and the type of the dynamic interference source.

[0037] It should be noted that the motion data of the dynamic interference sources in this application includes the real-time two-dimensional pixel coordinates, three-dimensional spatial coordinates, vibration amplitude, vibration frequency, and vibration period of the dynamic interference sources in the microbial testing area. It also includes quantitative parameters such as historical motion trajectory, real-time moving speed and direction, and displacement change per unit time generated based on continuous sampling data. These parameters reflect the current motion state, dynamic change pattern, and spatial position fluctuation of the interference sources. The types of dynamic interference sources mainly include personnel movement, airflow disturbance, mechanical component movement, and pipeline vibration, which reflect the essential attributes and influence characteristics of the dynamic interference sources.

[0038] In addition, in specific implementation, the motion data is denoised to obtain the denoised motion data in the following ways: an adaptation method is used for different types of data. For coordinate data, a median filtering method with a 3×3 template is used. Each coordinate point is traversed, and the median of the 9 pixels in the template is used to replace the original coordinate value to remove coordinate jumps caused by image noise. For three-dimensional coordinate data, a sliding window averaging method is used. The window size is set to 5 consecutive sampling points, and the average value of the coordinates in the window is calculated as the denoised data to smooth short-term fluctuations. For vibration data, a low-pass filtering method is used. The cutoff frequency is set to 10Hz to filter high-frequency electromagnetic interference to obtain the denoised motion data. Other methods can also be used in other embodiments, which are not limited here.

[0039] Furthermore, in specific implementation, determining the movement trend of dynamic interference sources in the microbial testing area based on the denoised motion data and the type of dynamic interference source can be achieved in the following way: Select the corresponding analysis model according to the type of interference source. For types with clear motion trajectories, such as personnel movement and mechanical parts, use a linear fitting model based on the least squares method, with the three-dimensional coordinates in the denoised motion data as the dependent variable and time as the independent variable to construct the motion trajectory equation, calculate velocity and acceleration, and predict coordinate changes within the next 5 seconds. For irregular types such as airflow disturbances, use an autoregressive integral moving average model from time series analysis, with... The denoised vibration amplitude and frequency data are used as inputs. The model order is set to (2,1,1). The model is trained using historical 30-second data to predict the vibration intensity and impact range for the next 5 seconds. For periodic types such as pipeline vibration, the spectrum analysis method is used. The denoised vibration data is converted into the frequency domain through fast Fourier transform to identify the main vibration frequency and period. The time node of the vibration peak is inferred by combining the periodic law. Finally, the output results of various models are integrated to form a dynamic trend of the interference source, including the direction of movement of the interference source, velocity change, impact area and confidence level. Other methods can be used to determine this in other embodiments, which are not limited here.

[0040] It should be noted that the movement trend in this application represents the movement trend of dynamic interference sources in the microbial testing area, and is used to predict the movement of dynamic interference sources in the microbial testing area.

[0041] In some embodiments, determining the local bypass path of the sampling device between sampling points based on the movement trend and the sampling accuracy rules of each sampling point can be achieved by the following steps: Obtain the sampling accuracy rules for each sampling point; Determine conflict sampling paths between each sampling point based on the described movement trend; The local bypass path of the sampling device between each sampling point is determined based on all conflict sampling paths and all sampling accuracy rules.

[0042] In practice, the sampling accuracy rules corresponding to the sampling point codes are retrieved from the preset accuracy rule database. The sampling accuracy rules include the position error threshold, the sampling quantity error threshold, the accuracy influence range, and the priority weight.

[0043] In addition, in specific implementation, the conflict sampling path between each sampling point can be determined according to the movement trend in the following way: extract the initial straight-line path coordinate sequence between any two adjacent sampling points based on the movement trend of the dynamic interference source, calculate the intersection of the path and the predicted influence area of ​​the interference source using the spatial coordinate comparison method, and determine whether each point on the path falls within the boundary of the influence area. If the intersection length is greater than or equal to 10% of the total path length or if there is a point on the path whose distance to the predicted trajectory of the interference source is less than the safety threshold, then the path is marked as a conflict sampling path.

[0044] Furthermore, in specific implementation, determining the local bypass path of the sampling device between each sampling point based on all conflict sampling paths and all sampling accuracy rules can be achieved in the following way: For each conflict sampling path, an artificial potential field method is used to plan the bypass trajectory. The target sampling point is set as an attractive potential field, where the potential energy function is inversely proportional to the distance; the closer the distance, the greater the attraction. The predicted influence area of ​​the interference source is set as a repulsive potential field, where the potential energy function is inversely proportional to the square of the distance; the closer the distance, the greater the repulsive force. The potential field strength coefficient is set in conjunction with the position error threshold in the sampling accuracy rules. This is achieved through iteration. The point where the potential field force is zero is calculated to generate the initial coordinate sequence of the bypass path. This ensures that the distance between the path and the area affected by the interference source is ≥ the safety threshold + 0.5 cm, with a buffer reserved, and that the deviation between the path endpoint and the target sampling point is ≤ the position error threshold. Subsequently, the initial path is smoothed to make the path curvature change rate ≤ 0.3 rad / cm, adapting to the device's steering performance, and the position error of the sampling device is verified during the path execution. If the proportion of path segments with errors exceeding the standard is < 5%, it is determined as the final local bypass path, thus obtaining the local bypass path of the sampling device between each sampling point.

[0045] It should be noted that the sampling accuracy rules in this application represent quantitative quality standards preset for different types of sampling points and testing needs to ensure the reliability of microbial test results. The sampling accuracy rules reflect the core quality requirements and priority differences in different sampling scenarios, and embody the rigid constraints of the testing objectives on the sampling operation. The conflict sampling path refers to the path in which the sampling device intersects with the predicted influence area of ​​the dynamic interference source in the initial straight path between adjacent sampling points, or the distance is less than the safety threshold, which may lead to the sampling accuracy exceeding the standard. The conflict sampling path reflects the direct contradiction between the initial path and the movement trend of the dynamic interference source, revealing the potential risk of sampling accuracy loss when interference is not avoided and the initial defects in path planning. The local bypass path refers to the bypass trajectory planned and smoothed for the conflict sampling path. It must meet the requirements of maintaining a safe buffer distance from the interference source, the deviation between the endpoint and the target sampling point being ≤ the accuracy threshold, and the turning performance of the curvature adaptation device. The local bypass path reflects the active avoidance strategy against dynamic interference and the mandatory guarantee capability of sampling accuracy.

[0046] In step 104, the motion constraints of the sampling device in the microbial testing area are determined, and the global sampling path is smoothly adjusted by the motion constraints and all local bypass paths to obtain a smooth path for the sampling device to perform stable sampling at each sampling point.

[0047] In some embodiments, determining the motion constraints of the sampling device in the microbial testing area can be achieved by the following steps: Obtain the spatial dimensions of the microbial testing area and the movement dimensions of the sampling device; The motion constraints of the sampling device in the microbial testing area are determined based on the spatial dimensions and the motion dimensions.

[0048] It should be noted that the spatial dimensions of the microbial testing area in this application include the width and height of the straight section, the minimum width and height of the curved section, the total length of the channel, the radius of curvature of the inner arc of each curve, and the three-dimensional coordinates and specific dimensions of length × width × height of the fixed obstacles in the channel. These dimensions reflect the geometric shape and physical spatial boundary of the narrow channel, and demonstrate the size of the movement space that the channel can provide for the sampling device. The movement dimensions of the sampling device include the maximum external dimensions of the body (length, width, and height), the maximum rotation angle of the sampling arm in the horizontal and vertical directions, the maximum extension length of the sampling arm, the maximum linear speed and minimum turning radius obtained under unconstrained conditions during no-load test runs, as well as the maximum output speed and rated torque of the drive motor. These dimensions reflect the mechanical structural limits and upper limits of the movement performance of the sampling device itself, and demonstrate the range of movement and action capabilities that the device can achieve in space.

[0049] In specific implementation, the motion constraints of the sampling device in the microbial testing area, based on the spatial dimensions and motion dimensions, can be achieved in the following ways: In terms of geometric constraints, calculate the safe clearance between the channel and the machine body to ensure that the distance between the machine body and the inner wall of the channel and obstacles is ≥2cm; at bends, the minimum turning radius of the constraint device is ≥R-1cm to adapt to the channel curvature, and the rotation angle of the sampling arm during turning is ≤ the maximum rotation angle -5° to avoid collision between the arm and the channel; in terms of mechanical performance constraints, combine the channel length and drive motor parameters to limit the linear motion speed to ≤min(maximum linear motion speed × 0.8, 5cm / s), where 0.8 is a safety factor and 5cm / s is an empirical value to avoid airflow disturbance; based on the matching relationship between motor torque and machine body weight, set the maximum climbing angle to ≤10°, suitable for inclined channels. Finally, integrate all constraint parameters to form motion constraint conditions that include constraint type, specific threshold, and applicable scenarios. In other embodiments, other methods can also be used to determine these constraints, which are not limited here.

[0050] It should be noted that the motion constraints in this application refer to the constraints on the movement of the sampling device during sampling in the microbial testing area, so as to facilitate the adjustment of the movement of the sampling device in the microbial testing area.

[0051] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flowchart for determining a smooth path in some embodiments of this application. In this embodiment, the global sampling path is smoothly adjusted by the motion constraints and all local avoidance paths to obtain a smooth path for the sampling device to perform stable sampling at each sampling point. This can be achieved by the following steps: First, in step 1041, the key parameters of each local bypass path are extracted; Secondly, in step 1042, the global sampling path is adjusted for conflict based on all the key parameters to obtain the sampling adjustment path; Finally, in step 1043, the sampling adjustment path is smoothed according to the motion constraints to obtain a smooth path for the sampling device to perform smooth sampling at each sampling point.

[0052] In practice, the key parameters of each local bypass path can be extracted in the following way: the key parameters include the three-dimensional coordinates of the starting and ending points of the path, the coordinates of the key turning points in the middle of the path and the corresponding turning angles, such as those calculated by the formula of the angle between the vectors of adjacent path segments, the minimum safe distance between the path and the dynamic interference source, and the design speed of each segment of the path. At the same time, the codes of the two sampling points associated with each path and the path length are marked. The extracted parameters are verified by comparing them with the original path design document to ensure that the key coordinate deviation is ≤0.1mm and the turning angle calculation error is ≤1°, thus forming standardized key parameters.

[0053] In addition, in specific implementation, the global sampling path is adjusted for conflicts based on all key parameters. The adjusted sampling path can be achieved in the following way: the coordinate sequence of the global sampling path is matched with the key parameters of all local bypass paths. The spatial coordinate comparison method is used to locate the conflict. If the road segment between two sampling points in the global path (let's call it road segment G) has a coordinate deviation > 0.5 mm from the corresponding local bypass path (let's call it path B), or if the design speed and turning angle of road segment G do not match the key parameters of path B, then it is determined to be a conflicting road segment. For the conflicting road segment, it is directly replaced with the corresponding local bypass path, and the coordinates of the replacement road segment and the connecting segments before and after the global path are adjusted. The coordinate deviation of the connecting point is calculated. If the deviation is > 0.2 mm, the coordinates of 3-5 nodes around the connecting point are finely adjusted so that the slope change rate of the path at the connecting point is ≤ 0.1 rad / mm to avoid abrupt inflection points, thus obtaining the adjusted sampling path.

[0054] In addition, in specific implementation, the smooth path obtained by the sampling device for smooth sampling of each sampling point can be achieved by smoothing the sampling adjustment path according to the motion constraints as described above. This can be done in the following way: First, the motion constraints are converted into segment-by-segment verification standards. For each segment of the sampling adjustment path, the maximum allowable speed is calculated. Combining the segment length and device acceleration parameters, smooth start-up and stopping are ensured. If the design speed of a segment exceeds the constraint value, the segment is divided into 2-3 sub-segments with uniform deceleration transition. The first segment is at the design speed, and subsequent sub-segments gradually reduce the speed to the constraint value. For turning angles > 3... For road sections with a 0° angle, insert 2-3 transition nodes, for example, using a third-order Bézier curve. Adjust the node coordinates to reduce the turning angle to within the constraint range and ensure the curve curvature change rate is ≤0.3 rad / cm. For road sections with insufficient safety clearance, fine-tune the path coordinates along the direction away from the channel wall to ensure the clearance is ≥2cm+0.2cm, leaving a buffer. After the adjustment is completed, import the path data using mechanical motion simulation software to simulate the entire process of the sampling device running along the path, monitor the device's body vibration amplitude and real-time position error. If the proportion of road sections that do not meet the constraints in the simulation is <3%, it is determined to be a smooth path.

[0055] It should be noted that the key parameters in this application represent the core quantitative indicators that determine the path shape and safety performance extracted from the local bypass path. The key parameters reflect the key design requirements set by the local bypass path to avoid dynamic interference and meet the sampling accuracy rules. The sampling adjustment path represents the adjustment path of the global sampling path to the local interference avoidance requirements, which solves the direct conflict between the global and local paths. The smooth path represents the stable sampling path when the sampling device samples each sampling point. It reflects the comprehensive adaptability of the sampling device's mechanical performance, the space limitation of the narrow channel, the sampling accuracy requirements, and the motion stability and reliability during the sampling operation. It is the final sampling path scheme that the device can directly execute.

[0056] In step 105, the sampling device is controlled to perform sampling operations at each sampling point based on the smooth path.

[0057] In some embodiments, controlling the sampling device to perform sampling operations at each sampling point based on the smooth path can be achieved by the following steps: The smooth path is converted into control commands for the sampling device; The sampling device performs sampling operations at each sampling point according to the control command.

[0058] In specific implementation, the smooth path can be converted into control commands for the sampling device in the following way: extract the three-dimensional coordinate sequence of the smooth path, the speed of each segment, and the turning angle; convert the coordinate data into execution parameters for each drive module of the sampling device using an inverse kinematics algorithm: the movement module uses stepper motor pulse control, calculates the number of pulses corresponding to each coordinate point based on the pulse equivalent, i.e., pulse number = straight-line distance between two points × pulse equivalent, and allocates pulse frequency according to the speed parameter, i.e., frequency = speed × pulse equivalent, in Hz; the extension and rotation of the sampling arm are controlled by servo motors. The telescopic range is calculated using the lead screw pitch. For example, a 5mm pitch corresponds to one rotation of the motor. The rotation angle is set according to the angle corresponding to each pulse. For example, for a stepper motor with 1.8° / 200 pulses, the angle = number of pulses × 0.009°. The start and stop of the sampling needle are converted into a solenoid valve switching signal. At the same time, the sampling task information is converted into sensor threshold parameters. For example, the sampling amount of the pressure sensor corresponds to a pressure threshold of 0.2MPa, and the depth of the displacement sensor corresponds to a 5mm electrical signal value. This forms a control command containing module identification, execution parameters, and threshold conditions, which is stored in the instruction buffer of the device control system.

[0059] In addition, in specific implementation, the sampling device can perform sampling operations on each sampling point according to the control command in the following way: After the sampling device is started, the control system in the sampling device calls the moving module in sequence according to the control command. The stepper motor is driven by pulse to move along the coordinate sequence. Every time it moves to a distance of 1cm from the target sampling point, positioning calibration is triggered. The visual camera collects the preset mark of the sampling point. The deviation between the mark center and the camera coordinate system is calculated by template matching method. The laser range sensor synchronously measures the vertical distance between the device and the sampling point. After the two data are fused, the pulse number of the moving module is finely adjusted by PID adjustment algorithm to ensure that the final position deviation is ≤±0.1mm; after the position calibration is completed. After completion, the control system calls the sampling arm module, drives the servo motor to adjust the sampling needle to the set height and angle according to the command parameters, triggers the solenoid valve to open the sampling, and at the same time, the pressure sensor monitors the pressure of the sampling pipeline in real time. When the pressure reaches the threshold of the corresponding sampling amount, it immediately outputs a low level to close the solenoid valve, completing a single sampling. After the operation of each sampling point is completed, the device's built-in data recording module automatically stores the actual sampling amount, operation time, positioning deviation and other data of that point. Then, according to the command, it moves to the next sampling point and repeats the above process until all sampling points are completed. Finally, it returns to the initial position and uploads all recorded data to the host computer through the RS485 communication interface, forming a complete sampling operation closed loop.

[0060] In another aspect, in some embodiments, this application provides an intelligent sampling device for microbial testing, with reference to... Figure 4The figure is a schematic diagram of the structure of an intelligent sampling device for microbial testing according to some embodiments of this application. The intelligent sampling device 400 for microbial testing includes: an acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to acquire the static environmental information, dynamic environmental information and sampling task information of the microbial testing area during the sampling process, so as to generate full-dimensional information of the microbial testing area. Processing module 402, in this application, is used to determine the collaborative correlation features between the various dimensions of information in the full-dimensional information, and to perform adaptive sampling point path planning for the sampling path of the sampling device in the microbial testing area based on the spatial distribution density of sampling points in the microbial testing area and the collaborative correlation features, thereby generating the global sampling path of the sampling device in the microbial testing area. It should be noted that the processing module 402 in this application is also used to determine the movement trend of the dynamic interference source in the microbial testing area, and to determine the local bypass path of the sampling device between the sampling points based on the movement trend and the sampling accuracy rules of each sampling point. Additionally, it should be noted that the processing module 402 in this application is also used to determine the motion constraints of the sampling device in the microbial testing area, and to smoothly adjust the global sampling path through the motion constraints and all local bypass paths to obtain a smooth path for the sampling device to perform stable sampling at each sampling point. The execution module 403 in this application is mainly used to control the sampling device to perform sampling operations on each sampling point based on the smooth path.

[0061] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, the processor being configured to acquire the code and execute the control method of the above-described intelligent sampling device for microbial testing.

[0062] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device for implementing a control method for an intelligent sampling device for microbial testing, according to some embodiments of this application. The control method for the intelligent sampling device for microbial testing in the above embodiments can be achieved through… Figure 5 The computer device shown is used to implement this, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0063] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0064] The communication bus 502 can be used to transmit information between the aforementioned components.

[0065] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0066] The memory 503 stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiments can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0067] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0068] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0069] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0070] In addition, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the control method of the above-described intelligent sampling device for microbial testing.

[0071] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0072] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A control method for an intelligent sampling device for microbial testing, wherein, The method involves sampling a microbial testing area using a sampling device, the microbial testing area comprising multiple sampling points, characterized by the following steps: The static environmental information, dynamic environmental information, and sampling task information of the microbial testing area during the sampling process are obtained to generate full-dimensional information of the microbial testing area. Determine the collaborative correlation features between the various dimensions of information in the full-dimensional information, and perform adaptive sampling point path planning with spatial feature matching based on the spatial distribution density of sampling points in the microbial testing area and the collaborative correlation features to generate the global sampling path of the sampling device in the microbial testing area. Determine the movement trend of dynamic interference sources in the microbial testing area, and determine the local bypass path of the sampling device between each sampling point based on the movement trend and the sampling accuracy rules of each sampling point; Determine the motion constraints of the sampling device in the microbial testing area, and smoothly adjust the global sampling path by the motion constraints and all local bypass paths to obtain a smooth path for the sampling device to perform stable sampling at each sampling point. The sampling device performs sampling operations at each sampling point based on the smooth path control.

2. The method as described in claim 1, characterized in that, Determining the collaborative correlation features between the various dimensions of information in the full-dimensional information specifically includes: The full-dimensional information is preprocessed to obtain preprocessed full-dimensional information; Based on the preprocessed full-dimensional information, determine the collaborative correlation features between the various dimensions of the full-dimensional information.

3. The method as described in claim 1, characterized in that, Based on the spatial distribution density of sampling points in the microbial testing area and the cooperative association features, adaptive sampling point path planning with spatial feature matching is performed on the sampling path of the sampling device in the microbial testing area to generate the global sampling path of the sampling device in the microbial testing area. Specifically, this includes: Obtain the spatial distribution density of sampling points in the microbial testing area; Based on the spatial distribution density, the microbial testing area is divided into a high-density area and a low-density area. Adaptive planning is performed on the sampling point paths of the high-density region and the low-density region to obtain the adaptive sampling point paths of the high-density region and the low-density region. Determine the spatial characteristic matching index between the microbial testing area and the sampling device; The adaptive sampling point paths of the high-density region and the low-density region are verified based on the spatial feature matching index to obtain the verification sampling point paths of the high-density region and the low-density region. Based on the collaborative association features, the paths of the verification sampling points in the high-density area and the low-density area are smoothly connected to obtain the global sampling path of the sampling device in the microbial testing area.

4. The method as described in claim 1, characterized in that, Determining the movement trend of dynamic interference sources in the microbial testing area specifically includes: Obtain the motion data and types of dynamic interference sources in the microbial testing area; The motion data is denoised to obtain denoised motion data; The movement trend of the dynamic interference source in the microbial testing area is determined based on the denoised motion data and the type of the dynamic interference source.

5. The method as described in claim 1, characterized in that, Determining the local bypass path of the sampling device between sampling points based on the movement trend and the sampling accuracy rules of each sampling point specifically includes: Obtain the sampling accuracy rules for each sampling point; Determine conflict sampling paths between each sampling point based on the described movement trend; The local bypass path of the sampling device between each sampling point is determined based on all conflict sampling paths and all sampling accuracy rules.

6. The method as described in claim 1, characterized in that, The specific constraints on the movement of the sampling device in the microbial testing area include: Obtain the spatial dimensions of the microbial testing area and the movement dimensions of the sampling device; The motion constraints of the sampling device in the microbial testing area are determined based on the spatial dimensions and the motion dimensions.

7. The method as described in claim 1, characterized in that, By smoothing the global sampling path using the aforementioned motion constraints and all local avoidance paths, the smooth path obtained when the sampling device performs stable sampling at each sampling point specifically includes: Extract the key parameters of each local bypass path; The global sampling path is adjusted for conflicts based on all key parameters to obtain the sampling adjustment path. The sampling adjustment path is smoothed according to the motion constraints to obtain a smooth path for the sampling device to perform smooth sampling at each sampling point.

8. A smart sampling device for microbial testing, wherein, A sampling device is used to sample a microbial testing area, the microbial testing area including multiple sampling points, characterized in that the device comprises: The acquisition module is used to acquire static environmental information, dynamic environmental information, and sampling task information of the microbial testing area during the sampling process, so as to generate full-dimensional information of the microbial testing area. The processing module is used to determine the collaborative correlation features between the various dimensions of information in the full-dimensional information, and to perform adaptive sampling point path planning for the sampling path of the sampling device in the microbial testing area based on the spatial distribution density of sampling points in the microbial testing area and the collaborative correlation features, thereby generating the global sampling path of the sampling device in the microbial testing area. The processing module is also used to determine the movement trend of dynamic interference sources in the microbial testing area, and to determine the local bypass path of the sampling device between each sampling point based on the movement trend and the sampling accuracy rules of each sampling point. The processing module is also used to determine the motion constraints of the sampling device in the microbial testing area, and to smoothly adjust the global sampling path through the motion constraints and all local bypass paths to obtain a smooth path for the sampling device to perform stable sampling at each sampling point. The execution module is used to control the sampling device to perform sampling operations at each sampling point based on the smooth path.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the control method of the intelligent sampling device for microbial testing as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the control method of the intelligent sampling device for microbial testing as described in any one of claims 1 to 7.

Images