A sampling method and system for a microbial population analyzer

Abstract

The present application discloses a sampling method and system for a microbial population analyzer, the method comprising establishing a detection sampling coordinate system for a sewage pool; collecting water samples from the sewage pool to obtain water quality parameters, and then calculating the water quality scores of each area in the sewage pool based on the water quality parameters and the process characteristics of sewage treatment; dividing the water into multiple abnormality level intervals according to the water quality scores, and dynamically allocating sampling points for different abnormality level intervals. Thus, by collecting water samples from the sewage pool, the water quality parameters of the water samples and the process characteristics of sewage treatment are obtained, and then the water quality scores are calculated based on the two, and the final results are matched with the constructed detection sampling coordinate system; the sampling points can correspond to the current situation of the sewage pool, making the selection of sampling points more scientific, and the samples used for microbial population analysis can make the final analysis results more accurate, thereby breaking through the modeling bottleneck of the separation between biological response and water quality parameters.

Description

Technical Field

[0001] The present application relates to the field of water body detection, and in particular to a sampling method and system for a microbial population analyzer. Background Art

[0002] In the fields of environmental monitoring and biotechnology, traditional microbial population analysis technology systems have significant limitations. This technical process starts with manual on-site sampling and uses microscopy to identify and analyze populations. However, this model faces multiple technical bottlenecks in practical application:

[0003] First, the manual sampling process lacks standardized spatial distribution and time series design, resulting in sample randomness significantly higher than the actual microbial distribution characteristics of the environment, making it difficult to represent the population structure of the target environment; second, due to the frequency of manual operations and time-consuming detection, traditional methods find it difficult to construct statistically significant time series data and cannot meet the spatiotemporal resolution requirements required for environmental systems to monitor dynamic changes in microorganisms; third, microscopy relies on the operator's experience for morphological identification and lacks unified quantitative standards, making it difficult to conduct accurate quantitative analysis of microbial populations.

[0004] Therefore, when faced with rapid succession of microbial communities, sampling decisions cannot respond to environmental changes in a timely manner, resulting in inaccurate test results. Summary of the Invention

[0005] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a sampling method and system for a microbial population analyzer that can construct a characteristic map of the microbial living environment in the area to be tested. This system, combined with multi-axis motion control, enables sampling points to respond to environmental changes, improving the representativeness of the sampling points and thereby increasing the accuracy of the test results.

[0006] In the first aspect, a sampling method for a microbial population analyzer according to an embodiment of the present application includes: establishing a detection sampling coordinate system for a sewage pool; collecting water samples from the sewage pool to obtain water quality parameters, and then calculating the water quality scores of each area in the sewage pool based on the water quality parameters and the process characteristics of sewage treatment; dividing a plurality of abnormal level intervals according to the water quality scores, and dynamically allocating sampling points for different abnormal level intervals; driving the sampling device to collect samples at the sampling points, and then performing microbial population analysis on the samples.

[0007] The effect is: by collecting water samples from sewage pools, the water quality parameters of the water samples and the process characteristics of sewage treatment are obtained, and then the water quality score is calculated based on the two, and the final result is matched with the constructed detection sampling coordinate system; thus, the sampling points can correspond to the current situation of the sewage pool, making the selection of sampling points more scientific, and the samples collected based on such sampling points for microbial population analysis can make the final analysis results more accurate, thereby breaking through the modeling bottleneck of the separation between biological response and water quality parameters.

[0008] Furthermore, water samples from sewage pools are collected using the following steps: a dynamic scheduling strategy is generated based on the operating status of multiple process lines in the sewage treatment plant to determine the target process line; sampling weights are calculated based on the real-time biochemical parameters of each treatment pool to generate a priority queue; and cross-process line sampling is performed using a sampling device with a mobile cart mounted on the bottom.

[0009] Furthermore, the sampling device measures the reference position of the water surface, and a three-dimensional detection sampling coordinate system is established with the water surface reference as a reference.

[0010] Furthermore, the three-dimensional spatial coordinates of the sampling points are converted into mechanical arm joint motion parameters; and the sampling probe of the sampling device is controlled by the multi-axis mechanical arm to reach a specified spatial position.

[0011] Furthermore, the water quality parameters include at least three of pH value, dissolved oxygen value (DO), chemical oxygen demand (COD), turbidity value, temperature value, and conductivity value.

[0012] Furthermore, the calculation of the water quality score includes: normalizing the values ​​of the water quality parameters relative to the normal range of the process.

[0013] Furthermore, normalizing the values ​​of the water quality parameters relative to the normal range of the process includes:

[0014] Defining enumeration parameters ={ PH DO Cond Tur Tem UVCOD};

[0015] Where, is the spatial point of the x, y, and z axes in the Cartesian coordinate system Enumeration parameters of; PH : pH value; DO : dissolved oxygen value; Cond : conductivity value; Tur : turbidity value; Tem : temperature value; UVCOD : COD value detected by UV method;

[0016] For enumeration parameters A dimensionless quantization normalization process is performed to normalize the water quality scores of each area in the sewage pool to [0, 1], and a plurality of abnormal level intervals are divided according to the water quality score results.

[0017] Furthermore, the criticality, sensitivity and influence of the water quality parameters in the process on the microbial activity are used as influence weights, and the single point deviation is calculated to correct each of the abnormal level intervals.

[0018] Furthermore, the calculation of single point deviation includes:

[0019] Define the criticality scoring model: ;

[0020] Where, : parameter criticality score;

[0021] : Process importance;

[0022] : parameter sensitivity;

[0023] : Impact of microbial activity;

[0024] in, , α, β, and γ are all coefficients;

[0025] The single-point deviation score is defined as:

[0026]

[0027] Where, : Single point deviation score, : intermediate variable, The intermediate variable is Parameter criticality score at , : The deviation corresponding to a single parameter of a single point.

[0028] Furthermore, based on the single point deviation score of the determined sampling point , score the deviation of each single point Add to collection middle;

[0029] And serialize the coordinates:

[0030] , filter the maximum and minimum values: , ;

[0031] Set the number of symmetrical deviation intervals: n, then the segmentation score value of the deviation interval point is:

[0032] ;

[0033] Where, : The split score value of the deviation interval point, : intermediate variable, ;

[0034]

[0035] : Number of non-zero value intervals;

[0036] Then count the number of zero values ​​in each scoring interval:

[0037]

[0038] Then get the Results for rating intervals:

[0039]

[0040] Place the positions of each scoring area into the corresponding sets , , : The previous scoring interval split score value of ; where: , M is a set The number of elements in , ∧ is the mathematical symbol for logical AND, and V is the mathematical symbol for logical OR;

[0041]

[0042] : No. The first interval The three-dimensional spatial position of the point, ;

[0043] Thus, the number of samples in each interval is obtained:

[0044]

[0045] : The number of samples in each sampling area;

[0046] : total sampling rate;

[0047] exist Randomly generated from the collection Sampling point, put its sampling point as location information into gather:

[0048]

[0049] in, For the The first random sample of the rating interval Point three-dimensional space position , , will Point three-dimensional space position The data are fed back to the sampling device for fixed-point sampling.

[0050] In a second aspect, an embodiment of the present application discloses a sampling system for a microbial population analyzer, wherein the sampling device is configured to collect water samples from a sewage pool; the water quality analysis module is configured to perform water quality analysis on the collected water samples from the sewage pool to obtain water quality parameters; the main control system is configured to execute the data processing process in the sampling method of the microbial population analyzer as described above; the radio frequency communication module is configured to execute communication between the main control system, the sampling device and the water quality analysis module; the storage module and the radio frequency communication module are configured to execute communication between the main control system, the sampling device and the water quality analysis module; the power supply module and the radio frequency communication module are configured to execute communication between the main control system, the sampling device and the water quality analysis module.

[0051] Furthermore, the system also includes: a process line scheduling module for allocating sampling resources based on water inlet load, energy consumption indicators and fault status; a weight calculation engine with a built-in weight algorithm based on the fluctuation coefficient of water quality parameters in the treatment pool; and a mobile sampling unit consisting of a multi-axis robotic arm and a trolley.

[0052] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0054] Figure 1 is a schematic flow chart of a sampling method of a microbial population analyzer according to some embodiments of the present application;

[0055] Figure 2 is a schematic structural diagram of a sampling system of a microbial population analyzer according to some embodiments of the present application;

[0056] Figure 3 It is a schematic diagram of the principle of the main control system in the sampling system of the microbial population analyzer according to some embodiments of the present application.

[0057] Description of reference numerals:

[0058] 10-sampling device; 20-water quality analysis module; 30-main control system; 40-RF communication module; 50-storage module; 60-power module;

[0059] 1001 - processor; 1002 - communication bus; 1003 - user interface; 1004 - network interface; 1005 - memory. DETAILED DESCRIPTION

[0060] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. The specific features, structures, materials, or characteristics described in this specification may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and integrate different embodiments or examples, and features of different embodiments or examples, as described herein, unless otherwise inconsistent.

[0061] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions.

[0062] refer to Figure 1-Figure 3 , understand the sampling method and system of a microbial population analyzer according to an embodiment of the present application.

[0063] Before explaining, let me explain some professional background:

[0064] Water quality parameters have many effects on the activated sludge colony structure. They act together on the growth, metabolism and community structure of microorganisms, thereby affecting the effect of sewage treatment.

[0065] Temperature is a key factor influencing microbial activity. Mesophilic bacteria are most active within a temperature range of 15-35°C, effectively treating wastewater. However, excessively low or high water temperatures inhibit microbial activity and may even lead to microbial death. Low temperatures can lead to excessive growth of filamentous bacteria, causing sludge bulking; high temperatures can disrupt the balance of microbial communities, compromising treatment efficiency.

[0066] pH also has a significant impact on microbial growth and metabolism. An optimal pH range (typically 6-9) maintains normal microbial physiological activity. Excessively low or high pH values ​​can inhibit microbial activity, alter colony structure, and even worsen treatment effectiveness. For example, when the pH is below 4.5, mold may become dominant, resulting in decreased sludge flocculation performance. Meanwhile, when the pH is above 9, bacterial flocs may disintegrate, leading to an increase in suspended solids.

[0067] An appropriate dissolved oxygen level (generally maintained at 2-4 mg / L) promotes normal microbial growth and metabolism, resulting in good flocculation and settling properties in activated sludge. However, both excessively low and high dissolved oxygen levels can negatively impact the microbial community structure. Low dissolved oxygen levels can lead to excessive growth of filamentous bacteria, causing sludge bulking; high dissolved oxygen levels can disrupt the balance of the microbial community structure, compromising treatment efficiency.

[0068] Water quality parameters such as conductivity, turbidity, and UVCOD / COD also affect the activated sludge colony structure. Conductivity reflects the ion concentration in the water, affecting the osmotic pressure and physiological activity of microorganisms; turbidity reflects the density of suspended particles in the sludge, affecting the settling performance of the activated sludge and the effluent quality; and UVCOD / COD reflects the organic matter content in the wastewater, affecting microbial metabolic activity and community structure. Changes in these parameters can alter the microbial living environment, thereby affecting the activated sludge colony structure.

[0069] Therefore, the influence of water quality parameters on the activated sludge colony structure is complex and multi-dimensional. During the activated sludge sampling process, the dynamic changes of water quality parameters directly affect the representativeness of the sampling point.

[0070] Based on the above content, the present application embodiment provides a sampling method of a microbial population analyzer, referring to Figure 1 Understand that when screening typical sampling points based on water quality parameters, the treatment process characteristics and parameter correlation are combined, including: obtaining the water body of the sewage pool and detecting the water quality parameters, and constructing the detection sampling coordinate system of the sewage pool; for example, locating the water surface reference point through the sampling device, and constructing a coordinate system to abstract the sewage pool (such as the aeration tank / digestion tank, etc.) into a spatial model that can be quantified and located.

[0071] Water samples are collected from the sewage pool to obtain water quality parameters, and then the water quality scores of each area in the sewage pool are calculated based on the water quality parameters and the process characteristics of sewage treatment. By detecting multi-dimensional water quality parameters such as pH, DO, COD, turbidity, temperature, conductivity, etc., the aforementioned detection sampling coordinate system is divided into corresponding intervals.

[0072] Specifically, the water quality score is divided into multiple abnormal water quality level intervals, and sampling points are dynamically allocated for different abnormal water quality level intervals; the sampling device is driven to collect samples at the points, and the samples are then analyzed for microbial populations.

[0073] In some examples, collecting water samples from the sewage pool specifically includes: formulating a dynamic scheduling strategy to select a target process line based on the real-time operating status of multiple process lines in the sewage treatment plant, such as influent flow, equipment operating status, and alarm information. This dynamic scheduling strategy can be based on existing process line load balancing algorithms or key equipment failure rate prediction models; and calculating sampling weights based on the real-time biochemical parameters of each treatment pool, such as aeration tanks and anaerobic digesters. These real-time biochemical parameters can be core water quality indicators such as pH, dissolved oxygen (DO), chemical oxygen demand (COD), and temperature. The system then calculates sampling weights based on the degree to which the parameters deviate from a set baseline value or the rate of change. A priority queue is generated, such as a list of sampling points sorted from high to low based on the weighted scores of each treatment pool. Finally, a sampling device, which can be equipped with a mobile vehicle that can move along a preset track or autonomously navigate the bottom, executes sampling across the process lines in the order of the generated priority queue.

[0074] It can be understood that the embodiment of the present application obtains the water quality parameters of the water samples and the process characteristics of sewage treatment by collecting water samples from the sewage pool, and then calculates the water quality score based on the two, and corresponds the final result to the constructed detection sampling coordinate system; thus, the sampling points can correspond to the current situation of the sewage pool, making the selection of sampling points more scientific, and the samples collected based on such sampling points for microbial population analysis can make the final analysis results more accurate, thereby breaking through the modeling bottleneck of the separation of biological response and water quality parameters.

[0075] For example, taking the activated sludge sampling process as an example, the dynamic changes of water quality parameters directly affect the representativeness of the sampling points. When screening typical sampling points based on water quality parameters, it is necessary to combine the treatment process characteristics and the correlation between the parameters.

[0076] Specifically, the sampling method of the microbial population analyzer in some embodiments includes the following steps:

[0077] The detection sampling coordinate system is preset according to the location of the sewage treatment tank. After initializing any sampling device, the detection sampling coordinate system can be determined by measuring the water surface position through the device.

[0078] For example, the preset spatial point is ,and ,in , , The sampling subdivision number of preset points for the x, y, and z axes is then defined, and the detection sampling coordinate system of the sewage treatment tank is then defined. The sewage treatment tank described here can be any of the inlet tanks, aeration tanks, digestion tanks, and other similar sewage tanks.

[0079] The detection sampling coordinate system is: ;

[0080] Where, Detection is the sampling coordinate system, which corresponds to the above content Horizontal plane coordinates, : perpendicular to the horizontal coordinate; : A set of Cartesian three-dimensional space coordinates.

[0081] Based on the water quality parameters, multiple abnormal level intervals are divided, and sampling points are determined according to the abnormal level intervals. Therefore, based on the detection results of water body sampling, typical sampling points (i.e., abnormal water quality areas with abnormal water quality) are determined, so that the sampled samples can match the current water quality characteristics to improve the detection accuracy.

[0082] Specifically, this embodiment determines sampling points based on a water quality calculation model. This model uses pre-collected water samples to obtain water quality parameters, thereby scoring the water quality at each point. This allows for the selection of representative sampling points based on the water quality scores for each region. Water quality parameters include, but are not limited to, pH, dissolved oxygen, conductivity, turbidity, and temperature, and can be detected using sensors.

[0083] In some examples, based on the integrated sewage treatment process and the operating requirements of the activated sludge process, the normal ranges of activated sludge water quality parameters are as follows:

[0084] In some examples, based on the integrated sewage treatment process and the operating requirements of the activated sludge process, the normal ranges of activated sludge water quality parameters are as follows:

[0085] The normal pH range is 6.5-8.5. Activated sludge microorganisms are most active within this range. When the pH is below 4.5 or above 9, microbial metabolism is inhibited, which can easily lead to sludge bulking.

[0086] Dissolved oxygen (DO): Aerobic zone: 2 mg / L - 4 mg / L, thereby ensuring the efficiency of microbial degradation of organic matter and preventing the proliferation of filamentous bacteria; Anoxic zone: less than 0.5 mg / L. Anaerobic zone: less than 0.2 mg / L.

[0087] The optimal temperature range is 15°C - 30°C. Under this temperature, the microbial metabolic rate and sludge activity reach their highest levels. When the temperature is above 40°C or below 10°C, the activity will significantly decrease or even stop.

[0088] COD (Chemical Oxygen Demand): Influent concentration: Based on process design, generally no more than 1000 mg / L (subject to adjustment based on treatment capacity). Outlet requirements: Typically must meet discharge standards (e.g., less than 50-100 mg / L), depending on local environmental requirements.

[0089] Turbidity (treated effluent): Generally less than 10 NTU. This indicator reflects sludge settling performance. Excessive turbidity may be caused by sludge bulking or poor settling.

[0090] Conductivity: There is no unified standard and it needs to be adjusted according to the raw water quality. Industrial wastewater generally has a higher conductivity (e.g., 1000 μS / cm - 5000 μS / cm), while domestic wastewater has a lower conductivity (500 μS / cm - 1500 μS / cm). Abnormal fluctuations that could affect microbial activity should be avoided.

[0091] Other key parameters include: Sludge concentration (MLSS): 2000 mg / L - 5000 mg / L, which needs to be adjusted based on the process type (e.g., aerobic / anaerobic). Sludge settling ratio (SV30): 20% - 30%, which reflects sludge settling performance.

[0092] Furthermore, it's important to note the interplay between parameters. For example, dissolved oxygen (DO) needs to be controlled in tandem with temperature and pH. High temperatures can reduce DO saturation. In some cases, if parameters exceed normal ranges, it's necessary to investigate issues with influent quality, aeration efficiency, or sludge activity, and, if necessary, add chemicals or adjust the process. For specific process parameters or complete standards, refer to wastewater treatment design specifications or test reports.

[0093] In this example, the water quality calculation model is:

[0094] Defining enumeration parameters ={ PH DO Cond Tur Tem UVCOD};

[0095] Where, is the spatial point of the x, y, and z axes in the Cartesian coordinate system Enumeration parameters of; PH : pH value; DO : dissolved oxygen value; Cond : conductivity value; Tur : turbidity value; Tem : temperature value; UVCOD: COD value detected by UV method.

[0096] For enumeration parameters A dimensionless quantitative normalization process is performed to normalize the absolute value of the water quality score of each area in the sewage pool to [0, 1]. The maximum and minimum values ​​of the water quality score are counted, and the water quality score is divided into several intervals; if there is 0 or 1, 0 is defined as the absolute abnormal interval, the non-0 and non-1 intervals are defined as the abnormal interval, and 1 is defined as the optimal interval. All absolute abnormal intervals are sampled, and the total number of samples for the abnormal interval is a fixed value. Sampling points are randomly selected in each interval according to the sampling ratio.

[0097] In some embodiments, a process criticality score coefficient is introduced during the normalization process to correct the result of the normalization process so as to associate it with the process parameters.

[0098] For example, for enumeration parameters The dimensionless quantization normalization process specifically includes the following:

[0099] ;

[0100] Where, is the normalized maximum water quality deviation, ;definition: , for no deviation, Increasing deviation, Decrement offset; : The spatial point of x, y, and z axes in the Cartesian coordinate system The value of water quality parameter m; : The minimum value of the normal range of water quality parameter m; : The minimum value of the normal range of water quality parameter m; : The optimal value of the water quality parameter No. is within the normal range.

[0101] Then, the method includes: defining a criticality scoring model based on process requirements

[0102] ;

[0103] Where, : parameter criticality score; : Process importance, which is adapted by process engineers according to different processes; for example, if COD and DO are key parameters, conductivity is a secondary parameter; COD: 1.0, DO: 0.9; Cond: 0.2; : Parameter sensitivity (the degree of impact of a unit change on the system), which is adapted by process engineers based on different processes; for example: pH: 0.8, COD: 0.01; :The influence of microbial activity (such as pH and temperature directly affect the metabolic rate), which is adapted by process engineers according to different processes; for example: pH: 0.9, DO: 0.85, COD, 0.02, α, β, γ are all coefficients, which are adapted by process engineers according to different processes, ;default: .

[0104] Therefore, the single-point deviation score is defined as:

[0105]

[0106] Where, : Single point deviation score, : intermediate variable, The intermediate variable is Parameter criticality score at , : The deviation corresponding to a single parameter of a single point.

[0107] It is understandable that the above method can be used to screen sampling points based on water quality parameters, combining treatment process characteristics with parameter correlation. The following is a systematic strategy; and water quality parameters (DO / pH / COD, etc.) are associated with microbial activity to give biological significance to the parameters and avoid pure mathematical weighting bias.

[0108] Furthermore, the sampling points are determined according to the determined sampling results and sampling is performed, and the single point deviation scores of the sampling points determined by the sampling results are scored. Put data into collection and serialize the coordinates:

[0109] , filter the maximum and minimum values: , .

[0110] Set the number of symmetrical deviation intervals: n, then the segmentation score value of the deviation interval point is:

[0111] ;

[0112] Where, : The split score value of the deviation interval point, : intermediate variable, ;

[0113]

[0114] : Number of non-zero value intervals;

[0115] Then count the number of zero values ​​in each scoring interval:

[0116]

[0117] Then get the Results for rating intervals:

[0118]

[0119] Place the positions of each scoring area into the corresponding sets , , : The previous scoring interval split score value of ; where: , M is a set The number of elements in , ∧ is the mathematical symbol for logical AND, and V is the mathematical symbol for logical OR;

[0120]

[0121] : No. The first interval The three-dimensional spatial position of the point, ;

[0122] Thus, the number of samples in each interval is obtained:

[0123]

[0124] : The number of samples in each sampling area;

[0125] : total sampling rate;

[0126] exist Randomly generated from the collection Sampling point, put its sampling point as location information into gather:

[0127]

[0128] in, For the The first random sample of the rating interval Point three-dimensional space position , .

[0129] Therefore, the Point three-dimensional space position Feedback is given to the sampling system to drive the sampling arm to control the sampling probe for fixed-point sampling.

[0130] For example, taking a six-axis robotic sampling arm as an example, the kinematic model of the robotic arm adopts the Denavit-Hartenberg (DH) parameter method:

[0131] ;

[0132] : single joint transformation matrix;

[0133] : Length of the common perpendicular line between the axis of connecting rod i-1 and the axis of connecting rod i (along x i-1 axis direction);

[0134] :around x i-1 The axis rotates so that z i-1 With z i The angle of alignment (the right-hand rule implicitly determines the y-axis);

[0135] : Along z i Axis direction, from z i-1 to z i The translation distance;

[0136] :around z i The axis rotates so that z i-1 With z i Angle of alignment (right-hand rule implicitly determines the y-axis)

[0137] Total transformation matrix (base to tip):

[0138] Where, : total transformation matrix from base to n axis; : Dot multiplication (also called dot product) operation rule; : intermediate variable.

[0139] Then, based on the end sampling device from the starting position To the end position The solution is:

[0140] According to the kinematic model of the manipulator and the total transformation matrix, the end pose matrix is ​​obtained:

[0141]

[0142] Determine the first three joint angles so that the wrist center reaches the target position:

[0143]

[0144] Where, : The center of the wrist reaches the target position; : is the end normal vector: : Normal vectors in the three directions of the end;

[0145] but:

[0146] Constructing geometric equations:

[0147] The solution is:

[0148]

[0149] , : intermediate variable; , , : The three-dimensional coordinates of the wrist center point in the base coordinate system.

[0150] Calculate the wrist coordinate system rotation matrix:

[0151]

[0152] : The composite rotation matrix from the base coordinate system to the third joint coordinate system; : Rotation around the Z axis of the base coordinate system The rotation matrix of

[0153] ;

[0154] : Rotate around the Y axis of the new coordinate system The rotation matrix of

[0155] ;

[0156] : Rotate around the Z axis of the latest coordinate system The rotation matrix of

[0157] ;

[0158] : Rotation around the Z axis of the base coordinate system The rotation matrix of

[0159] ;

[0160] : Rotate around the Y axis of the new coordinate system The rotation matrix of

[0161] ;

[0162] : Rotate around the Z axis of the latest coordinate system The rotation matrix of

[0163] ;

[0164] The solution is:

[0165]

[0166]

[0167]

[0168] in, : The rotation matrix from the third joint coordinate system to the wrist coordinate system; Represents the element with row number row and column number column in the coordinate system rotation matrix with the number of joints frame; frame: the number of joints; row: the number of rows in the coordinate system rotation matrix; column: the number of columns in the coordinate system rotation matrix; for example: Representation matrix Parameters in: 1.

[0169] Finally, based on the determination of The random sampling of the interval Point three-dimensional space position , that is; will Substitute into the model to solve the motion angle of each axis , thereby collecting samples corresponding to each typical sampling point determined based on water quality parameters.

[0170] The water sample is subjected to microbial population inversion. Any inversion method can be used in the embodiments. For example, single-point-based biological population inversion can be performed using the method described in the applicant's prior patent (Application No. 2025107344728). Alternatively, other inversion methods may be used, which will not be detailed here.

[0171] This method breaks away from traditional grid-based uniform sampling methods, constructing a microbial concentration field using water quality parameters as covariates. By dividing scoring intervals, it dynamically identifies hotspots of microbial activity within a three-dimensional water body, achieving targeted sampling of representative intervals and matching sampling points with water quality characteristics. Furthermore, by integrating spatial distribution models of functional bacterial communities (such as ammonia oxidizers), it enables quantitative assessment of activated sludge metabolic capacity, going beyond simple quantitative statistics. This approach addresses the industry challenges of modeling biological responses separately from water quality parameters and the risk of error associated with linear extrapolation of sampling uncertainty.

[0172] It should be noted that the above embodiment is described in a specific step sequence, which does not limit the method of this embodiment to the above step sequence, and the claims should not be limited to this sequence when interpreting.

[0173] Based on the description of the above embodiments, in some embodiments, the multi-process line scheduling strategy of the sewage treatment plant can be further integrated to achieve collaborative optimization of the entire process.

[0174] Specifically, the multi-process line of a sewage treatment plant includes multiple process lines, and different scheduling strategies can be adopted for each process line. For example, when it is detected that the dissolved oxygen in the aeration tank of a certain process line continuously deviates from the threshold, the scheduling center will dynamically increase the sampling weight of the process line to 1.5 times the baseline value, and at the same time, adjust the sampling frequency of adjacent process units (such as the secondary sedimentation tank) in a coordinated manner. Different weights are assigned based on the criticality of the process and the risk of abnormal water quality transmission. For example, the aeration tank has a basic weight of 0.4 because it undertakes the core biodegradation function; the digester is set to 0.3 due to the need for sludge stability monitoring; the inlet tank and the return sludge channel are configured with weights of 0.2 and 0.1, respectively. In some cases, when multiple process units trigger abnormal alarms at the same time, the system will allocate mobile sampling resources according to the weight ratio.

[0175] Furthermore, to support flexible sampling across process lines, a track-based intelligent trolley is installed at the bottom of the sampling device, enabling three-dimensional positioning and sampling along pre-set process corridors. For example, the sampling device can generate an optimal movement path based on process line weights and transmit sampling data in real time to a central analysis platform.

[0176] The sampling system of the microbial population analyzer according to the embodiment of the present application includes a sampling device 10 , a water quality analysis module 20 , a main control system 30 , a radio frequency communication module 40 , a storage module 50 and a power supply module 60 .

[0177] Specifically, refer to Figure 2 Figure 2 shows a schematic diagram of the sampling system structure of a microbial population analyzer. In the sampling system of the microbial population analyzer, a sampling device 10 is configured to collect water samples from a sewage pool; a water quality analysis module 20 is configured to analyze the collected water samples from the sewage pool to obtain water quality parameters; a main control system 30 is configured to execute the data processing process described above in the sampling method of the microbial population analyzer; a radio frequency communication module 40 is configured to perform communication between the main control system, the sampling device, and the water quality analysis module; a storage module 50 is configured to perform data storage during the data processing process; and a power supply module 60 is configured to power the sampling device 10, the water quality analysis module 20, the main control system 30, the radio frequency communication module 40, and the storage module 50.

[0178] Therefore, this embodiment proposes a sampling system for a microbial population analyzer, which utilizes a sampling device 10, a water quality analysis module 20, a main control system 30, a radio frequency communication module 40, a storage module 50, and a power supply module 60 to collect water samples from a sewage pool to obtain water quality parameters of the water samples and process characteristics of sewage treatment. Then, a water quality score is calculated based on the two, and the final result is matched with the constructed detection sampling coordinate system. Thus, the sampling points correspond to the current conditions of the sewage pool, making the selection of sampling points more scientific, and samples for microbial population analysis collected based on such sampling points can make the final analysis results more accurate, thereby breaking through the modeling bottleneck of the separation between biological response and water quality parameters.

[0179] In some embodiments, the system also includes a process line scheduling module for dynamically allocating resources and sampling task priorities of mobile sampling devices based on the real-time operating parameters of multiple process production lines of the sewage treatment plant (including but not limited to influent load, energy consumption indicators and key equipment failure status); a weight calculation engine with a built-in algorithm that calculates the sampling priority weight of each treatment pool based on the fluctuation coefficient (such as degree of deviation, rate of change) between the water quality parameters (such as pH, DO, COD, temperature, etc.) monitored in real time in each treatment pool (such as aeration tank, digester) and their set benchmark values; a mobile sampling unit consisting of an intelligent mobile platform (such as a rail trolley or an autonomous navigation trolley) mounted at the bottom and a multi-axis robotic arm mounted on the top. This combined structure enables the sampling device to move along a preset path or an autonomously planned path to the designated treatment pool of the target process line, and accurately locate a specific position under the water surface through the robotic arm to perform water sample collection operations.

[0180] Further, refer to Figure 3 As shown, a schematic diagram of the principle of the main control system in the sampling system of the microbial population analyzer. The main control system may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0181] Those skilled in the art will understand that Figure 3The structure of the main control system shown in the figure does not constitute a limitation on the main control system, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0182] like Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a data processing program for the sampling method of the microbial population analyzer.

[0183] exist Figure 3 In the main control system shown, the network interface 1004 is mainly used to connect to the backend server and communicate data with the backend server; the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client; and the processor 1001 can be used to call the data processing program of the sampling method of the microbial population analyzer stored in the memory 1005 and perform the following operations:

[0184] Establish a detection sampling coordinate system for the sewage pool;

[0185] Collecting water samples from the sewage pool to obtain water quality parameters, and then calculating a water quality score for each area in the sewage pool based on the water quality parameters and process characteristics of sewage treatment;

[0186] Divide the water quality into multiple abnormality level intervals according to the water quality score, and dynamically allocate sampling points for different abnormality level intervals;

[0187] The sampling device is driven to collect a sample at the sampling point, and then the sample is subjected to microbial population analysis.

[0188] The specific embodiments of the present invention applied to the main control system are basically the same as the above-mentioned sampling method and system embodiments of the microbial population analyzer, and will not be described in detail here.

[0189] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0190] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0191] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0192] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and are not to be construed as limiting the present invention. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A sampling method for a microbial population analyzer, characterized in that: include: Establish a detection sampling coordinate system for the sewage pool; Collecting water samples from the sewage pool to obtain water quality parameters, and then calculating a water quality score for each area in the sewage pool based on the water quality parameters and process characteristics of sewage treatment; Divide the water quality into multiple abnormality level intervals according to the water quality score, and dynamically allocate sampling points for different abnormality level intervals; driving the sampling device to collect a sample at the sampling point, and then performing a microbial population analysis on the sample; The water quality parameters include at least three of pH value, dissolved oxygen value DO, chemical oxygen demand COD, turbidity value, temperature value or conductivity value; The calculation of the water quality score of each area in the sewage pool includes: normalizing the values ​​of the water quality parameters relative to the normal range of the process; Defining enumeration parameters ={ PH DO Cond Tur Tem UVCOD }; Where, is the spatial point of the x, y, and z axes in the Cartesian coordinate system Enumeration parameters of; PH : pH value; DO : dissolved oxygen value; Cond : conductivity value; Tur : turbidity value; Tem : temperature value; UVCOD : COD value detected by UV method; For enumeration parameters Performing dimensionless quantization normalization processing to normalize the water quality scores of each area in the sewage pool to [0, 1], and dividing the abnormal level intervals into multiple intervals according to the water quality score results; The criticality, sensitivity and influence of the water quality parameters in the process on microbial activity are used as influence weights, and the single-point deviation is calculated to correct each of the abnormal level intervals.

2. The sampling method of the microbial population analyzer according to claim 1, characterized in that: The collecting of water samples from the sewage pool comprises: A dynamic scheduling strategy is generated based on the operating status of multiple process lines in the sewage treatment plant to determine the target process line; sampling weights are calculated based on the real-time biochemical parameters of each treatment pool to generate a priority queue; and cross-process line sampling is performed using a sampling device equipped with a mobile cart at the bottom.

3. The sampling method of the microbial population analyzer according to claim 1, characterized in that: The sampling device measures the reference position of the water surface, and the three-dimensional detection sampling coordinate system is established with the water surface reference as a reference.

4. The sampling method of the microbial population analyzer according to claim 3, characterized in that: Converting the three-dimensional spatial coordinates of the sampling points into mechanical arm joint motion parameters; The sampling probe of the sampling device is controlled by a multi-axis robotic arm to reach a specified spatial position.

5. The sampling method of the microbial population analyzer according to claim 1, characterized in that: Calculating the single point deviation includes: Define the criticality scoring model: ； Where, : parameter criticality score; : Process importance; : parameter sensitivity; : Impact of microbial activity; in, , α, β and γ are coefficients; The single-point deviation score is defined as: ； Where, : Single point deviation score, : intermediate variable, The intermediate variable is Parameter criticality score at , : The deviation corresponding to a single parameter of a single point.

6. The sampling method of the microbial population analyzer according to claim 5, characterized in that: Based on the single point deviation score of the determined sampling point , score the deviation of each single point Add to collection middle; And serialize the coordinates: , filter the maximum and minimum values: , ; Set the number of symmetrical deviation intervals: n, then the segmentation score value of the deviation interval point is: ； Where, : The split score value of the deviation interval point, : intermediate variable, ; ； : Number of non-zero value intervals; Then count the number of zero values ​​in each scoring interval: ； Then get the Results for rating intervals: ； Place the positions of each scoring area into the corresponding sets , , : The previous scoring interval split score value of ; where: , M is a set The number of elements in , ∧ is the mathematical symbol for logical AND, and V is the mathematical symbol for logical OR; ； : No. The first interval The three-dimensional spatial position of the point, ; Thus, the number of samples in each interval is obtained: ； : The number of samples in each sampling area; : total sampling rate; exist Randomly generated from the collection Sampling point, put its sampling point as location information into gather: ； in, For the The first random sample of the rating interval Point three-dimensional space position , , will Point three-dimensional space position The data are fed back to the sampling device for fixed-point sampling.

7. A sampling system for a microbial population analyzer, characterized in that: include: a sampling device configured to collect water samples from the sewage pond; A water quality analysis module is configured to perform water quality analysis on water samples collected from the sewage pool to obtain water quality parameters; A main control system configured to execute the data processing process in the sampling method of the microbial population analyzer according to any one of claims 1 to 6; a radio frequency communication module configured to perform communication between the main control system, the sampling device, and the water quality analysis module; a storage module configured to perform data storage during data processing; The power module is configured to supply power to the sampling device, the water quality analysis module, the main control system, the radio frequency communication module and the storage module.

8. The sampling system of the microbial population analyzer according to claim 7, characterized in that: The system also includes: a process line scheduling module for allocating sampling resources based on influent load, energy consumption indicators and fault status; a weight calculation engine with a built-in weighting algorithm based on the fluctuation coefficient of treatment tank water quality parameters; and a mobile sampling unit consisting of a multi-axis robotic arm and a trolley.

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