Microorganism detection waste liquid component intelligent analysis processing system and method

Abstract

The application discloses a kind of microbial detection waste liquid component intelligent analysis processing system and method, it is related to the technical field of waste liquid component analysis processing.Method includes: obtaining the basic attribute data of waste liquid to be processed, dynamically setting the weight coefficient of waste liquid attribute index;Set and execute stage analysis process, obtain preliminary analysis determination result;Determine whether to meet the set key index condition, if not satisfied, then waste liquid analysis process is placed in path locking state;Start cross-sample batch linkage judgment mechanism, obtain attribute index similar historical waste liquid sample from historical batch;Determine whether to start abnormal combination trigger mechanism, trigger abnormal supplementary determination process, generate supplementary determination path;Update analysis determination result, generate corresponding waste liquid processing mode suggestion.The application realizes the risk control and self-adaptive optimization of waste liquid processing whole process, improves processing efficiency while ensuring environmental safety.

Description

Technical Field

[0001] The present application relates to the technical field of waste liquid component analysis and treatment, and in particular to a system and method for intelligent analysis and treatment of waste liquid components for microbial detection. Background Art

[0002] With the widespread application of microbial testing in medicine, food, environmental monitoring, and other fields, the types of wastewater generated by related experiments are becoming increasingly complex. The composition of this wastewater is diverse, uncertain, and potentially risky. Direct discharge or improper disposal of wastewater, especially when it contains residual reagents or biological reaction byproducts, can cause environmental pollution or safety hazards. Therefore, proper wastewater treatment is particularly important.

[0003] In related technologies, traditional waste liquid treatment processes mainly rely on manual experience judgment or simple static discrimination rules. They cannot flexibly adjust the judgment logic according to the experimental type and detection purpose, and the analysis results of complex waste liquids are unstable. At the same time, they cannot perform structured recognition and emergency remediation of specific abnormal patterns, and their dynamic recognition capabilities are weak, so there is room for improvement. Summary of the Invention

[0004] The purpose of the present invention is to provide a system and method for intelligent analysis and processing of waste liquid components for microbial detection, so as to solve the problems raised in the above-mentioned background technology.

[0005] In the first aspect, the present application provides a method for intelligent analysis and treatment of waste liquid components for microbial detection, which adopts the following technical solutions:

[0006] Obtain the basic attribute data of the waste liquid to be treated, extract the experimental type and experimental reagent components of the microbial detection experiment, and dynamically set the weight coefficient of the waste liquid attribute index;

[0007] Setting a staged analysis process, which includes a prejudgment stage, a preliminary screening stage, and a refined judgment stage, and executing the staged analysis process to obtain a preliminary analysis result;

[0008] Based on the preliminary analysis results, determine whether the set key indicator conditions are met. If not, place the current waste liquid analysis process in a path locking state;

[0009] Based on the path locking status, a cross-sample batch linkage judgment mechanism is activated to obtain historical waste liquid samples with similar attribute indicators from historical batches as supplementary judgment reference information;

[0010] Based on the historical waste liquid samples, determine whether to activate the abnormal combination trigger mechanism, match the abnormal combination pattern to trigger the abnormal supplementary determination process, and generate a supplementary determination path;

[0011] Based on the supplementary determination path, the path unlocking state is entered, the analysis and determination result is updated, and a corresponding waste liquid treatment method recommendation is generated.

[0012] Preferably, the steps of obtaining basic attribute data of the waste liquid to be treated, extracting the experimental type and experimental reagent components of the microbiological detection experiment, and dynamically setting the weight coefficient of the waste liquid attribute index are specifically as follows:

[0013] Obtaining basic attribute data of the waste liquid to be treated, wherein the basic attribute data includes color information, foam characteristics, odor level, and sedimentation state;

[0014] Extracting microbial detection experiment context information corresponding to the waste liquid to be processed, wherein the experiment context information includes the experiment type, experimental reagent composition, and batch number;

[0015] According to the experimental type and experimental reagent components, calling the preset basic attribute weight configuration template;

[0016] Based on the batch number, if an abnormal record mark appears in the experiment with the same batch number, the basic weight coefficient dynamic adjustment rule is triggered, and the weight of the basic attribute index is adjusted to obtain the standard weight coefficient of the waste liquid attribute index.

[0017] Preferably, a staged analysis process is set, which includes a pre-judgment stage, a preliminary screening stage, and a precise judgment stage. The steps of executing the staged analysis process to obtain a preliminary analysis result are specifically as follows:

[0018] Setting up a staged analysis process, which includes a prejudgment stage, a preliminary screening stage, and a precise judgment stage;

[0019] In the prediction stage, based on the experimental type and experimental reagent components, the main risk components are inferred and the corresponding risk labels are obtained;

[0020] In the initial screening stage, the basic attribute data of the waste liquid to be treated are compared with the preset risk threshold corresponding to the risk label item by item. If any attribute indicator exceeds the preset risk threshold, the waste liquid is marked as high-risk waste liquid and the subsequent analysis process is terminated;

[0021] In the precise judgment stage, for waste liquids that are not marked as high-risk, weighted scores are performed on each attribute indicator according to the standard weight coefficient of the waste liquid attribute indicator to obtain the risk score result and output the preliminary analytical judgment result.

[0022] Preferably, based on the preliminary analysis determination result, it is determined whether the set key indicator conditions are met. If not, the step of placing the current waste liquid analysis process in a path locking state is specifically as follows:

[0023] Based on the preliminary analysis and determination result, a matching key indicator condition set is loaded, wherein the key indicator condition set includes a value range constraint, a qualitative outlier constraint, and a scoring interval constraint;

[0024] The numerical range constraint is used to constrain the attribute index to be within a reasonable numerical range, the qualitative outlier constraint is used to determine whether there are predefined unacceptable qualitative description values, and the score interval constraint is used to exclude risk score results in uncertain edge areas;

[0025] Compare and judge each attribute indicator in the preliminary analysis and judgment result with the key indicator condition set item by item;

[0026] If any attribute indicator does not meet the corresponding constraint condition, the current waste liquid analysis process will be placed in a path locking state and the subsequent processing process will be terminated.

[0027] Preferably, based on the path locking state, a cross-sample batch linkage judgment mechanism is enabled to obtain historical waste liquid samples with similar attribute indicators from historical batches as supplementary judgment reference information, specifically the following steps:

[0028] Based on the path locking status, a cross-sample batch linkage judgment mechanism is enabled;

[0029] According to the preset attribute indicator similarity threshold, several historical waste liquid samples whose attribute indicators are similar to those of the waste liquid to be treated and whose similarity is not less than the preset attribute indicator similarity threshold are selected from historical batches as supplementary judgment reference information under the path locking state.

[0030] Preferably, based on the historical waste liquid samples, determining whether to start the abnormal combination trigger mechanism, matching the abnormal combination pattern to trigger the abnormal supplementary determination process, and generating the supplementary determination path are specifically as follows:

[0031] Based on the historical waste liquid samples, extracting parsing path information of the historical waste liquid samples, and statistically analyzing the disposal category distribution of the historical waste liquid samples to obtain historical disposal category results;

[0032] Calculating the result confidence of the historical treatment category result, and comparing the result confidence with a preset confidence threshold;

[0033] If the confidence level of the result reaches a preset confidence threshold, the corresponding analytical result of the historical disposal category result is obtained as the analytical determination result of the waste liquid to be treated;

[0034] If the confidence level of the result does not reach the preset confidence threshold, the abnormal combination trigger mechanism is activated, the abnormal combination pattern is matched to trigger the abnormal supplementary judgment process, and a supplementary judgment path is generated.

[0035] Preferably, if the confidence level of the result does not reach a preset confidence threshold, the abnormal combination trigger mechanism is activated, the abnormal combination pattern is matched to trigger the abnormal supplementary judgment process, and the steps of generating a supplementary judgment path are specifically as follows:

[0036] If the confidence level of the result does not reach the preset confidence threshold, the abnormal combination trigger mechanism is activated;

[0037] Constructing an abnormal pattern combination library, wherein the abnormal pattern combination library includes a plurality of attribute combination patterns of high-risk waste liquids, each pattern group consisting of two or more attribute indicators;

[0038] Matching the attribute indicators of the waste liquid to be treated with each group of abnormal patterns in the abnormal pattern combination library one by one;

[0039] If the attribute indicators of the waste liquid to be treated meet any set of abnormal patterns in the abnormal pattern combination library, the abnormality supplementary judgment process is triggered;

[0040] Based on the triggered abnormal pattern, a supplementary determination policy template matching the pattern is called to generate a supplementary determination path.

[0041] Preferably, based on the supplementary determination path, the process of entering the path unlocking state, updating the analysis determination result, and generating the corresponding waste liquid treatment method recommendation is as follows:

[0042] Based on the supplementary determination path, the current waste liquid analysis process is switched from a path-locked state to a path-unlocked state;

[0043] For the unlocked state of the path, the waste liquid analysis process is allowed to go back to the refined judgment stage and re-execute the judgment to obtain an updated analysis and judgment result;

[0044] Based on the updated analysis and determination results, a component-strategy mapping table is matched to generate corresponding waste liquid treatment method recommendations.

[0045] In a second aspect, the present application provides an intelligent analysis and processing system for waste liquid components used for microbial detection, which adopts the following technical solutions:

[0046] An intelligent analysis and processing system for waste liquid components used for microbial detection, comprising:

[0047] The weight dynamic setting module obtains the basic attribute data of the waste liquid to be treated, extracts the experimental type and experimental reagent components of the microbial detection experiment, and dynamically sets the weight coefficient of the waste liquid attribute index;

[0048] The stage-by-stage analysis module sets a stage-by-stage analysis process, which includes a prejudgment stage, a preliminary screening stage, and a precise judgment stage. The stage-by-stage analysis process is executed to obtain a preliminary analysis result.

[0049] A path status management module determines whether the set key indicator conditions are met based on the preliminary analysis results. If not, the current waste liquid analysis process is placed in a path lock state;

[0050] A cross-batch linkage judgment module, based on the path locking state, starts a cross-sample batch linkage judgment mechanism, and obtains historical waste liquid samples with similar attribute indicators from historical batches as supplementary judgment reference information;

[0051] The abnormality identification and supplementary judgment module determines whether to activate the abnormal combination trigger mechanism based on the historical waste liquid samples, matches the abnormal combination pattern to trigger the abnormal supplementary judgment process, and generates a supplementary judgment path;

[0052] The analysis and determination processing module enters a path unlocking state based on the supplementary determination path, updates the analysis and determination result, and generates a corresponding waste liquid treatment method recommendation.

[0053] In summary, this application includes at least one of the following beneficial technical effects:

[0054] 1. Different experimental types generate wastewater with varying properties. By dynamically assigning indicator weights, wastewater attribute assessment becomes scenario-specific, addressing the varying impact of wastewater attributes across different testing contexts and improving the relevance and accuracy of wastewater analysis. A three-stage, hierarchical analysis process is constructed, enabling comprehensive assessment from risk inference to precise scoring. This not only improves the stability and controllability of the analysis process, but also avoids the errors and uncertainties introduced by a single assessment step and the waste of resources for each wastewater sample throughout the entire process. By setting key indicator constraints for judgment, the analysis process is locked in the event of a judgment failure, preventing risky wastewater from entering the processing phase with insufficient data or uncertain results, thus ensuring system stability and security. A cross-sample batch linkage judgment mechanism is implemented, supplementing the judgment basis for current wastewater processing by incorporating historical samples, enhancing the contextual richness and accuracy of judgments and improving the adaptive processing capabilities of the intelligent system. By building a library of anomaly combination patterns, matching pre-set high-risk combination patterns triggers anomaly compensation logic, enabling automated compensatory judgments in abnormal situations. This improves the system's ability to identify attribute anomalies and enhances the intelligent handling of hidden risks. By utilizing the anomaly re-judgment results to resume process execution, the system can unlock the path lock, regenerate the analysis results, and output the corresponding treatment strategy, thus implementing a closed-loop decision-making mechanism under abnormal conditions. This ensures that the analysis process is fault-tolerant, self-recovering, and adaptive, ultimately enabling intelligent recommendations for wastewater treatment strategies. Through dynamic weight adjustment, staged intelligent analysis, and anomaly triggering mechanisms, risk management and adaptive optimization of the entire wastewater treatment process are achieved, improving treatment efficiency and compliance, ensuring environmental safety, and optimizing resource allocation.

[0055] 2. Build a hierarchical and progressive intelligent wastewater analysis mechanism. In microbial testing scenarios where wastewater composition is complex and variable, this staged structure reduces the risk of single-point misidentification, improves processing efficiency and analysis accuracy, and provides an early blocking mechanism for high-risk samples to ensure system safety. In the pre-diagnosis stage, the types of reagents and chemical structures used in different testing experiments vary significantly, leading to significant differences in the types of potentially hazardous wastewater components. Establishing an expected risk tag through risk labeling can guide the indicator warning logic in the subsequent initial screening stage, empowering the system with predictive capabilities. In the initial screening stage, a rapid preliminary filtering mechanism is implemented to identify samples that significantly exceed standards or are abnormal, providing early warnings and blocking. This allows the routine identification process for hazardous wastewater to be terminated at an early stage, thereby avoiding the serious consequences of misidentification and mishandling, while conserving system resources and improving overall processing efficiency. In the refined identification stage, a weighting mechanism is used to comprehensively evaluate the multi-dimensional attributes of wastewater, providing a comprehensive reflection of the wastewater's risk level or treatment difficulty. This is suitable for routine analysis processes for large volumes of complex samples, ensuring scalability and versatility, and significantly enhancing the engineering usability and intelligent analysis capabilities of the wastewater analysis system for microbial testing.

[0056] 3. Establish a structured knowledge base, preset and include the composite attribute feature combinations of typical high-risk waste liquids, and form an abnormal pattern combination library, which can more effectively identify potential high-risk waste liquids. Match the attribute indicators of the waste liquid to be treated with each group of abnormal patterns in the abnormal pattern combination library one by one, and compare whether the current waste liquid hits the combined abnormal pattern to avoid the problem of false triggering of alarms due to fluctuations in a single indicator, and improve the accuracy and pertinence of abnormal identification. When any group of abnormal combinations is hit, it enters the supplementary judgment processing flow. The system dynamically supplements the judgment based on the actual abnormal situation, does not rely on fixed paths, and enhances the flexibility of the processing flow and the agility of abnormal response. Different types of abnormal patterns may require different analysis methods and processing suggestions. According to the abnormal combination that has been hit, the supplementary judgment strategy template that matches the pattern is called to achieve differentiation and strategy of the parsing and judgment path, avoid a one-size-fits-all processing solution, and form a highly self-consistent intelligent supplementary judgment mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the specific steps of an embodiment of a method for intelligent analysis and treatment of waste liquid components for microbial detection of the present invention.

[0058] Figure 2 This is a schematic diagram of module connections of an embodiment of an intelligent analysis and processing system for waste liquid components for microbial detection according to the present invention. DETAILED DESCRIPTION

[0059] Below is a combination of the embodiments and Figure 1-Figure 2 The present invention will be described in further detail, but the embodiments of the present invention are not limited thereto.

[0060] The present invention discloses a method for intelligently analyzing and processing waste liquid components for microbial detection, which specifically comprises the following steps:

[0061] Step S1, obtaining basic attribute data of the waste liquid to be treated, extracting the experimental type and experimental reagent components of the microbial detection experiment, and dynamically setting the weight coefficient of the waste liquid attribute index;

[0062] Step S2: setting a staged analysis process, which includes a pre-judgment stage, a preliminary screening stage, and a precise judgment stage, and executing the staged analysis process to obtain a preliminary analysis result;

[0063] Step S3: Based on the preliminary analysis result, determine whether the set key indicator conditions are met. If not, place the current waste liquid analysis process in a path locking state;

[0064] Step S4: Based on the path locking status, a cross-sample batch linkage judgment mechanism is activated to obtain historical waste liquid samples with similar attribute indicators from historical batches as supplementary judgment reference information;

[0065] Step S5: Based on the historical waste liquid samples, determine whether to start the abnormal combination trigger mechanism, match the abnormal combination pattern to trigger the abnormal supplementary determination process, and generate a supplementary determination path;

[0066] Step S6: Based on the supplementary determination path, the process enters the path unlocking state, updates the analysis and determination results, and generates corresponding waste liquid treatment method recommendations.

[0067] In actual application, the properties of waste liquid generated under different experimental types are different. By dynamically setting the index weight, the waste liquid property evaluation has scene adaptability, solves the problem of different influence degrees of waste liquid properties in different detection backgrounds, and improves the pertinence and accuracy of waste liquid analysis and judgment. A three-section hierarchical analysis process is constructed to realize the whole process evaluation from risk speculation to accurate scoring, which not only improves the stability and controllability of the analysis process, avoids errors and uncertainties caused by a single determination step, but also avoids waste of resources for each waste liquid sample in the whole process. Through key index constraint judgment setting, when the judgment fails, the execution of the analysis process is locked to avoid risk waste liquid entering the processing stage in the case of insufficient data or uncertain results, and to ensure the stability and safety of the system. The cross-sample batch linkage judgment mechanism is started, the judgment basis of the current waste liquid to be processed is supplemented by introducing historical samples, the context richness and accuracy of the judgment are improved, and the adaptive processing capability of the intelligent system is improved. By constructing an abnormal combination mode library, the abnormal compensation judgment logic is triggered by matching the preset high-risk combination mode, the automatic compensation judgment in abnormal conditions is realized, the identification ability of the system to abnormal properties is improved, and the intelligent processing of implicit risks is enhanced. The abnormal compensation judgment result is used to restore the process execution, the system can release the path lock state, and the analysis result is regenerated, the corresponding processing strategy is output, the closed-loop decision mechanism in abnormal state is realized, the analysis process has fault tolerance, self-recovery and adaptive ability, and finally the intelligent recommendation of waste liquid processing strategy is realized.

[0068] The basic attribute data of the waste liquid to be processed is obtained, the experimental type and experimental reagent composition of the microbial detection experiment are extracted, and the weight coefficient of the waste liquid attribute index is dynamically set. The steps are as follows:

[0069] Step S11, obtaining the basic attribute data of the waste liquid to be processed, the basic attribute data including color information, foam characteristics, odor level and sediment state;

[0070] Step S12, extracting the microbial detection experiment context information corresponding to the waste liquid to be processed, the experiment context information including experimental type, experimental reagent composition and batch number;

[0071] Step S13, calling a preset basic attribute weight configuration template according to the experimental type and experimental reagent composition;

[0072] Step S14, based on the batch number, if an abnormal record identifier appears in the same batch number experiment, triggering the basic weight coefficient dynamic adjustment rule to adjust the weight of the basic attribute index, and obtaining the standard weight coefficient of the waste liquid attribute index.

[0073] In actual application, the observable physical properties of wastewater directly reflect its possible risk factors and treatment characteristics. For example, the precipitation state may be associated with biochemical reaction residues, the odor level may indicate potential volatile harmful components, and the foam characteristics are related to surfactants. The basic characterization feature dimensions of wastewater samples are constructed to provide input parameters for subsequent judgments. Different types of microbial detection experiments have different requirements for the type and concentration of reagents used, and the corresponding wastewater characteristics will also be different. Contextual information can be used as a key label factor to guide the attribute interpretation dimension, helping to avoid judgment errors due to different meanings of the same attribute. According to the experimental type and experimental reagent components, the preset basic attribute weight configuration template is called to determine the priority and weight of the attribute indicators used for analysis and judgment. For example, in some types of microbial detection experiments, color changes can better reflect the reagent reaction results, while in other types, precipitation is the key discrimination indicator. By calling different weight configuration templates, the system has scene perception capabilities, ensuring that subsequent analysis is carried out with the most effective feature-driven logic. If misjudgments or conflicting judgments frequently occur in experiments within the same batch, it can be considered that there is batch deviation or environmental interference in the data characteristics. In this case, the fixed template will not be sufficient to cope with it. By dynamically adjusting the weight mechanism, the intelligent analysis system can have short-term adaptive capabilities, thereby enhancing its response sensitivity and processing accuracy to batch anomalies.

[0074] A staged analysis process is set up, which includes a prejudgment stage, a preliminary screening stage, and a precise judgment stage. The steps of executing the staged analysis process to obtain a preliminary analysis result are as follows:

[0075] Step S21, setting a staged analysis process, the staged analysis process includes a pre-judgment stage, a preliminary screening stage, and a precise judgment stage;

[0076] Step S22: For the prejudgment stage, the main risk components are inferred based on the experiment type and the experimental reagent components, and corresponding risk labels are obtained;

[0077] Step S23: During the initial screening phase, the basic attribute data of the waste liquid to be treated is compared item by item with the preset risk threshold corresponding to the risk label. If any attribute index exceeds the preset risk threshold, the waste liquid is marked as high-risk waste liquid, and the subsequent analysis process is terminated.

[0078] Step S24, for the precise judgment stage, for waste liquids that are not marked as high-risk, weighted scores are performed on each attribute indicator according to the standard weight coefficient of the waste liquid attribute indicator to obtain a risk score result, and a preliminary analysis and judgment result is output.

[0079] In actual application, a hierarchical and progressive waste liquid intelligent analysis mechanism is constructed. In the complex and variable microbial detection scene of waste liquid composition, the stage structure can reduce the risk of single point misjudgment, improve the processing efficiency and analysis accuracy, and at the same time, set an early blocking mechanism for high-risk samples to ensure system safety. In the prediction stage, the types of reagents and chemical structures are different in different detection experiments, and the types of potential harmful waste liquid components also have significant differences. By establishing an expected risk label, the system can guide the index early warning logic in the subsequent screening stage, so that the system has the ability to predict. In the screening stage, a rapid preliminary filtering mechanism is executed to identify significantly over-standard or abnormal samples, and early warning and blocking are performed to terminate the regular judgment process of dangerous waste liquid in the early stage, thereby avoiding serious consequences such as misjudgment and misdisposal, saving system resources, and improving overall processing efficiency. In the precise judgment stage, a weight mechanism is used to comprehensively evaluate the multi-dimensional attributes of waste liquid, which can more comprehensively reflect the risk level or processing difficulty of waste liquid, and is suitable for the regular analysis process of large quantities of complex samples, ensuring the scalability and universality of the analysis, and greatly enhancing the engineering usability and intelligent analysis capability of the microbial detection waste liquid analysis system.

[0080] Based on the preliminary analysis and judgment result, it is judged whether the set key index condition is met, and if not, the current waste liquid analysis process is placed in the path locking state, specifically:

[0081] Step S31, based on the preliminary analysis and judgment result, load the key index condition set matched therewith, the key index condition set includes numerical range constraint, qualitative abnormal value constraint and score interval constraint;

[0082] Step S32, the numerical range constraint is used to constrain the attribute index in a reasonable numerical range, the qualitative abnormal value constraint is used to judge whether there is a predefined unacceptable qualitative description value, and the score interval constraint is used to exclude the risk score result in the uncertain edge zone;

[0083] Step S33, compare each attribute index in the preliminary analysis and judgment result with the key index condition set one by one;

[0084] Step S34, if any attribute index does not meet the corresponding constraint condition, the current waste liquid analysis process is placed in the path locking state, and the subsequent processing process is terminated.

[0085] In actual application, the judgment criteria required for different experimental backgrounds and waste liquid categories vary. The corresponding judgment criteria are selected based on the preliminary analysis results. By dynamically matching the key indicator condition set, the waste liquid treatment process is ensured to have a targeted judgment basis, improving the analysis accuracy and misjudgment control capabilities, and avoiding adaptation deviations caused by generalized templates. The functions and threshold types of various constraints in the key indicator condition set are precisely defined to form the core logic of the judgment rules; the numerical range constraint ensures that each physical / chemical indicator is within a safe or reasonable range, preventing numerical errors from masking potential risks; qualitative outlier constraints are used to eliminate obviously unacceptable conditions, such as strong irritating odors and flocculent precipitation, forming a regularized veto mechanism; the scoring interval constraint effectively prevents the scoring results from falling within the boundary range of unclear high and low, causing misleading results, making the judgment more decision-making and executable. Each attribute indicator in the analysis result is checked item by item to verify whether it meets the key constraint conditions. It can comprehensively cover different risk sources and accurately identify hidden dangers, reflecting the system's fine-grained rule control capabilities. If any attribute indicator does not meet the corresponding constraint conditions, the system will immediately enter the path locking state, blocking the regular processing path and waiting for the abnormal judgment mechanism to intervene, preventing uncertain or potential high-risk waste liquid from mistakenly entering the regular disposal process, causing treatment decision errors or environmental hazards, and improving the stability and credibility of the system.

[0086] Based on the path locking status, a cross-sample batch linkage judgment mechanism is enabled to obtain historical waste liquid samples with similar attribute indicators from historical batches as supplementary judgment reference information. Specifically, the following steps are performed:

[0087] Step S41: Based on the path locking status, a cross-sample batch linkage judgment mechanism is enabled;

[0088] Step S42 , based on a preset attribute index similarity threshold, select several historical waste liquid samples from historical batches whose attribute index similarity with the waste liquid to be processed is not less than the preset attribute index similarity threshold as supplementary determination reference information in the path locking state.

[0089] In actual use, after the parsing process enters the path locking state, it automatically triggers the linkage judgment mechanism and enters the enhanced judgment process to make up for the problem of insufficient data or ambiguous judgment of a single sample under abnormal conditions; the introduction of external reference samples through the linkage mechanism can provide a horizontal analogy basis for the judgment of complex or boundary samples, and realize intelligent correction and judgment enhancement. A feature comparison method is used to perform similarity calculations on waste liquid samples in historical data sets. The similarity calculation is usually based on attribute indicators such as color, odor level, sedimentation state, and foam characteristics, and is quantified through Euclidean distance, cosine similarity or preset template rules. The similarity threshold is used as a screening standard to ensure that the reference sample is representative and comparable, and to avoid the introduction of low-correlation data to interfere with subsequent judgments. Samples with similar attribute indicators are screened out as a comparison reference, and processed precedent labels and recommended judgment paths are provided to support the current waste liquid re-judgment process, which greatly improves the intelligent linkage capability of the parsing system.

[0090] Based on the historical waste liquid samples, determining whether to activate the abnormal combination trigger mechanism, matching the abnormal combination pattern to trigger the abnormal supplementary determination process, and generating the supplementary determination path are specifically as follows:

[0091] Step S51, based on the historical waste liquid samples, extracting the parsing path information of the historical waste liquid samples, and statistically analyzing the disposal category distribution of the historical waste liquid samples to obtain the historical disposal category results;

[0092] Step S52, calculating the result confidence of the historical treatment category result, and comparing the result confidence with a preset confidence threshold;

[0093] Step S53: If the confidence level of the result reaches a preset confidence threshold, the corresponding analysis result of the historical disposal category result is obtained as the analysis and determination result of the waste liquid to be treated;

[0094] Step S54: If the confidence level of the result does not reach the preset confidence threshold, the abnormal combination trigger mechanism is activated to match the abnormal combination pattern to trigger the abnormal supplementary judgment process and generate a supplementary judgment path.

[0095] In actual application, the processing path of the selected historical waste liquid samples is traced back to extract the final disposal category in the analysis process, such as direct discharge, solidification treatment, high-temperature disinfection, special recycling, etc., and the distribution is statistically analyzed. Using the actual processing results of existing samples, a historical disposal reference set that is highly correlated with the attributes of the current sample to be processed is constructed. Through statistical distribution, it can be preliminarily judged whether a certain attribute combination has obvious tendencies or pattern aggregation. By calculating the concentration of the highest-proportioned disposal category in historical samples, the confidence level of the historical disposal category results is obtained, and it is judged whether the current similar attribute combination has a stable judgment trend that can be used as a reference in history; if a certain disposal category accounts for a very high proportion in similar historical samples, it means that this type of attribute combination has a high-certainty tendency, and its judgment path can be directly inherited to avoid blindly entering the abnormal supplementary judgment process with high resource consumption, achieve rapid convergence of the judgment path, and improve the operating efficiency and response stability of the overall analysis system. If the confidence level of the result does not reach the preset threshold and the historical samples cannot form a stable judgment trend, the abnormal combination trigger mechanism will be activated to match typical high-risk patterns from the established abnormal combination pattern library, such as precipitation + odor + severe foaming, and then construct a supplementary judgment path to fill the judgment gap when historical experience is insufficient, and realize knowledge base assisted judgment of abnormal handling, which significantly improves the stability, generalization and adaptability of the analysis system.

[0096] If the confidence level of the result does not reach the preset confidence threshold, the abnormal combination trigger mechanism is activated to match the abnormal combination pattern to trigger the abnormal supplementary judgment process and generate the steps of the supplementary judgment path, specifically:

[0097] Step S541: If the confidence level of the result does not reach a preset confidence threshold, an abnormal combination trigger mechanism is activated;

[0098] Step S542: construct an abnormal pattern combination library, wherein the abnormal pattern combination library includes a plurality of attribute combination patterns of high-risk waste liquids, and each pattern group is composed of two or more attribute indicators;

[0099] Step S543, matching the attribute index of the waste liquid to be processed with each group of abnormal patterns in the abnormal pattern combination library one by one;

[0100] Step S544: If the attribute index of the waste liquid to be processed meets any set of abnormal patterns in the abnormal pattern combination library, the abnormality supplementary judgment process is triggered;

[0101] Step S545 : Based on the triggered abnormal pattern, a supplementary determination policy template matching the pattern is called to generate a supplementary determination path.

[0102] In practical applications, a structured knowledge base is established that pre-sets and includes composite attribute feature combinations of typical high-risk wastewater to form an abnormal pattern combination library, which can more effectively identify potential high-risk wastewater. The attribute indicators of the wastewater to be treated are matched item by item with each abnormal pattern in the abnormal pattern combination library to compare whether the current wastewater matches the combined abnormal pattern. This avoids the problem of false alarm triggering due to fluctuations in a single indicator, and improves the accuracy and specificity of abnormality identification. When any abnormal combination is matched, the system enters the supplementary judgment processing flow. The system dynamically supplements the judgment based on the actual abnormal situation, without relying on a fixed path, enhancing the flexibility of the processing flow and the agility of abnormal response. Different types of abnormal patterns may require different analysis methods and processing suggestions. Based on the abnormal combination that has been matched, the supplementary judgment strategy template that matches the pattern is called to achieve differentiated and strategic analysis and judgment paths, avoiding a one-size-fits-all processing solution and forming a highly self-consistent intelligent supplementary judgment mechanism.

[0103] Based on the supplementary determination path, the path unlocking state is entered, the analysis and determination results are updated, and the corresponding waste liquid treatment method recommendation is generated, specifically:

[0104] Step S61, based on the supplementary determination path, switching the current waste liquid analysis process from a path locked state to a path unlocked state;

[0105] Step S62: For the unlocked path, the waste liquid analysis process is allowed to go back to the refined judgment stage to re-execute the judgment and obtain an updated analysis result.

[0106] Step S63 : Based on the updated analysis and determination result, the component-strategy mapping table is matched to generate corresponding waste liquid treatment method suggestions.

[0107] In actual use, the previously locked waste liquid analysis process is restored to an operational state, and the subsequent judgment mechanism is activated to realize the dynamic control capability of the process, ensuring that the system continues to advance the judgment after absorbing the supplementary judgment information and does not fall into judgment stagnation. The waste liquid analysis process is allowed to go back to the precise judgment stage to re-execute the judgment, and the original risk score and attribute weight score results are recalculated using the supplementary information to improve the accuracy of the judgment results and avoid deviations caused by insufficient early information. At the same time, the newly added abnormal supplementary judgment results are used to guide the system back to the executable node to form a closed loop, realize the information closed loop and the judgment closed loop, and build a self-repairing judgment system architecture. Based on the final updated judgment result, the most suitable processing suggestion is found in the policy database, and an automated and structured processing suggestion output is provided. It no longer relies on manual experience judgment, and realizes the logical linkage of intelligent analysis and processing suggestions.

[0108] A system for intelligent analysis and treatment of waste liquid components for microbial detection, which applies the above-mentioned method for intelligent analysis and treatment of waste liquid components for microbial detection, comprises:

[0109] The weight dynamic setting module obtains the basic attribute data of the waste liquid to be treated, extracts the experimental type and experimental reagent components of the microbial detection experiment, and dynamically sets the weight coefficient of the waste liquid attribute index;

[0110] The stage-by-stage analysis module sets a stage-by-stage analysis process, which includes a prejudgment stage, a preliminary screening stage, and a precise judgment stage. The stage-by-stage analysis process is executed to obtain a preliminary analysis result.

[0111] A path status management module determines whether the set key indicator conditions are met based on the preliminary analysis results. If not, the current waste liquid analysis process is placed in a path lock state;

[0112] A cross-batch linkage judgment module, based on the path locking state, starts a cross-sample batch linkage judgment mechanism, and obtains historical waste liquid samples with similar attribute indicators from historical batches as supplementary judgment reference information;

[0113] The abnormality identification and supplementary judgment module determines whether to activate the abnormal combination trigger mechanism based on the historical waste liquid samples, matches the abnormal combination pattern to trigger the abnormal supplementary judgment process, and generates a supplementary judgment path;

[0114] The analysis and determination processing module enters a path unlocking state based on the supplementary determination path, updates the analysis and determination result, and generates a corresponding waste liquid treatment method recommendation.

[0115] In actual application, the weight dynamic setting module is used to obtain the attribute data and experimental context information of the waste liquid, and the weight coefficient is set in combination with the experiment type and reagent composition; ensure that the weight of the attribute indicator evaluation is dynamically adjusted due to differences in the experimental background, thereby improving the personalization and accuracy of the judgment. Through the staged analysis module, a three-level analysis process of pre-judgment, preliminary screening, and precise judgment is constructed, and the preliminary analysis and judgment results are output accordingly; the rigor of the waste liquid component analysis is improved through multi-stage, progressive judgment to avoid one-time misjudgment. The path status management module is used to determine whether the key indicator conditions are met based on the preliminary judgment results. If not, the path lock state is entered; a process control mechanism is established to ensure that the judgment process is temporarily suspended when the risk is unclear, waiting for additional information to prevent mishandling. Through the cross-batch linkage judgment module, in the path lock state, samples with similar attributes are retrieved from historical batches as supplementary references; historical data is used to enhance the stability of the current judgment and the completeness of the basis. The anomaly identification and re-judgment module determines whether a combination of features in the anomaly pattern library matches. If so, a supplementary judgment process is triggered. This enhances the system's ability to identify hidden risks or complex attribute combinations, improving the intelligence level of abnormal waste liquid identification. The analysis and judgment processing module completes the path unlocking and re-judgment process, updates the analysis results, and generates treatment recommendations. This achieves a closed-loop linkage between intelligent analysis and treatment recommendation output, promoting the automation and rationalization of waste liquid treatment decisions.

[0116] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for intelligent analysis and treatment of waste liquid components for microbial detection, characterized in that: The following steps are involved: Obtain the basic attribute data of the waste liquid to be treated, extract the experimental type and experimental reagent components of the microbial detection experiment, and dynamically set the weight coefficient of the waste liquid attribute index; Setting a staged analysis process, which includes a prejudgment stage, a preliminary screening stage, and a precise judgment stage, and executing the staged analysis process; In the prediction stage, based on the experimental type and experimental reagent components, the main risk components are inferred and the corresponding risk labels are obtained; In the initial screening stage, the basic attribute data of the waste liquid to be treated are compared with the preset risk threshold corresponding to the risk label item by item. If any attribute indicator exceeds the preset risk threshold, the waste liquid is marked as high-risk waste liquid and the subsequent analysis process is terminated; In the fine judgment stage, for waste liquids that are not marked as high-risk, weighted scores are performed on each attribute indicator according to the standard weight coefficient of the waste liquid attribute indicator to obtain the risk score result and output the preliminary analysis and judgment result; Based on the preliminary analysis results, determine whether the set key indicator conditions are met. If not, place the current waste liquid analysis process in a path locking state; Based on the path locking status, a cross-sample batch linkage judgment mechanism is activated to obtain historical waste liquid samples with similar attribute indicators from historical batches as supplementary judgment reference information; Based on the historical waste liquid samples, determine whether to activate the abnormal combination trigger mechanism, match the abnormal combination pattern to trigger the abnormal supplementary determination process, and generate a supplementary determination path; Based on the supplementary determination path, the path unlocking state is entered, the analysis and determination result is updated, and a corresponding waste liquid treatment method recommendation is generated.

2. The method for intelligent analysis and treatment of waste liquid components for microbial detection according to claim 1, characterized in that: The steps of obtaining basic attribute data of the waste liquid to be treated, extracting the experimental type and experimental reagent components of the microbiological detection experiment, and dynamically setting the weight coefficient of the waste liquid attribute index are specifically as follows: Obtaining basic attribute data of the waste liquid to be treated, wherein the basic attribute data includes color information, foam characteristics, odor level, and sedimentation state; Extracting microbial detection experiment context information corresponding to the waste liquid to be processed, wherein the experiment context information includes the experiment type, experimental reagent composition, and batch number; According to the experimental type and experimental reagent components, calling the preset basic attribute weight configuration template; Based on the batch number, if an abnormal record mark appears in the experiment with the same batch number, the basic weight coefficient dynamic adjustment rule is triggered, and the weight of the basic attribute index is adjusted to obtain the standard weight coefficient of the waste liquid attribute index.

3. The method for intelligent analysis and treatment of waste liquid components for microbial detection according to claim 1, characterized in that: The step of judging whether the set key indicator conditions are met based on the preliminary analysis judgment result, and if not, placing the current waste liquid analysis process in a path locking state, specifically includes: Based on the preliminary analysis and determination result, a matching key indicator condition set is loaded, wherein the key indicator condition set includes a value range constraint, a qualitative outlier constraint, and a scoring interval constraint; The numerical range constraint is used to constrain the attribute index to be within a reasonable numerical range, the qualitative outlier constraint is used to determine whether there are predefined unacceptable qualitative description values, and the score interval constraint is used to exclude risk score results in uncertain edge areas; Compare and judge each attribute indicator in the preliminary analysis and judgment result with the key indicator condition set item by item; If any attribute indicator does not meet the corresponding constraint condition, the current waste liquid analysis process will be placed in a path locking state and the subsequent processing process will be terminated.

4. The method for intelligent analysis and treatment of waste liquid components for microbial detection according to claim 1, characterized in that: The step of starting a cross-sample batch linkage judgment mechanism based on the path locking state and obtaining historical waste liquid samples with similar attribute indicators from historical batches as supplementary judgment reference information is specifically as follows: Based on the path locking status, a cross-sample batch linkage judgment mechanism is enabled; According to the preset attribute indicator similarity threshold, several historical waste liquid samples whose attribute indicators are similar to those of the waste liquid to be treated and whose similarity is not less than the preset attribute indicator similarity threshold are selected from historical batches as supplementary judgment reference information under the path locking state.

5. The method for intelligent analysis and treatment of waste liquid components for microbial detection according to claim 1, characterized in that: The steps of determining whether to activate the abnormal combination trigger mechanism based on the historical waste liquid samples, matching the abnormal combination pattern to trigger the abnormal supplementary determination process, and generating the supplementary determination path are specifically as follows: Based on the historical waste liquid samples, extracting parsing path information of the historical waste liquid samples, and statistically analyzing the disposal category distribution of the historical waste liquid samples to obtain historical disposal category results; Calculating the result confidence of the historical treatment category result, and comparing the result confidence with a preset confidence threshold; If the confidence level of the result reaches a preset confidence threshold, the corresponding analytical result of the historical disposal category result is obtained as the analytical determination result of the waste liquid to be treated; If the confidence level of the result does not reach the preset confidence threshold, the abnormal combination trigger mechanism is activated, the abnormal combination pattern is matched to trigger the abnormal supplementary judgment process, and a supplementary judgment path is generated.

6. The method for intelligent analysis and treatment of waste liquid components for microbial detection according to claim 5, characterized in that: If the confidence level of the result does not reach the preset confidence threshold, the abnormal combination trigger mechanism is activated, the abnormal combination pattern is matched to trigger the abnormal supplementary judgment process, and the steps of generating a supplementary judgment path are specifically as follows: If the confidence level of the result does not reach the preset confidence threshold, the abnormal combination trigger mechanism is activated; Constructing an abnormal pattern combination library, wherein the abnormal pattern combination library includes a plurality of attribute combination patterns of high-risk waste liquids, each pattern group consisting of two or more attribute indicators; Matching the attribute indicators of the waste liquid to be treated with each group of abnormal patterns in the abnormal pattern combination library one by one; If the attribute indicators of the waste liquid to be treated meet any set of abnormal patterns in the abnormal pattern combination library, the abnormality supplementary judgment process is triggered; Based on the triggered abnormal pattern, a supplementary determination policy template matching the pattern is called to generate a supplementary determination path.

7. The method for intelligent analysis and treatment of waste liquid components for microbial detection according to claim 1, characterized in that: The steps of entering the path unlocking state based on the supplementary determination path, updating the analysis determination result, and generating the corresponding waste liquid treatment method recommendation are specifically as follows: Based on the supplementary determination path, the current waste liquid analysis process is switched from a path-locked state to a path-unlocked state; For the unlocked state of the path, the waste liquid analysis process is allowed to go back to the refined judgment stage and re-execute the judgment to obtain an updated analysis and judgment result; Based on the updated analysis and determination results, a component-strategy mapping table is matched to generate corresponding waste liquid treatment method recommendations.

8. An intelligent analysis and processing system for waste liquid components used for microbial detection, characterized in that: A method for intelligent analysis and treatment of waste liquid components for microbial detection according to any one of claims 1 to 7 is applied, comprising: The weight dynamic setting module obtains the basic attribute data of the waste liquid to be treated, extracts the experimental type and experimental reagent components of the microbial detection experiment, and dynamically sets the weight coefficient of the waste liquid attribute index; A stage-by-stage analysis module sets a stage-by-stage analysis process, which includes a prejudgment stage, a preliminary screening stage, and a precise judgment stage, and executes the stage-by-stage analysis process; The prediction module, in the prediction stage, infers the main risk components generated according to the experiment type and experimental reagent components, and obtains the corresponding risk labels; The initial screening module compares the basic attribute data of the waste liquid to be treated with the preset risk threshold corresponding to the risk label item by item. If any attribute indicator exceeds the preset risk threshold, the waste liquid is marked as high-risk waste liquid and the subsequent analysis process is terminated; The precise judgment module, for waste liquids not marked as high-risk, performs weighted scoring on each attribute indicator according to the standard weight coefficient of the waste liquid attribute indicator, obtains the risk score result, and outputs the preliminary analysis and judgment result; A path status management module determines whether the set key indicator conditions are met based on the preliminary analysis results. If not, the current waste liquid analysis process is placed in a path lock state; A cross-batch linkage judgment module, based on the path locking state, starts a cross-sample batch linkage judgment mechanism, and obtains historical waste liquid samples with similar attribute indicators from historical batches as supplementary judgment reference information; The abnormality identification and supplementary judgment module determines whether to activate the abnormal combination trigger mechanism based on the historical waste liquid samples, matches the abnormal combination pattern to trigger the abnormal supplementary judgment process, and generates a supplementary judgment path; The analysis and determination processing module enters a path unlocking state based on the supplementary determination path, updates the analysis and determination result, and generates a corresponding waste liquid treatment method recommendation.

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