Microbial pollution evaluation method and system based on multiple index parameters

Abstract

The invention discloses a microbial pollution evaluation method and system based on multiple index parameters, and relates to the technical field of microbial pollution detection and evaluation. The evaluation method comprises the following steps: acquiring index parameters of microbial pollution evaluation; performing standardization processing on the index parameters to obtain standardized data; establishing a hierarchical structure model comprising a target layer and a criterion layer; according to the influence of the index parameters on the microbial pollution degree, assigning the relative importance among the index parameters to obtain a relative importance score; constructing a judgment matrix by using the relative importance score, calculating the contribution weight of each index parameter to the microbial pollution degree through eigenvalue decomposition, and recording the contribution weight as an analytic hierarchy process weight; calculating a comprehensive score of the microbial pollution degree according to the standardized data and the analytic hierarchy process weight; carrying out risk assessment on the microbial pollution degree according to the comprehensive score, and determining a risk level; the comprehensive evaluation of the microbial pollution problem of the electrical equipment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of microbial pollution detection and evaluation, and particularly to a method and system for evaluating microbial pollution based on multi-index parameters. Background Art

[0002] With the continuous development of electrical equipment and the expansion of the power grid scale, the use of external insulation materials is increasing day by day, and the impact of microbial pollution problems in the operation of power equipment has also been paid more and more attention; the external insulation surface of electrical equipment is exposed to the environment for a long time and is extremely vulnerable to microbial invasion. Especially under environmental conditions such as humidity, high temperature or severe pollution, the growth and reproduction of microorganisms are particularly rapid; microbial pollution may not only affect the insulation performance of equipment, but also lead to the aging and corrosion of equipment surface materials, and even cause major accidents such as electrical failures and fires in extreme cases, posing a serious threat to the safety and stability of the power system.

[0003] However, despite the increasing importance of microbial pollution to power equipment, there are still relatively large technical gaps in the current evaluation methods for microbial pollution. The existing methods usually only focus on a single microbial species or quantity index, lacking a multi-dimensional and comprehensive evaluation of microbial pollution; moreover, the existing evaluation methods are difficult to handle the non-linear relationships between different parameters and cannot comprehensively consider various complex factors such as environmental factors, equipment usage status, and microbial growth activity, resulting in relatively one-sided evaluation results and lacking sufficient scientificity and reliability. Summary of the Invention

[0004] The present invention provides a method and system for evaluating microbial pollution based on multi-index parameters, aiming to comprehensively evaluate the microbial pollution problems of power equipment.

[0005] The method for evaluating microbial pollution based on multi-index parameters provided by the present invention includes the following steps:

[0006] S1, obtaining the numerical values of multiple index parameters for microbial pollution evaluation;

[0007] S2, performing standardization processing on the numerical values of the index parameters to obtain standardized data;

[0008] S3, establishing a hierarchical structure model; the hierarchical structure model includes a target layer and a criterion layer, the criterion layer includes the index parameters, and the target layer is set as the evaluation of the degree of microbial pollution;

[0009] According to the influence of the index parameters on the degree of microbial pollution, assign relative importance to the relative importance between the index parameters to obtain a relative importance score; use the relative importance score to construct a judgment matrix, and calculate the contribution weight of each index parameter to the degree of microbial pollution through eigenvalue decomposition, denoted as the analytic hierarchy process weight;

[0010] S4. Calculate the comprehensive score of the microbial contamination degree according to the standardized data and the analytic hierarchy process weights; and conduct a risk assessment on the microbial contamination degree based on the comprehensive score to determine the risk level.

[0011] Optionally, before or after the step S3, it further includes the step of obtaining the contribution score of each index parameter to the microbial contamination degree by the expert scoring method, denoted as the expert score.

[0012] Before the step S4, it further includes the step of performing a weighted average on the expert score and the analytic hierarchy process weights corresponding to the index parameters to obtain the comprehensive weight of each index parameter.

[0013] The step S4 includes: inputting the standardized data and the comprehensive weights corresponding to all index parameters into a scoring algorithm, and calculating the comprehensive score of the microbial contamination degree by weighted summation.

[0014] Optionally, in the step S3, the hierarchical structure model further includes a sub-criterion layer, and the sub-criterion layer includes at least one index parameter; the index parameters of the sub-criterion layer are influencing factors of at least one index parameter of the criterion layer.

[0015] Optionally, the sub-criterion layer includes multiple index parameters, and the multiple index parameters of the sub-criterion layer are influencing factors of at least one index parameter of the criterion layer. The step S3 further includes:

[0016] Assign values to the relative importance among the multiple index parameters of the sub-criterion layer to obtain the relative importance score, construct a judgment matrix using the relative importance score, calculate the contribution weights of each index parameter of the sub-criterion layer to the corresponding index parameter of the criterion layer through eigenvalue decomposition, and calculate the contribution weights of each index parameter of the sub-criterion layer to the microbial contamination degree according to the contribution weights of the corresponding index parameter of the criterion layer to the microbial contamination degree.

[0017] Optionally, the index parameters of the sub-criterion layer are influencing factors of multiple index parameters of the criterion layer. The step S3 further includes:

[0018] Assign values to the relative importance of the index parameters of the sub-criterion layer to different index parameters of the criterion layer to obtain the relative importance score, construct a judgment matrix using the relative importance score, calculate the contribution weights of the index parameters of the sub-criterion layer to different index parameters of the criterion layer through eigenvalue decomposition, and calculate the contribution weights of the index parameters of the sub-criterion layer to the microbial contamination degree according to the contribution weights of different index parameters of the criterion layer to the microbial contamination degree.

[0019] Optionally, the step S1 specifically includes:

[0020] S11, collect the microbial contamination samples and environmental parameters on the surface of the device;

[0021] S12, detect the contamination samples to obtain sample parameters; the index parameters include the sample parameters and the environmental parameters;

[0022] The index parameters include multiple ones among microbial quantity density, microbial species distribution, metabolite species, metabolite concentration, sample pH value, corrosive substance concentration, surface conductivity, adhesion strength, temperature, humidity, atmospheric pressure.

[0023] Optionally, in the step S2, the standardization processing method is at least one of the minimum-maximum calibration method, Z-score standardization method, and decimal calibration method.

[0024] A microbial contamination evaluation system based on multiple index parameters proposed by the present invention includes:

[0025] A sampling module for collecting the microbial contamination samples and environmental parameters on the surface of the device;

[0026] A detection module for detecting the microbial contamination samples to obtain sample parameters;

[0027] A data standardization module for standardizing the values of the index parameters to obtain standardized data; the index parameters include the environmental parameters and the sample parameters;

[0028] A hierarchical structure model establishment module for establishing a hierarchical structure model including a target layer and a criterion layer, the criterion layer includes the index parameters, and the target layer is set as the evaluation of microbial contamination degree;

[0029] An input module for assigning relative importance to the relative importance among the index parameters according to the influence of the index parameters on the microbial contamination degree to obtain a relative importance score;

[0030] A weight assignment module for constructing a judgment matrix using the relative importance score, calculating the contribution weight of each index parameter to the microbial contamination degree through eigenvalue decomposition, and recording it as the analytic hierarchy process weight;

[0031] A comprehensive score module for calculating the comprehensive score of the microbial contamination degree according to the standardized data and the analytic hierarchy process weight;

[0032] A risk level confirmation module for performing risk assessment on the microbial contamination degree according to the comprehensive score to determine the risk level.

[0033] Optionally, the input module is further configured to input the contribution score of each index parameter to the microbial contamination degree, denoted as the expert score;

[0034] The weight allocation module is further configured to perform weighted averaging on the expert scores and the analytic hierarchy process weights corresponding to all index parameters to obtain the comprehensive weight of each index parameter;

[0035] The comprehensive scoring module is configured to input the standardized data and the comprehensive weights corresponding to all index parameters into a scoring algorithm, and calculate the comprehensive score of the degree of microbial contamination through weighted summation.

[0036] Optionally, the hierarchical structure model further includes a sub-criterion layer, the sub-criterion layer includes at least one index parameter, and the index parameters of the sub-criterion layer are influencing factors of at least one index parameter of the criterion layer.

[0037] From the above technical solutions, it can be seen that the present invention has the following beneficial effects:

[0038] The method for evaluating microbial contamination based on multiple index parameters proposed by the present invention standardizes the detection data of different physical dimensions, enabling the data between different indexes to be compared with each other, providing a reliable data basis for comprehensive scoring; assigns relative importance to the index parameters, and scientifically and reasonably determines the contribution weight of each index parameter in the degree of microbial contamination through the analytic hierarchy process, providing an objective calculation basis for comprehensive scoring. Combining the standardized index parameters and the corresponding contribution weights to the degree of microbial contamination, the comprehensive score of the degree of microbial contamination can be obtained through the weighted summation algorithm; this evaluation method fully considers the non-linear relationship between different parameters and the influence of various complex factors on the degree of microbial contamination of power equipment, realizing a comprehensive and comprehensive evaluation of the microbial contamination problem of power equipment; according to the obtained comprehensive score, different risk levels can be divided, providing a clear decision-making basis for equipment maintenance, helping to take corresponding prevention and control measures in a timely manner, and ensuring the safe operation of power equipment. Description of the Drawings

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0040] Figure 1 It is a step block diagram of some embodiments of the method for evaluating microbial contamination based on multiple index parameters of the present invention;

[0041] Figure 2 It is a structure and implementation process block diagram of some embodiments of the system for the method for evaluating microbial contamination based on multiple index parameters of the present invention;

[0042] Figure 3 Shows the sampling object of the sampling module in the embodiment of the present invention;

[0043] Figure 4 Shows the detection object of the detection module in the embodiment of the present invention and the corresponding detection means;

[0044] Figure 5 Is the implementation logic block diagram of some embodiments of the microbial contamination evaluation method based on multi-index parameters of the present invention. Specific implementation manners

[0045] In order to make the invention purpose, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

[0046] The terms "including" and "having" in the specification of this application and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0047] Refer to Figure 1 , the microbial contamination evaluation method based on multi-index parameters proposed in the embodiment of the present invention includes the following steps:

[0048] S1. Obtain multiple index parameters for microbial contamination evaluation.

[0049] Specifically, it includes: S11. Collect microbial contamination samples on the surface of the device and environmental parameters; S12. Detect the contamination samples to obtain sample parameters; the index parameters adopted in the embodiment of the present invention include sample parameters and environmental parameters where the sample is included or located.

[0050] In some embodiments, the sampling objects in the embodiments of the present invention may include the following four types:

[0051] Microbial populations, collecting microorganisms such as bacteria, fungi, and algae attached to the insulating surface;

[0052] Metabolites, metabolites produced during the growth of microorganisms, such as organic acids, lipids, proteins, etc. These metabolites can reflect the metabolic activity of microorganisms and the severity of contamination;

[0053] Adhesions and particles, including biofilms formed by microbial aggregation, adherent particles, and other pollution-related impurities;

[0054] Environmental parameters, i.e., environmental data in which the sample is contained and located, such as atmospheric pressure, surface temperature and humidity, conductivity of pollutants, etc.

[0055] After sampling is completed, the sample is quickly transferred to the detection module for analysis, and the following indicator parameters are detected: microbial quantity density and species distribution, types and concentrations of metabolites, pH value and concentration of corrosive substances of the sample, surface conductivity, and adhesion strength.

[0056] Specifically, for the microbial quantity density and species distribution, fluorescence microscopy imaging technology can be used for detection. The DNA fragments of microorganisms are amplified by polymerase chain reaction (PCR) technology, and the microbial species are identified through specific primers to generate the microbial quantity data per unit area and the species distribution map.

[0057] For the types and concentrations of metabolites, liquid chromatography-mass spectrometry (LC-MS) and enzyme-linked immunosorbent assay (ELISA) can be used for detection. The sample liquid is separated into metabolite components through a high-performance liquid chromatography module and then connected to a mass spectrometry module for molecular weight analysis to identify the types and contents of metabolites. At the same time, for specific metabolites (such as specific proteins or organic acids), quantitative detection is carried out through ELISA technology to generate a detailed ingredient list and concentration data of metabolites.

[0058] For the pH value and concentration of corrosive substances of the sample, pH electrode method and colorimetric analysis can be used for detection. The pH value of the sample solution is directly measured using a high-precision pH electrode, and the concentration of organic acids and other corrosive substances is detected by colorimetry. Specifically, the result is obtained through colorimetric analysis after the sample reacts with a chemical chromogenic agent, generating the chemical property data of the sample, including the pH value and organic acid concentration.

[0059] For surface conductivity and adhesion strength, a four-probe conductivity tester and atomic force microscope (AFM) can be used for detection. The four-probe method is used to measure the conductivity of pollutants on an insulating surface, and the adhesion strength of microorganisms and their damage to the surface material are analyzed through AFM to generate data such as the conductivity of pollutants and the adhesion strength of microorganisms.

[0060] For the detection of environmental parameters, an integrated temperature and humidity sensor and atmospheric pressure sensor can be used for detection to monitor the temperature, humidity, and air pressure data of the sampling area in real time and compare them with the suitable environmental parameters for microbial growth to generate on-site environmental data.

[0061] S2, Standardize the indicator parameters to obtain standardized data.

[0062] Since the selected index parameters involve different physical dimensions, the min-max normalization method, Z-score normalization method, and decimal scaling method can be used to normalize these data, enabling the comparison of data between different indicators and eliminating the impact of dimensions on the evaluation results.

[0063] Among them, min-max normalization (Min-Max Scaling) converts each data point into a value in the range [0, 1] through the following formula:

[0064]

[0065] In this way, all data can be mapped to a unified scale, making each data value present a normalized measure relative to its data range.

[0066] Z-score normalization calculates the normalized value for each indicator data using the following formula:

[0067]

[0068] Among them, x is the data point, μ is the mean of this data set, and σ is the standard deviation; this normalization method makes the mean of the data 0 and the standard deviation 1, and is more suitable for data with different distribution characteristics.

[0069] The decimal scaling method normalizes by moving the decimal point position of the data; specifically, it divides the original data by an appropriate base, which is a power of 10, so that the normalized data is in the range [0, 1].

[0070] Specifically, for the normalization of the microbial number density, the min-max normalization method is adopted; for example, assume that the microbial number density of a certain sample is 800 cells / cm², and the maximum value of the microbial number density in the sample is 1000 cells / cm², and the minimum value is 100 cells / cm², then the normalization result of this sample is:

[0071]

[0072] For the normalization of the metabolite concentration, the Z-score normalization method is adopted. The metabolite concentration (organic acids, proteins, etc.) usually has a large fluctuation range, and the Z-score normalization can better reflect its abnormal fluctuation relative to the mean; assume that the concentration of a certain metabolite is 50 mg / L, the mean of the sample is 40 mg / L, and the standard deviation is 10 mg / L, then the Z-score of this data is:

[0073]

[0074] For the standardization of chemical components, the min-max standardization method is used for processing. For example, the pH value usually ranges from 0 to 14, so it is directly mapped to the range of [0, 1].

[0075] For the standardization of physical property parameters (conductivity, adhesion strength), the decimal scaling method is adopted. By appropriate scaling (dividing by a power of 10), these physical property data can be transformed into a reasonable range.

[0076] For the standardization of environmental factors, the Z-score standardization method can be used for processing.

[0077] S3. Establish a hierarchical structure model, and divide all the indicators affecting the degree of microbial contamination into several levels; the hierarchical structure model includes the target layer in the upper layer and the criterion layer in the lower layer. The criterion layer includes index parameters, and the target layer is set as the evaluation of the degree of microbial contamination.

[0078] According to the influence of index parameters on the degree of microbial contamination, assign values to the relative importance between index parameters to obtain the relative importance scores; use the relative importance scores to construct a judgment matrix, and calculate the contribution weights of each index parameter to the degree of microbial contamination through eigenvalue decomposition, denoted as the analytic hierarchy process weights.

[0079] In some embodiments, for each pair of indicators, experts judge their relative importance through pairwise comparison; for example, based on the influence of the number of microbial species and the concentration of metabolites on the degree of microbial contamination, experts give their relative importance scores, using a scale from 1 to 9, where 1 means the two indicators are equally important, and 9 means one indicator is much more important than the other.

[0080] In a feasible implementation manner, the construction method of the judgment matrix is as follows:

[0081] Suppose there are three main indicators: the number and type of microorganisms (M1), metabolite concentration (M2), and chemical composition (M3).

[0082] Through expert evaluation, the following judgment matrix is constructed:

[0083]

[0084] Among them, the elements of the matrix represent the relative importance between each pair of indicators; for example, the relative importance score of "the number and type of microorganisms" relative to "metabolite concentration" is 3, indicating that the former is more important than the latter; correspondingly, the relative importance score of "chemical composition" relative to "the number and type of microorganisms" is 2, and the relative importance score of "chemical composition" relative to "metabolite concentration" is 4.

[0085] By performing eigenvalue decomposition on the judgment matrix, the weight vector of each index is calculated; the weight vector reflects the relative importance of each index in the evaluation system; for the above judgment matrix, the weight vector can be obtained through eigenvalue decomposition, as follows:

[0086]

[0087] This weight vector means that the contribution of "microbial species and quantity" to the pollution level is 50%, the contribution of "metabolite concentration" to the pollution level is 30%, and the contribution of "chemical composition" to the pollution level is 20%.

[0088] In some other embodiments, the hierarchical model further includes a sub-criterion layer; the top layer is the target layer, i.e., "microbial pollution level evaluation", the next layer is the criterion layer, i.e., all the main evaluation indexes (such as microbial species, metabolite concentration, etc.), and the bottom layer is the sub-criterion layer, which further details the influencing factors of each index (such as the influence of temperature and humidity on microbial growth, etc.); the sub-criterion layer includes at least one index parameter, and the index parameters of the sub-criterion layer are the influencing factors of at least one index parameter of the criterion layer.

[0089] Specifically, the calculation method of the contribution weight of the index parameters in the sub-criterion layer to the microbial pollution level includes the following situations:

[0090] Situation 1:

[0091] The sub-criterion layer includes multiple index parameters, and the multiple index parameters in the sub-criterion layer are the influencing factors of a single index parameter in the criterion layer.

[0092] Assign values to the relative importance among the multiple index parameters in the sub-criterion layer to obtain the relative importance score, construct a judgment matrix using the relative importance score, calculate the contribution weight of each index parameter in the sub-criterion layer to the corresponding index parameter in the criterion layer through eigenvalue decomposition, and calculate the contribution weight of the index parameters in the sub-criterion layer to the microbial pollution level according to the contribution weight of the corresponding index parameter in the criterion layer to the microbial pollution level.

[0093] For example, the three environmental parameters of temperature, humidity, and atmospheric pressure all affect the quantity density of microorganisms, and the quantity density of microorganisms directly affects the microbial pollution level of the equipment. In the hierarchical model, temperature, humidity, and atmospheric pressure are located in the sub-criterion layer, and the quantity density of microorganisms is located in the criterion layer; referring to the example in step S3, by obtaining the relative importance scores of temperature, humidity, and atmospheric pressure from experts, constructing a judgment matrix and solving the weight vector, the contribution weights of temperature, humidity, and atmospheric pressure to the quantity density of microorganisms can be obtained, and then multiplying by the contribution weight of the quantity density of microorganisms to the microbial pollution level, the contribution weights of temperature, humidity, and atmospheric pressure to the microbial pollution level can be obtained.

[0094] Case 2:

[0095] A single sub-criterion layer index parameter is an influencing factor for multiple criterion layer index parameters. Assign relative importance scores to the relative importance of the sub-criterion layer index parameters for different criterion layer index parameters. Use the relative importance scores to construct a judgment matrix. Calculate the contribution weights of the sub-criterion layer index parameters for different criterion layer index parameters through eigenvalue decomposition, and calculate the contribution weights of the sub-criterion layer index parameters for the degree of microbial contamination based on the contribution weights of different criterion layer index parameters to the degree of microbial contamination.

[0096] For example, the humidity at the sub-criterion layer affects the microbial number density, microbial species distribution, metabolite types, and surface conductivity at the criterion layer at the same time. Referring to the example in step S3, according to the contribution degree of humidity to the microbial number density, microbial species distribution, metabolite types, and surface conductivity, assign relative importance scores by experts, construct a judgment matrix and solve the weight vector, and combine the contribution weights of the microbial number density, microbial species distribution, metabolite types, and surface conductivity to the degree of microbial contamination, and perform weighted summation to obtain the contribution weight of humidity to the degree of microbial contamination.

[0097] In addition, for the "many-to-many" mapping relationship where multiple sub-criterion layer index parameters affect multiple criterion layer index parameters, the combined weight vector of the sub-criterion layer for the target layer can be obtained by weighted summation with reference to Case 1 and Case 2.

[0098] By setting the sub-criterion layer, the contributing factors of the degree of microbial contamination are further refined. In particular, considering the influence of environmental factors on the growth and metabolism of microorganisms, the microbial contamination evaluation method proposed by the present invention is made more scientific and comprehensive.

[0099] S4. Calculate the comprehensive score of the degree of microbial contamination based on the standardized data and the analytic hierarchy process weights; and perform a risk assessment on the degree of microbial contamination according to the comprehensive score to determine the risk level.

[0100] In some embodiments, input the standardized data and analytic hierarchy process weights corresponding to all index parameters into the scoring algorithm, and the comprehensive score of the degree of microbial contamination can be calculated through weighted summation; scientifically and reasonably determine the contribution weights of each index parameter in the degree of microbial contamination through the analytic hierarchy process, providing an objective calculation basis for the comprehensive score; the comprehensive score result reflects the overall contamination degree of the sample, and the higher the value, the more serious the microbial contamination, and vice versa, the lighter the contamination.

[0101] In some other embodiments, before or after step S3, it further includes the step of obtaining the contribution score of each index parameter to the degree of microbial contamination by the expert scoring method, denoted as the expert score.

[0102] For example, 20 experts with experience in the fields of power equipment, microbial detection, and electrical equipment safety can be invited. According to the judgment basis (the judgment basis includes: 1. The role of the index in affecting the degree of microbial contamination on the outer insulation surface of electrical equipment; 2. The potential impact of the index on the safety and operating performance of the equipment; 3. The sensitivity of the change of this index to the change of the degree of microbial contamination), the experts score each index parameter from 1 to 5. 1 indicates that the index has a relatively small impact on pollution, and 5 indicates that its impact is very important.

[0103] Before step S4, there is also a step: performing a weighted average on the expert scores and the analytic hierarchy process weights corresponding to all index parameters to obtain the comprehensive weight of each index parameter; the expert scoring method provides a direct reflection of expert experience, while the analytic hierarchy process in step S3 provides a more objective calculation basis by quantifying the relative importance between indicators. Combining the two can eliminate subjectivity and ensure the reliability of the evaluation results.

[0104] In the embodiment of calculating the comprehensive weight, step S4 is actually: inputting the standardized data and the comprehensive weights corresponding to all index parameters into a scoring algorithm, and calculating the comprehensive score of the degree of microbial contamination through weighted summation; the comprehensive score result reflects the overall pollution degree of the sample. The higher the value, the more serious the microbial contamination, and vice versa, the lighter the pollution.

[0105] In the embodiment of the present invention, the score of each index is given by a weighted summation model, and the calculation formula of the comprehensive score is as follows:

[0106]

[0107] Among them, f is the scoring function, is the standardized data for each item, is the weight value corresponding to each item of data.

[0108] In step S4, based on the comprehensive score, a risk assessment of the degree of microbial contamination is performed and divided into different risk levels.

[0109] Table 1 shows an implementation method of defining risk levels. Four score intervals of "<25", "25 - 75", "75 - 95", and ">95" are defined according to the comprehensive score, corresponding to four levels of "low", "medium", "high", and "extremely severe" for the degree of microbial contamination, and corresponding descriptions of risks and equipment maintenance suggestions are put forward.

[0110]

[0111] Table 1 Comparison table of comprehensive score and pollution risk level

[0112] Refer to Figure 5 , Figure 5 which shows the implementation logic block diagram of the embodiments of the present invention. The method for evaluating microbial contamination based on multi-index parameters proposed in the embodiments of the present invention standardizes the detection data of different physical dimensions so that the data between different indicators can be compared with each other, providing a reliable data basis for comprehensive scoring; assigns weights to the relative importance between indicator parameters, and scientifically and reasonably determines the contribution weight of each indicator parameter to the degree of microbial contamination through the analytic hierarchy process, providing an objective calculation basis for comprehensive scoring. Combining the standardized indicator parameters and the corresponding contribution weights to the degree of microbial contamination, the comprehensive score of the degree of microbial contamination can be obtained through the weighted summation algorithm; it is also possible to further combine the empirical scores of the expert scoring method, and perform weighted average fusion of the expert scores and the analytic hierarchy process weights to generate the comprehensive weight of each final indicator. The comprehensive score calculated based on the comprehensive weight and the standardized indicator parameters can eliminate subjectivity and further improve the reliability of the evaluation results.

[0113] The evaluation method proposed in the embodiments of the present invention fully considers the non-linear relationship between different parameters and the influence of various complex factors on the degree of microbial contamination of power equipment; different risk levels can be divided according to the obtained comprehensive score, providing a clear decision-making basis for equipment maintenance, helping to take corresponding prevention and control measures in a timely manner, and ensuring the safe operation of power equipment.

[0114] The embodiments of the present invention fill the technical gap in the current field of microbial contamination assessment, provide strong technical support for the operation and maintenance and risk management of electrical equipment, and promote the application progress of microbial contamination detection technology in the field of power equipment.

[0115] Based on the embodiments of the method for evaluating microbial contamination based on multi-index parameters described above, the present invention also proposes a system for evaluating microbial contamination based on multi-index parameters.

[0116] Refer to Figure 2 , the system for evaluating microbial contamination based on multi-index parameters includes a sampling module, a detection module, and a data standardization module, a hierarchical structure model establishment module, an input module, a weight assignment module, and a comprehensive scoring module for data processing.

[0117] Specifically, the sampling module is used to collect the contamination samples on the surface of the equipment and environmental parameters.

[0118] Refer to Figure 3 , the collection objects of the sampling module include the following four types:

[0119] Microbial population, collecting microorganisms such as bacteria, fungi, and algae attached to the insulation surface;

[0120] Metabolites, the metabolites produced during the growth of microorganisms, such as organic acids, lipids, proteins, etc. These metabolites can reflect the metabolic activity of microorganisms and the severity of pollution;

[0121] Attachments and particles, including biofilms formed by microbial aggregation, adherent particles, and other impurities related to pollution;

[0122] Environmental parameters, that is, the environmental data contained in the sample, such as atmospheric pressure, surface temperature and humidity, pollutant conductivity, etc.

[0123] In the embodiments of the present invention, the sampling module can use a flexible sampling head for sampling; the flexible sampling head contacts the surface of the device, and separates the attached microorganisms and their metabolites through slight vibration or friction. The sampling head can be configured with tiny bristles and adsorption sheets, which can physically capture the sample and reduce sample loss.

[0124] Refer to Figure 4 , the detection module is used to detect the contaminated sample, and detect the following index parameters based on the sampled sample: microbial number density and species distribution, types and concentrations of metabolites, pH value and corrosive substance concentration of the sample, surface conductivity and adhesion strength, as well as environmental temperature, humidity and air pressure parameters of the sampling area. Among them, the relevant sensors for detecting environmental parameters can be integrated with the sampling module to improve the detection efficiency.

[0125] The detection module includes various sensors such as optical sensors, chemical sensors, conductivity sensors, and environmental monitoring sensors, etc. to obtain the index parameters for microbial evaluation. At the same time, professional detection equipment is also used to obtain the index parameters. The detection methods for specific items can refer to the foregoing method embodiments and will not be elaborated here.

[0126] The data standardization module is used to perform standardization processing on the index parameters to obtain standardized data; according to the selected index parameters involving different physical dimensions, the min-max standardization method, Z-score standardization method, and decimal scaling method can be used to standardize these data.

[0127] Among them, for the standardization of microbial number density, the min-max standardization method is adopted; for the standardization of metabolite concentration, the Z-score standardization method is adopted; for the standardization of chemical components, the min-max standardization method is adopted; for the standardization of physical property parameters (conductivity, adhesion strength), the decimal scaling method is adopted.

[0128] The hierarchical structure model establishment module is used to establish a hierarchical structure model including the target layer and the criterion layer. The criterion layer includes index parameters, and the target layer is set as the evaluation of microbial pollution degree.

[0129] The hierarchical model can also establish a sub-criterion layer, which includes at least one index parameter. The index parameters in the sub-criterion layer are the influencing factors of at least one index parameter in the criterion layer. The top layer of the hierarchical model obtained in this way is the target layer, that is, "microbial pollution degree evaluation", the next layer is the criterion layer, that is, all the main evaluation indicators, and the bottom layer is the sub-criterion layer, which further refines the influencing factors of each index.

[0130] An input module is used to assign relative importance values to the relative importance between index parameters according to the influence of the index parameters on the microbial pollution degree, so as to obtain a relative importance score. Among them, in a feasible implementation manner, for each pair of indexes, experts judge their relative importance through pairwise comparison and give a relative importance score.

[0131] The weight allocation module is used to construct a judgment matrix by using the relative importance score, calculate the contribution weight of each index parameter to the microbial pollution degree through eigenvalue decomposition, and record it as the analytic hierarchy process weight. The specific calculation method of the analytic hierarchy process weight refers to the method embodiment described above and will not be elaborated here.

[0132] The comprehensive score module is used to calculate the comprehensive score of the microbial pollution degree according to the standardized data and the analytic hierarchy process weight.

[0133] The score of each index is given by a weighted summation model, and the calculation formula of the comprehensive score is as follows:

[0134]

[0135] Among them, f is the scoring function, are the standardized data items, are the corresponding weight values.

[0136] In the embodiment of calculating the comprehensive weight, the input module is further used to input the contribution score of each index parameter to the microbial pollution degree, which is recorded as the expert score;

[0137] Correspondingly, the weight allocation module is further used to perform weighted averaging on the expert scores and the analytic hierarchy process weights corresponding to all index parameters to obtain the comprehensive weight of each index parameter; the comprehensive score module is used to input the standardized data and the comprehensive weights corresponding to all index parameters into the scoring algorithm, and calculate the comprehensive score of the microbial pollution degree through weighted summation.

[0138] The risk level confirmation module is used to perform risk assessment on the microbial pollution degree according to the comprehensive score and determine the risk level; the division of the risk level can refer to the method embodiment described above and will not be elaborated here.

[0139] The microbial contamination evaluation system based on multi-index parameters proposed by the present invention is applicable to the embodiments of the aforementioned microbial contamination evaluation method based on multi-index parameters. All the beneficial effects possessed by the foregoing method embodiments are also possessed by the microbial contamination evaluation system based on multi-index parameters, and will not be elaborated herein.

[0140] Those of ordinary skill in the art can understand that to implement all or part of the processes in the above method embodiments using this system, it can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memories.

[0141] As can be seen from the above embodiments, the research and development and application of the present invention fill the technical gap in the current field of microbial contamination assessment, thus providing strong technical support for the operation and maintenance and risk management of electrical equipment, and promoting the application progress of microbial contamination detection technology in the field of power equipment.

[0142] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating microbial contamination based on multi-index parameters, characterized in that, It includes the following steps: S1. Obtain the values of multiple index parameters for microbial contamination assessment; S2. Standardize the values of the index parameters to obtain standardized data; S3. Establish a hierarchical structure model; the hierarchical structure model includes an objective layer and a criterion layer, the criterion layer includes the index parameters, and the objective layer is set as the assessment of microbial contamination degree; According to the influence of the index parameters on the microbial contamination degree, assign relative importance to the index parameters to obtain a relative importance score; construct a judgment matrix using the relative importance score, and calculate the contribution weight of each index parameter to the microbial contamination degree through eigenvalue decomposition, denoted as the analytic hierarchy process weight; S4. Calculate the comprehensive score of the microbial contamination degree according to the standardized data and the analytic hierarchy process weight; And conduct a risk assessment on the microbial contamination degree according to the comprehensive score to determine the risk level.

2. The microbial contamination evaluation method based on multi-index parameters according to claim 1, wherein Before or after the step S3, it further includes the step: obtain the contribution score of each index parameter to the microbial contamination degree through the expert scoring method, denoted as the expert score; Before the step S4, it further includes the step: conduct a weighted average on the expert score and the analytic hierarchy process weight corresponding to the index parameter to obtain the comprehensive weight of each index parameter; The step S4 includes: input the standardized data and the comprehensive weight corresponding to all index parameters into a scoring algorithm, and calculate the comprehensive score of the microbial contamination degree through weighted summation.

3. The microbial contamination evaluation method based on multi-index parameters according to claim 1, characterized in that, In the step S3, the hierarchical structure model further includes a sub-criterion layer, and the sub-criterion layer includes at least one index parameter; the index parameters of the sub-criterion layer are the influencing factors of at least one index parameter of the criterion layer.

4. The method for evaluating microbial contamination based on multi-index parameters according to claim 3, wherein The sub-criterion layer includes multiple index parameters, and the multiple index parameters of the sub-criterion layer are the influencing factors of at least one index parameter of the criterion layer. The step S3 further includes: Assign relative importance to the multiple index parameters of the sub-criterion layer to obtain a relative importance score, construct a judgment matrix using the relative importance score, calculate the contribution weight of each index parameter of the sub-criterion layer to the corresponding index parameter of the criterion layer through eigenvalue decomposition, and calculate the contribution weight of each index parameter of the sub-criterion layer to the microbial contamination degree according to the contribution weight of the corresponding index parameter of the criterion layer to the microbial contamination degree.

5. The method for evaluating microbial contamination based on multi-index parameters according to claim 3, wherein The index parameters of the sub-criterion layer are the influencing factors of multiple index parameters of the criterion layer. The step S3 further includes: Assign relative importance to the relative importance of the index parameters of the sub-criterion layer to different index parameters of the criterion layer to obtain a relative importance score, construct a judgment matrix using the relative importance score, calculate the contribution weight of the index parameters of the sub-criterion layer to different index parameters of the criterion layer through eigenvalue decomposition, and calculate the contribution weight of the index parameters of the sub-criterion layer to the microbial contamination degree according to the contribution weight of different index parameters of the criterion layer to the microbial contamination degree.

6. The method for evaluating microbial contamination based on multi-index parameters according to claim 1, wherein The step S1 specifically includes: S11. Collect microbial contamination samples and environmental parameters on the surface of the equipment; S12. Detect the contamination samples to obtain sample parameters; the index parameters include the sample parameters and the environmental parameters; The index parameters include multiple ones among the microbial quantity density, microbial species distribution, metabolite species, metabolite concentration, sample pH value, corrosive substance concentration, surface conductivity, adhesion strength, temperature, humidity, and atmospheric pressure.

7. The method for evaluating microbial contamination based on multi-index parameters according to claim 6, wherein In the step S2, the standardization method is at least one of the minimum-maximum calibration method, Z-score standardization method, and decimal calibration method.

8. A microbial contamination assessment system based on multi-index parameters, characterized in that, Including: A sampling module for collecting microbial contamination samples and environmental parameters on the surface of the device; A detection module for detecting the microbial contamination samples and obtaining sample parameters; A data standardization module for standardizing the values of the index parameters to obtain standardized data; the index parameters include the environmental parameters and the sample parameters; A hierarchical structure model establishment module for establishing a hierarchical structure model including a target layer and a criterion layer, the criterion layer includes the index parameters, and the target layer is set as the evaluation of the degree of microbial contamination; An input module for assigning relative importance to the relative importance among the index parameters according to the influence of the index parameters on the degree of microbial contamination to obtain a relative importance score; A weight allocation module for constructing a judgment matrix using the relative importance score, calculating the contribution weight of each index parameter to the degree of microbial contamination through eigenvalue decomposition, and recording it as the analytic hierarchy process weight; A comprehensive score module for calculating the comprehensive score of the degree of microbial contamination according to the standardized data and the analytic hierarchy process weight; A risk level confirmation module for conducting a risk assessment on the degree of microbial contamination according to the comprehensive score to determine the risk level.

9. The microbial contamination evaluation system based on multi-index parameters according to claim 8, wherein The input module is further used to input the contribution score of each index parameter to the degree of microbial contamination, denoted as the expert score; The weight allocation module is further used to perform a weighted average on the expert scores and the analytic hierarchy process weights corresponding to all index parameters to obtain the comprehensive weight of each index parameter; The comprehensive score module is used to input the standardized data and the comprehensive weights corresponding to all index parameters into a scoring algorithm, and calculate the comprehensive score of the degree of microbial contamination through weighted summation.

10. The microbial contamination evaluation system based on multi-index parameters according to claim 8, characterized in that, The hierarchical structure model further includes a sub-criterion layer, the sub-criterion layer includes at least one index parameter, and the index parameters in the sub-criterion layer are influencing factors of at least one index parameter in the criterion layer.

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