A single-point calculation method, system and inversion method for single-point sampling of microbial population analysis

Abstract

The present application discloses a single-point calculation method, system and inversion method for single-point sampling of microbial population analysis, the method comprises: controlling a sampling device to sample multiple target intervals, based on a preset or instantaneous trigger interval time Tc After replacing the old water in the pipeline, multiple parallel samples are collected; the microbial species and population density at each point are calculated. After uncertainty assessment of the individual points, the microbial species and population density data for all sampling points within the target interval are integrated to generate the microbial species set and population density for that target interval. This model overcomes the boundary effect limitations of traditional laminar flow theory, accurately calculating the trigger interval time Tc and completely eliminating sample cross-contamination caused by retained old water particles. When parallel samples deviate, arithmetic averaging is used to maintain linearity for low-fluctuation data; in high-fluctuation scenarios, the calculation mode switches to root mean square (RMS) mode to effectively suppress abnormal noise interference.

Description

Technical Field

[0001] The present application relates to the technical field of microbial population analysis, and in particular to a single-point calculation method, system and inversion method for single-point sampling of microbial population analysis. Background Art

[0002] Traditional methods for analyzing microbial populations in wastewater treatment rely on manual sampling and laboratory testing, which presents systemic technical flaws. In particular, peristaltic pumps can experience severe boundary retention issues due to laminar flow when extracting samples from pipelines. This can lead to residual old water contaminating subsequent samples during actual sampling, resulting in errors, especially at low flow rates. Furthermore, existing technologies lack responsiveness to data volatility. For example, in scenarios with high deviations, the arithmetic mean is still used, resulting in excessive amplification of outliers.

[0003] Therefore, after the sample is distorted due to pipeline residue, the distorted data is further amplified by improper averaging calculation, which ultimately makes the activated sludge process control lose its scientific basis. Summary of the Invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art. One purpose of the present application is to propose a single-point calculation method, system and inversion method for single-point sampling of microbial population analysis, which can construct a collaborative computing framework that integrates fluid dynamics, statistical decision-making and uncertainty transfer, and solve the problems of sampling contamination and calculation distortion from the root.

[0005] In the first aspect, a single-point calculation method for single-point sampling of microbial population analysis is disclosed, comprising: controlling a sampling device to sample multiple target intervals, based on a preset or instantaneous trigger interval time. Tc After completing the replacement of the old water in the pipeline, multiple parallel samples are collected; the single-point microbial species and population density are calculated, including: calculating the arithmetic mean and standard deviation based on the multiple parallel samples; calculating the deviation based on the ratio of the standard deviation to the arithmetic mean, if the deviation is less than a preset deviation threshold, the arithmetic mean is used as the single-point mean, otherwise the root mean square value is used as the single-point mean; after performing uncertainty assessment on the single point, the microbial species and population density data of all sampling points in the target interval are integrated to generate the microbial species set and population density of the target interval.

[0006] The effect is this: by establishing a laminar flow-diffusion dual-mechanism replacement model, the effect of turbulent diffusion on water molecules on the tube wall is quantified. This model breaks through the boundary effect limitations of traditional laminar flow theory, accurately calculating the trigger interval time Tc and completely eliminating sample cross-contamination caused by the retention of old water particles. In addition, based on the dynamic judgment of the deviation threshold, differentiated processing is implemented for the intrinsic fluctuation characteristics of microbial data. When parallel samples deviate, arithmetic averaging is used to maintain the linear characteristics of low-fluctuation data; in high-fluctuation scenarios, the calculation mode switches to root mean square calculation mode to effectively suppress abnormal noise interference.

[0007] In addition, the sample expansion error is constrained by scaling uncertainty, and the impact of spatial heterogeneity is quantified by introducing water quality deviation uncertainty, providing a quantifiable reliability boundary for process control.

[0008] Further, the trigger interval time is calculated Tc include:

[0009] ;

[0010] Where, is the permutation delay coefficient, , The actual project old water replacement time:

[0011] ;

[0012] Where, R is the pipe radius R, L is the pipe length, Q For traffic, is the empirical coefficient of weak turbulence.

[0013] Furthermore, the deviation threshold is 10%, and the deviation is:

[0014] ;

[0015] Where, is the standard deviation of multiple parallel samples, It is the arithmetic mean of multiple parallel samples.

[0016] Furthermore, the microbial species set is obtained by merging the microbial species of all sampling points within the target interval.

[0017] Furthermore, the target interval includes a zero interval, and integrating the zero interval includes: merging all microbial species in the target interval to generate a set of microbial species in the zero interval; the population density of the zero interval is:

[0018]

[0019] In the formula, the total number of sampling points is The total types of microorganisms are , the corresponding microbial population density is .

[0020] Furthermore, the quantified single-point measurement uncertainty also includes the uncertainty caused by scaling:

[0021] ;

[0022] Where, Target range No. Uncertainty caused by scaling for microorganisms;

[0023] And the uncertainty due to sampling:

[0024] ;

[0025] ;

[0026] ;

[0027] Where, for The relative uncertainty of the interval deviation score is for Interval deviation score deviation standard deviation, Score Deviation , Target range The arithmetic mean of the deviation scores of is the intermediate variable, for The number of detection points in the interval.

[0028] Furthermore, the deviation score is: ;

[0029] in, : intermediate variable, The intermediate variable is Parameter criticality score at ;

[0030] ;

[0031] : parameter criticality score; : Process importance; : parameter sensitivity; : Impact of microbial activity;

[0032] in, , α, β, and γ are all coefficients;

[0033] The single-point deviation score is defined as:

[0034] ;

[0035] Where, : Single point deviation score, : The deviation corresponding to a single parameter of a single point, the subscript is the spatial point of the x, y, and z axes in the Cartesian coordinate system , based on the single point deviation score of the determined sampling point , score the deviation of each single point Add to collection and serialize the coordinates:

[0036] , mentioned above That is the set The i-th element in .

[0037] In the second aspect, the present application discloses an inversion method, which is based on the single-point calculation method of single-point sampling for microbial population analysis, comprising: obtaining a set of microbial species in the sampling pool by a union operation. :

[0038] ;

[0039] Calculate the number of microbial species in the sampling pool :

[0040] ;

[0041] Where, is the process variable, .

[0042] Furthermore, the uncertainty of the number of microbial species is:

[0043] ;

[0044] Where, For sampling pool Uncertainty in the number of microbial species;

[0045] Sampling pool The uncertainty of the number of microbial species and the expanded uncertainty are:

[0046] ;

[0047] Where, For sampling pool Expanded uncertainty of the number of microbial species, is the confidence level;

[0048] The uncertainty of the total number of microbial species in the sampling pool is:

[0049] ;

[0050] Where, is the process variable, , is the total microbiological uncertainty;

[0051] The total uncertainty is expressed as:

[0052] , where U is the expanded uncertainty of the total number of microbial species in the sampling pool.

[0053] In a third aspect, a single-point computing system for single-point sampling of microbial population analysis is disclosed, comprising a processor and a memory; the memory stores a computer program, and when the program is executed by the processor, the single-point computing method according to any one of claims 1 to 6 is implemented.

[0054] 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

[0055] 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:

[0056] Figure 1 is a schematic flow chart of a single-point calculation method for single-point sampling for microbial population analysis according to some embodiments of the present application;

[0057] Figure 2 is a schematic flow chart of an inversion method according to some embodiments of the present application;

[0058] Figure 3 Schematic diagram of a single-point computing system for single-point sampling of microbial population analysis according to some embodiments of the present application. DETAILED DESCRIPTION

[0059] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0060] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[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 single-point calculation method, system and inversion method for single-point sampling of microbial population analysis according to the embodiments of the present application.

[0063] According to an embodiment of the present application, a single-point calculation method for single-point sampling of microbial population analysis includes: controlling a sampling device to sample multiple target intervals, based on a preset or instantaneous trigger interval time. Tc After completing the replacement of old water in the pipeline, collect multiple parallel samples.

[0064] Therefore, by establishing a laminar flow-diffusion dual-mechanism displacement model, the effect of turbulent diffusion on water molecules on the tube wall was quantified. This model breaks through the boundary effect limitations of traditional laminar flow theory, accurately calculating the trigger interval time Tc and completely eliminating sample cross-contamination caused by the retention of old water particles.

[0065] Calculating the single-point microbial species and population density includes: calculating the arithmetic mean and standard deviation based on multiple parallel samples; calculating the deviation based on the ratio of the standard deviation to the arithmetic mean, if the deviation is less than a preset deviation threshold, using the arithmetic mean as the single-point mean, otherwise using the root mean square value as the single-point mean; after performing uncertainty assessment on the single point, integrating the microbial species and population density data of all sampling points in the target interval to generate the microbial species set and population density of the target interval.

[0066] In this way, based on the dynamic judgment of the deviation threshold, differentiated processing is implemented for the intrinsic fluctuation characteristics of microbial data. When parallel samples deviate, arithmetic averaging is used to maintain the linear characteristics of low-fluctuation data; in high-fluctuation scenarios, the root mean square calculation mode is switched to effectively suppress abnormal noise interference. In addition, by scaling uncertainty to constrain sample expansion errors and introducing water quality deviation uncertainty to quantify the impact of spatial heterogeneity, a quantifiable reliability boundary is provided for process control.

[0067] Specifically, in some embodiments, a single-point calculation method for single-point sampling for microbial population analysis includes the following steps:

[0068] S1. The sampling device moves along a preset path to the target sampling point calculated by the model. After the sampling pump is activated, the new water completely replaces the old water after a predetermined trigger interval Tc, leaving no old water remaining in the pipeline. For example, the sampling device can collect six parallel samples at each sampling point, but it can also collect samples in one go, with engineers performing sample splitting.

[0069] Next, how to calculate the trigger interval time Tc in some embodiments is described in detail.

[0070] Before explaining, it should be understood that, due to the actual water exchange working conditions where the flow velocity of the fluid at each point in the pipeline is uneven, based on the laminar flow theory calculation, the old water particles at the pipe wall can never reach the outlet. Therefore, due to the limitations of the laminar flow theory, the accurate trigger interval time Tc cannot be obtained. Based on the calculation method commonly used in the prior art, the new water will not completely replace the old water each time a sample is taken.

[0071] To this end, in this embodiment, a micro-motion model is established to calculate the theoretical replacement time, and then the aforementioned trigger interval time Tc is obtained.

[0072] Specifically, based on the actual working condition of uneven flow velocity at each point in the pipeline, this embodiment establishes a micro-motion model to calculate the theoretical replacement time. Consider a circular cross-section pipeline (radius R ,length L ), the velocity distribution under laminar flow conditions follows the model:

[0073] ;

[0074] in, is the maximum flow velocity at the center of the tube, r is the radial distance. r At, take the thickness dr The volume of the annular fluid element is:

[0075] ;

[0076] The theoretical displacement time of the infinitesimal element moving along the pipe is determined by the flow rate v ( r ) Decide:

[0077] ;

[0078] Moreover, the flow Q With average flow rate satisfy , under laminar flow conditions, the average flow velocity is half of the maximum flow velocity, that is: Where, is the flow velocity at the center of the pipe, and , is the wall velocity, and , t is the diffusion time.

[0079] Theoretical analysis shows that when r approaches R (at the tube wall), , that is, the old water particles on the pipe wall can never be discharged under laminar flow theory. To better illustrate this phenomenon, we quantify it here. As follows, we establish the old water volume fraction function:

[0080] ;

[0081] when Time limit analysis proves that: ;

[0082] In the above formula, is the old water flow rate. Limit analysis shows that it is impossible to achieve replacement of particles at the pipe wall even if the particles are theoretically infinitely long. Therefore, in order to complete the replacement in a finite time, the molecular diffusion effect (Einstein's diffusion law) is introduced:

[0083] ;

[0084] Where D is the diffusion coefficient of water molecules (Einstein) calculated by experiment or empirical value; usually empirical value , is the Boltzmann constant, T is the absolute temperature, is the viscosity coefficient of water, r is the equivalent particle size of water molecules. Model the diffusion from the edge to the center:

[0085] ;

[0086] Where, is the statistical average value. Then the diffusion time from the wall to the center is:

[0087] ;

[0088] Where, t d is the diffusion time from the tube wall to the center.

[0089] Center flow maximum velocity movement time:

[0090] ;

[0091] Where, The engineering replacement time model for the final theoretical 100% old water replacement time is:

[0092] ;

[0093] in, is the theoretical maximum time. However, in actual engineering, turbulence will add diffusion, so the diffusion coefficient needs to be corrected for turbulence:

[0094] ;

[0095] Where, D d is the pipe diffusion coefficient, D t is the turbulent diffusion coefficient in the pipe, , It is the empirical coefficient of weak turbulence, usually taken as 0.01.

[0096] Therefore, the actual project old water replacement time for:

[0097] ;

[0098] And, because , so it can be ignored in time engineering calculations D , which is simplified to:

[0099] ;

[0100] Then the trigger interval time Tc of pipeline sampling is:

[0101] ;

[0102] Where, is the permutation delay coefficient, ,default =1.01.

[0103] For example, the pipe radius R =0.01m; length L =1m;Flow Q =0.0001m 3 The following describes the case of / s as an example.

[0104] The average flow rate is ;

[0105] Calculated ;

[0106] The diffusion time is: ;

[0107] The convection time is: ;

[0108] The total replacement time can be obtained: , trigger time: .

[0109] S2. Calculate the microbial species and corresponding microbial population density of a single sample, and calculate the single point value.

[0110] Specifically, the calculation can be performed based on the content disclosed in the applicant's prior patent (application number 2025107344728). Of course, other calculation methods in the prior art can also be used to obtain the single-point microbial species of a sample. , and the corresponding microbial population density , the uncertainty of a single measurement of the instrument is .

[0111] For example, the pool to be sampled can be divided into several partitions, and then six parallel samples are taken from each partition. The group density can be expressed as:

[0112] ;

[0113] in, is the number of samples in each sampling area, Indicates the target intervals, Is a positive integer.

[0114] It is worth noting that in data processing, arithmetic mean is a common method to reflect the trend of a data set. However, it has obvious limitations, namely, it cannot reflect the degree of discreteness of the data, may mask the influence of measurement errors or outliers, and is overly sensitive to large values. Therefore, when characterizing volatility or energy-related indicators, it is usually more appropriate to use the root mean square value. The root mean square can more effectively capture the dynamic changes of the data and can suppress certain types of background noise to a certain extent (such as monitoring noise caused by microbial movement). In view of the above background, especially in order to effectively deal with scenarios with similar noise interference and more accurately reflect the fluctuation characteristics, the embodiment of the present application proposes the following calculation method:

[0115] Calculate the single point mean:

[0116] ;

[0117] Where, For the The sampling points corresponding to the target interval No. The arithmetic mean of the parallel samples of microorganisms, i is an intermediate variable.

[0118] Then, calculate the standard deviation of a single point:

[0119] ;

[0120] The subscripts in this formula have the same meaning as above. is the standard deviation of the six parallel samples of the aforementioned single point, It is the arithmetic mean of multiple parallel samples.

[0121] Based on the above results, is the deviation threshold, is the deviation, the subscript kind represents a certain type of microorganism. When calculating the single point mean, if the deviation is less than the deviation threshold, that is , then the single point mean is: ;

[0122] If the deviation is not less than the deviation threshold, , then the single point mean is .

[0123] In other words, the single point mean is as follows: ;

[0124] Where, the deviation threshold The default threshold is set by engineers based on actual conditions and is 10%. .

[0125] S3. Evaluate the uncertainty of a single point. First, quantify the uncertainty component introduced by the instrument measurement. The target interval The sampling point For microorganisms, the uncertainty of the six parallel samples caused by the instrument itself can be expressed as:

[0126] ;

[0127] Among them, the Class A uncertainty caused by each single parallel sample in the six parallel samples is: .

[0128] According to the uncertainty propagation theory, The target interval The sampling point The uncertainty of microbial-like properties is:

[0129] ;

[0130] Identifies the uncertainty caused by instrument measurement for the i-th replicate; where i is the intermediate variable.

[0131] S4. Integrate the microbial data of all sampling points within the target interval and define the target interval The number of sampling points is , then the set of microbial species in the target interval Obtained through union operation:

[0132] ;

[0133] The uniqueness of microbial species is ensured based on the union operation.

[0134] And calculate the corresponding microbial population density :

[0135] ;

[0136] is the total sampling rate, the default is 5%, Departure interval The i-th sampling point Microbial-like values; .

[0137] S5. Evaluate the uncertainty of a single target interval, considering two types of errors:

[0138] The first type is uncertainty caused by scaling. According to uncertainty propagation theory, when a statistic (such as the sum) is linearly scaled, its uncertainty (standard deviation) will also be scaled by the same proportion:

[0139] ;

[0140] Where, Target range No. Uncertainty due to scaling for microorganisms.

[0141] prove:

[0142] Assume that the original statistic is (e.g. the sum of 5 samples), the uncertainty is , then when the statistic is scaled to When the new uncertainty is .

[0143] 1. Assume that the variance of each sample is Extraction The sum of samples , whose variance is: ;

[0144] The uncertainty is: ;

[0145] : uncertainty after scaling;

[0146] : scaled variance;

[0147] : standard deviation;

[0148] 2. When the sum is scaled When , the new variance is:

[0149] ;

[0150] The new uncertainty (standard deviation) is therefore: ;

[0151] : uncertainty after scaling;

[0152] : Scaled variance.

[0153] : Variance function, variance is an important indicator in statistics used to measure the degree of deviation between a set of data and its mean. The details are as follows:

[0154] Where n is the number of data points in the sample, is the i-th data point, is the sample mean.

[0155] The second category is the uncertainty caused by sampling, which is obtained by the relative uncertainty of water quality score and target interval sampling in the following way:

[0156] ;

[0157] ;

[0158] ;

[0159] Where, for The relative uncertainty of the interval deviation score is for Interval deviation score deviation standard deviation, Score Deviation , Target range The arithmetic mean of the deviation scores of is the intermediate variable, for The number of detection points in the interval.

[0160] In some examples, the single-point deviation score is: ;

[0161] in, : intermediate variable, The intermediate variable is Parameter criticality score at ;

[0162] ;

[0163] : parameter criticality score; : Process importance; : parameter sensitivity; : Impact of microbial activity;

[0164] in, , α, β, and γ are all coefficients;

[0165] The single-point deviation score is defined as: ;

[0166] Where, : Single point deviation score, : The deviation corresponding to a single parameter of a single point, the subscript is the spatial point of the x, y, and z axes in the Cartesian coordinate system Based on the single point deviation score of the determined sampling point , score the deviation of each single point Add to collection and serialize the coordinates: , mentioned above That is the set The i-th element in . Filter the maximum and minimum values , ;

[0167] Set the number of symmetrical deviation intervals: n, then the segmentation score value of the deviation interval point is:

[0168] ;

[0169] Where, : The segmentation score value of the deviation interval point; : intermediate variable, ;

[0170] ;

[0171] : Number of non-zero value intervals;

[0172] Then count the number of zero values ​​in each scoring interval:

[0173] ;

[0174] Then get the Results for rating intervals:

[0175] ;

[0176] 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;

[0177] ;

[0178] : No. The first interval The three-dimensional spatial position of the point, ;

[0179] Thus, the number of samples in each interval is obtained: ;

[0180] : The number of samples in each sampling area; : Total sampling rate.

[0181] Then, calculate the standard deviation of the deviation score from the target interval : ;

[0182] According to the above content, the target interval is obtained Relative uncertainty of deviation score : .

[0183] Thus, the uncertainty caused by sampling deviation is obtained:

[0184] ,in, Target range l No. Uncertainty caused by deviation score sampling for microorganisms.

[0185] Finally, the target interval is obtained according to the uncertain propagation theory No. Uncertainty of microorganisms : ;

[0186] In some embodiments, a target interval is defined as a zero interval, and the total number of sampling points is , the total types of microorganisms are And the corresponding microbial population density is ;

[0187] There is a relationship: , based on the union operation, to ensure that only one of the same type is selected.

[0188] Then the microbial population density in the zero interval is: .

[0189] Similarly, according to uncertainty propagation theory, when a statistic (such as the sum) is linearly scaled, its uncertainty (standard deviation) will also be scaled by the same proportion:

[0190] , is the first Uncertainty of microorganisms.

[0191] An inversion method is implemented based on the single point calculation results obtained in the above embodiment, including: counting the types of microorganisms in the sampling pool :

[0192] ;

[0193] nl is the number of non-zero intervals.

[0194] The number of microbial species in the sampling pool for: ;

[0195] is the process variable, .

[0196] Based on the uncertainty of the number of microbial species introduced in the above examples: ;

[0197] Where, For sampling pool Uncertainty in the number of microbial species.

[0198] Sampling pool The uncertainty of the number of microbial species and the expanded uncertainty are: ;

[0199] Where, For sampling pool Expanded uncertainty of the number of microbial species, For confidence, for example, , the confidence level is 95%.

[0200] The uncertainty of the total number of microbial species in the sampling pool is: ;

[0201] Where, is the process variable, , is the total microbiological uncertainty.

[0202] The total uncertainty is expressed as:

[0203] , where U is the expanded uncertainty of the total number of microbial species in the sampling pool. Based on the above uncertainty, the results are corrected.

[0204] Finally, after the pool-level inversion, each sample is automatically assigned a unique sample ID after being placed in a reagent bottle. Traceability information is then written onto the bottle label using radio frequency identification (RFID) technology. This traceability information includes: a unique sample ID; the sampling time (UTC timestamp); real-time water quality parameters (pH, temperature, conductivity, dissolved oxygen, UVCOD, turbidity); geographic coordinates (WGS-84 latitude and longitude) and process location identifiers; the sampling equipment serial number and operating parameters. The labeled samples are then transferred to a constant temperature refrigeration unit (set temperature: 4±2°C).

[0205] A single-point computing system for single-point sampling of microbial population analysis includes: a processor, such as a CPU, for executing computing instructions for microbial population analysis; a communication bus for realizing connection and communication between various components; a user interface, including a display screen and an input unit, optionally supporting wired / wireless interfaces; a network interface, such as a Wi-Fi interface, supporting data communication with a background server; and a memory, such as a high-speed RAM or disk memory, which is independent of the processor configuration.

[0206] The memory stores a computer program for implementing the single-point calculation method and inversion method for single-point sampling of microbial population analysis in the above embodiment; exemplarily, it stores a microbial population single-point sampling calculation program, which at least includes: a trigger interval time calculation module, based on the pipeline parameters (radius R ,length L ,flow Q ) and turbulence correction model to calculate the replacement time Tc ; Single point microbial density calculation module, performs mean / standard deviation statistics and deviation threshold judgment of parallel samples; Uncertainty assessment module, quantifies instrument measurement error and sampling error; Target interval data integration module, combines the microbial species at the sampling point and calculates the population density; When the above processor calls the program, it executes: sampling position control, Tc Trigger timing, microbiological data analysis and uncertainty output.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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 is 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 single-point calculation method for single-point sampling of microbial population analysis, characterized in that: include: Control sampling device to sample multiple target intervals based on preset or instantaneous trigger intervals Tc After completing the replacement of old water in the pipeline, collect multiple parallel samples; Wherein, the trigger interval time is calculated Tc ,include: ； Where, is the permutation delay coefficient, , The actual project old water replacement time: ； Where, R is the pipe radius R, L is the pipe length, Q For traffic, is the empirical coefficient of weak turbulence; Calculate single-point microbial species and population density, including: Calculating the arithmetic mean and standard deviation based on the plurality of parallel samples; calculating the deviation based on the ratio of the standard deviation to the arithmetic mean, and if the deviation is less than a preset deviation threshold, using the arithmetic mean as the single-point mean; otherwise, using the root mean square value as the single-point mean; Among them, the deviation threshold 10%; The deviation is: ； Where, is the standard deviation of multiple parallel samples, is the arithmetic mean of multiple parallel samples; After uncertainty assessment of a single point, the microbial species and population density data of all sampling points within the target interval are integrated to generate a microbial species set and population density for the target interval; Among them, quantifying the single-point measurement uncertainty also includes: Uncertainty due to scaling: ； Where, Target range l No. Uncertainty caused by scaling for microorganisms; And the uncertainty due to sampling: ； ； ； Where, for The relative uncertainty of the interval deviation score is for Interval deviation score deviation standard deviation, Score Deviation , Target range The arithmetic mean of the deviation scores of is the intermediate variable, for The number of detection points in the interval; The deviation score is: ； in, : intermediate variable, The intermediate variable is Parameter criticality score at ; : parameter criticality score; : Process importance; : parameter sensitivity; : Impact of microbial activity; in, , α, β, and γ are all coefficients; The single-point deviation score is defined as: ； Where, : Single point deviation score, : The deviation corresponding to a single parameter of a single point, the subscript is the spatial point of the x, y, and z axes in the Cartesian coordinate system , based on the single point deviation score of the determined sampling point , score the deviation of each single point Add to collection and serialize the coordinates: , mentioned above That is the set The i-th element in .

2. The single-point calculation method for single-point sampling of microbial population analysis according to claim 1, characterized in that: The microbial species set is obtained by merging the microbial species of all sampling points within the target interval.

3. The single-point calculation method for single-point sampling of microbial population analysis according to claim 1, characterized in that: The target interval includes a zero interval, and integrating the zero interval includes: Merging the union of all microbial species in the target interval to generate the microbial species set in the zero interval; The population density of the zero interval is: ； In the formula, the total number of sampling points is The total types of microorganisms are , the corresponding microbial population density is .

4. An inversion method, based on the single-point calculation method for single-point sampling of microbial population analysis according to any one of claims 1 to 3, characterized in that: Obtain the set of microbial species in the sampling pool through union operation : ； Calculate the microbial population density in the sampling pool : ； Where, is the number of non-zero intervals, is the process variable, .

5. The inversion method according to claim 4, characterized in that: The uncertainty of the number of microbial species is: ； Where, For sampling pool Uncertainty in the number of microbial species, is the general category of microorganisms; Sampling pool The uncertainty of the number of microbial species and the expanded uncertainty are: ； Where, For sampling pool Expanded uncertainty of the number of microbial species, is the confidence level; The uncertainty of the total number of microbial species in the sampling pool is: ； Where, is the process variable, , is the total microbiological uncertainty; The total uncertainty is expressed as: , where U is the expanded uncertainty of the total number of microbial species in the sampling pool.

6. A single-point calculation system for single-point sampling of microbial population analysis, characterized in that: including processor and memory; The memory stores a computer program, and when the program is executed by the processor, the single-point calculation method according to any one of claims 1 to 3 is implemented.

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